diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..43e5e34b278da705ae76ed1b4a5f2f1235540b33
--- /dev/null
+++ b/app.py
@@ -0,0 +1,54 @@
+import random
+import gradio as gr
+from colbert.data import Queries
+from colbert.infra import Run, RunConfig, ColBERTConfig
+from colbert import Searcher
+
+
+# def init():
+searcher = None
+with Run().context(RunConfig(nranks=1, experiment="medqa")):
+    config = ColBERTConfig(
+        root="./experiments",
+    )
+    searcher = Searcher(index="medqa_idx", config=config)
+    
+
+        
+def search(query):
+    results = searcher.search(query, k=5)
+    responses=[]
+    # idx = 0 
+    for passage_id, _, _ in zip(*results):
+          responses.append(searcher.collection[passage_id])
+        #   idx = idx+1
+    return responses
+
+
+
+
+
+def chat(question):
+    # history = history or []
+    # message = message.lower()
+
+    # if message.startswith("how many"):
+    #     response = random.randint(1, 10)
+    # elif message.startswith("how"):
+    #     response = random.choice(["Great", "Good", "Okay", "Bad"])
+    # elif message.startswith("where"):
+    #     response = random.choice(["Here", "There", "Somewhere"])
+    # else:
+    #     response = "I don't know"
+    responses = search(question)
+    # history.append((message, response))
+    return responses
+
+chatbot = gr.Chatbot().style(color_map=("green", "pink"))
+demo = gr.Interface(
+    chat,
+    inputs=gr.Textbox(lines=2, placeholder="输入你的问题"),
+    outputs =["text", "text","text","text","text"]
+)
+if __name__ == "__main__":
+    demo.launch(share=True)
diff --git a/baleen/condenser/condense.py b/baleen/condenser/condense.py
new file mode 100644
index 0000000000000000000000000000000000000000..27ac302fb4863424cbfd3a234aeea4e7433fb5ab
--- /dev/null
+++ b/baleen/condenser/condense.py
@@ -0,0 +1,141 @@
+import torch
+
+from colbert.utils.utils import load_checkpoint
+from colbert.utils.amp import MixedPrecisionManager
+from colbert.utils.utils import flatten
+
+from baleen.utils.loaders import *
+from baleen.condenser.model import ElectraReader
+from baleen.condenser.tokenization import AnswerAwareTokenizer
+
+
+
+class Condenser:
+    def __init__(self, collectionX_path, checkpointL1, checkpointL2, deviceL1='cuda', deviceL2='cuda'):
+        self.modelL1, self.maxlenL1 = self._load_model(checkpointL1, deviceL1)
+        self.modelL2, self.maxlenL2 = self._load_model(checkpointL2, deviceL2)
+
+        assert self.maxlenL1 == self.maxlenL2, "Add support for different maxlens: use two tokenizers."
+
+        self.amp, self.tokenizer = self._setup_inference(self.maxlenL2)
+        self.CollectionX, self.CollectionY = self._load_collection(collectionX_path)
+
+    def condense(self, query, backs, ranking):
+        stage1_preds = self._stage1(query, backs, ranking)
+        stage2_preds, stage2_preds_L3x = self._stage2(query, stage1_preds)
+
+        return stage1_preds, stage2_preds, stage2_preds_L3x
+
+    def _load_model(self, path, device):
+        model = torch.load(path, map_location='cpu')
+        ElectraModels = ['google/electra-base-discriminator', 'google/electra-large-discriminator']
+        assert model['arguments']['model'] in ElectraModels, model['arguments']
+
+        model = ElectraReader.from_pretrained(model['arguments']['model'])
+        checkpoint = load_checkpoint(path, model)
+
+        model = model.to(device)
+        model.eval()
+
+        maxlen = checkpoint['arguments']['maxlen']
+
+        return model, maxlen
+    
+    def _setup_inference(self, maxlen):
+        amp = MixedPrecisionManager(activated=True)
+        tokenizer = AnswerAwareTokenizer(total_maxlen=maxlen)
+
+        return amp, tokenizer
+    
+    def _load_collection(self, collectionX_path):
+        CollectionX = {}
+        CollectionY = {}
+
+        with open(collectionX_path) as f:
+            for line_idx, line in enumerate(f):
+                line = ujson.loads(line)
+
+                assert type(line['text']) is list
+                assert line['pid'] == line_idx, (line_idx, line)
+
+                passage = [line['title']] + line['text']
+                CollectionX[line_idx] = passage
+
+                passage = [line['title'] + ' | ' + sentence for sentence in line['text']]
+
+                for idx, sentence in enumerate(passage):
+                    CollectionY[(line_idx, idx)] = sentence
+        
+        return CollectionX, CollectionY
+    
+    def _stage1(self, query, BACKS, ranking, TOPK=9):
+        model = self.modelL1
+
+        with torch.inference_mode():
+            backs = [self.CollectionY[(pid, sid)] for pid, sid in BACKS if (pid, sid) in self.CollectionY]
+            backs = [query] + backs
+            query = ' # '.join(backs)
+
+            # print(query)
+            # print(backs)
+            passages = []
+            actual_ranking = []
+
+            for pid in ranking:
+                actual_ranking.append(pid)
+                psg = self.CollectionX[pid]
+                psg = ' [MASK] '.join(psg)
+
+                passages.append(psg)
+
+            obj = self.tokenizer.process([query], passages, None)
+
+            with self.amp.context():
+                scores = model(obj.encoding.to(model.device)).float()
+
+            pids = [[pid] * scores.size(1) for pid in actual_ranking]
+            pids = flatten(pids)
+
+            sids = [list(range(scores.size(1))) for pid in actual_ranking]
+            sids = flatten(sids)
+
+            scores = scores.view(-1)
+
+            topk = scores.topk(min(TOPK, len(scores))).indices.tolist()
+            topk_pids = [pids[idx] for idx in topk]
+            topk_sids = [sids[idx] for idx in topk]
+
+            preds = [(pid, sid) for pid, sid in zip(topk_pids, topk_sids)]
+
+            pred_plus = BACKS + preds
+            pred_plus = f7(list(map(tuple, pred_plus)))[:TOPK]
+
+        return pred_plus
+    
+    def _stage2(self, query, preds):
+        model = self.modelL2
+
+        psgX = [self.CollectionY[(pid, sid)] for pid, sid in preds if (pid, sid) in self.CollectionY]
+        psg = ' [MASK] '.join([''] + psgX)
+        passages = [psg]
+        # print(passages)
+
+        obj = self.tokenizer.process([query], passages, None)
+
+        with self.amp.context():
+            scores = model(obj.encoding.to(model.device)).float()
+            scores = scores.view(-1).tolist()
+
+            preds = [(score, (pid, sid)) for (pid, sid), score in zip(preds, scores)]
+            preds = sorted(preds, reverse=True)[:5]
+
+            preds_L3x = [x for score, x in preds if score > min(0, preds[1][0] - 1e-10)] # Take at least 2!
+            preds = [x for score, x in preds if score > 0]
+
+            earliest_pids = f7([pid for pid, _ in preds_L3x])[:4]  # Take at most 4 docs.
+            preds_L3x = [(pid, sid) for pid, sid in preds_L3x if pid in earliest_pids]
+
+            assert len(preds_L3x) >= 2
+            assert len(f7([pid for pid, _ in preds_L3x])) <= 4
+
+        return preds, preds_L3x
diff --git a/baleen/condenser/model.py b/baleen/condenser/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..07307db89fff4dea8b9b507f0e2c076485e77293
--- /dev/null
+++ b/baleen/condenser/model.py
@@ -0,0 +1,79 @@
+import torch
+import torch.nn as nn
+
+from transformers import ElectraPreTrainedModel, ElectraModel
+
+
+class ElectraReader(ElectraPreTrainedModel):
+    def __init__(self, config, learn_labels=False):
+        super(ElectraReader, self).__init__(config)
+
+        self.electra = ElectraModel(config)
+
+        self.relevance = nn.Linear(config.hidden_size, 1)
+
+        if learn_labels:
+            self.linear = nn.Linear(config.hidden_size, 2)
+        else:
+            self.linear = nn.Linear(config.hidden_size, 1)
+
+        self.init_weights()
+
+        self.learn_labels = learn_labels
+
+    def forward(self, encoding):
+        outputs = self.electra(encoding.input_ids,
+                               attention_mask=encoding.attention_mask,
+                               token_type_ids=encoding.token_type_ids)[0]
+
+        scores = self.linear(outputs)
+
+        if self.learn_labels:
+            scores = scores[:, 0].squeeze(1)
+        else:
+            scores = scores.squeeze(-1)
+            candidates = (encoding.input_ids == 103)
+            scores = self._mask_2d_index(scores, candidates)
+
+        return scores
+
+    def _mask_2d_index(self, scores, mask):
+        bsize, maxlen = scores.size()
+        bsize_, maxlen_ = mask.size()
+
+        assert bsize == bsize_, (scores.size(), mask.size())
+        assert maxlen == maxlen_, (scores.size(), mask.size())
+
+        # Get flat scores corresponding to the True mask positions, with -inf at the end
+        flat_scores = scores[mask]
+        flat_scores = torch.cat((flat_scores, torch.ones(1, device=self.device) * float('-inf')))
+
+        # Get 2D indexes
+        rowidxs, nnzs = torch.unique(torch.nonzero(mask, as_tuple=False)[:, 0], return_counts=True)
+        max_nnzs = nnzs.max().item()
+
+        rows = [[-1] * max_nnzs for _ in range(bsize)]
+        offset = 0
+        for rowidx, nnz in zip(rowidxs.tolist(), nnzs.tolist()):
+            rows[rowidx] = [offset + i for i in range(nnz)]
+            rows[rowidx] += [-1] * (max_nnzs - len(rows[rowidx]))
+            offset += nnz
+
+        indexes = torch.tensor(rows).to(self.device)
+
+        # Index with the 2D indexes
+        scores_2d = flat_scores[indexes]
+
+        return scores_2d
+
+    def _2d_index(self, embeddings, positions):
+        bsize, maxlen, hdim = embeddings.size()
+        bsize_, max_out = positions.size()
+
+        assert bsize == bsize_
+        assert positions.max() < maxlen
+
+        embeddings = embeddings.view(bsize * maxlen, hdim)
+        positions = positions + torch.arange(bsize, device=positions.device).unsqueeze(-1) * maxlen
+
+        return embeddings[positions]
diff --git a/baleen/condenser/tokenization.py b/baleen/condenser/tokenization.py
new file mode 100644
index 0000000000000000000000000000000000000000..a92e793e70589d41039fe25295a02321359a58ac
--- /dev/null
+++ b/baleen/condenser/tokenization.py
@@ -0,0 +1,118 @@
+import torch
+
+from transformers import ElectraTokenizerFast
+
+class AnswerAwareTokenizer():
+    def __init__(self, total_maxlen, bert_model='google/electra-base-discriminator'):
+        self.total_maxlen = total_maxlen
+
+        self.tok = ElectraTokenizerFast.from_pretrained(bert_model)
+
+    def process(self, questions, passages, all_answers=None, mask=None):
+        return TokenizationObject(self, questions, passages, all_answers, mask)
+
+    def tensorize(self, questions, passages):
+        query_lengths = self.tok(questions, padding='longest', return_tensors='pt').attention_mask.sum(-1)
+
+        encoding = self.tok(questions, passages, padding='longest', truncation='longest_first',
+                            return_tensors='pt', max_length=self.total_maxlen, add_special_tokens=True)
+
+        return encoding, query_lengths
+
+    def get_all_candidates(self, encoding, index):
+        offsets, endpositions = self.all_word_positions(encoding, index)
+
+        candidates = [(offset, endpos)
+                      for idx, offset in enumerate(offsets)
+                      for endpos in endpositions[idx:idx+10]]
+
+        return candidates
+
+    def all_word_positions(self, encoding, index):
+        words = encoding.word_ids(index)
+        offsets = [position
+                   for position, (last_word_number, current_word_number) in enumerate(zip([-1] + words, words))
+                   if last_word_number != current_word_number]
+
+        endpositions = offsets[1:] + [len(words)]
+
+        return offsets, endpositions
+
+    def characters_to_tokens(self, text, answers, encoding, index, offset, endpos):
+        # print(text, answers, encoding, index, offset, endpos)
+        # endpos = endpos - 1
+
+        for offset_ in range(offset, len(text)+1):
+            tokens_offset = encoding.char_to_token(index, offset_)
+            # print(f'tokens_offset = {tokens_offset}')
+            if tokens_offset is not None:
+                break
+
+        for endpos_ in range(endpos, len(text)+1):
+            tokens_endpos = encoding.char_to_token(index, endpos_)
+            # print(f'tokens_endpos = {tokens_endpos}')
+            if tokens_endpos is not None:
+                break
+
+        # None on whitespace!
+        assert tokens_offset is not None, (text, answers, offset)
+        # assert tokens_endpos is not None, (text, answers, endpos)
+        tokens_endpos = tokens_endpos if tokens_endpos is not None else len(encoding.tokens(index))
+
+        return tokens_offset, tokens_endpos
+
+    def tokens_to_answer(self, encoding, index, text, tokens_offset, tokens_endpos):
+        # print(encoding, index, text, tokens_offset, tokens_endpos, len(encoding.tokens(index)))
+
+        char_offset = encoding.word_to_chars(index, encoding.token_to_word(index, tokens_offset)).start
+
+        try:
+            char_next_offset = encoding.word_to_chars(index, encoding.token_to_word(index, tokens_endpos)).start
+            char_endpos = char_next_offset
+        except:
+            char_endpos = encoding.word_to_chars(index, encoding.token_to_word(index, tokens_endpos-1)).end
+
+        assert char_offset is not None
+        assert char_endpos is not None
+
+        return text[char_offset:char_endpos].strip()
+
+
+class TokenizationObject():
+    def __init__(self, tokenizer: AnswerAwareTokenizer, questions, passages, answers=None, mask=None):
+        assert type(questions) is list and type(passages) is list
+        assert len(questions) in [1, len(passages)]
+
+        if mask is None:
+            mask = [True for _ in passages]
+
+        self.mask = mask
+
+        self.tok = tokenizer
+        self.questions = questions if len(questions) == len(passages) else questions * len(passages)
+        self.passages = passages
+        self.answers = answers
+
+        self.encoding, self.query_lengths = self._encode()
+        self.passages_only_encoding, self.candidates, self.candidates_list = self._candidize()
+
+        if answers is not None:
+            self.gold_candidates = self.answers  # self._answerize()
+
+    def _encode(self):
+        return self.tok.tensorize(self.questions, self.passages)
+
+    def _candidize(self):
+        encoding = self.tok.tok(self.passages, add_special_tokens=False)
+
+        all_candidates = [self.tok.get_all_candidates(encoding, index) for index in range(len(self.passages))]
+
+        bsize, maxcands = len(self.passages), max(map(len, all_candidates))
+        all_candidates = [cands + [(-1, -1)] * (maxcands - len(cands)) for cands in all_candidates]
+
+        candidates = torch.tensor(all_candidates)
+        assert candidates.size() == (bsize, maxcands, 2), (candidates.size(), (bsize, maxcands, 2), (self.questions, self.passages))
+
+        candidates = candidates + self.query_lengths.unsqueeze(-1).unsqueeze(-1)
+
+        return encoding, candidates, all_candidates
diff --git a/baleen/engine.py b/baleen/engine.py
new file mode 100644
index 0000000000000000000000000000000000000000..f215258c8e844367fa6c3022aefe202e03d6ed83
--- /dev/null
+++ b/baleen/engine.py
@@ -0,0 +1,58 @@
+from baleen.utils.loaders import *
+from baleen.condenser.condense import Condenser
+
+
+class Baleen:
+    def __init__(self, collectionX_path: str, searcher, condenser: Condenser):
+        self.collectionX = load_collectionX(collectionX_path)
+        self.searcher = searcher
+        self.condenser = condenser
+
+    def search(self, query, num_hops, depth=100, verbose=False):
+        assert depth % num_hops == 0, f"depth={depth} must be divisible by num_hops={num_hops}."
+        k = depth // num_hops
+
+        searcher = self.searcher
+        condenser = self.condenser
+        collectionX = self.collectionX
+
+        facts = []
+        stage1_preds = None
+        context = None
+
+        pids_bag = set()
+
+        for hop_idx in range(0, num_hops):
+            ranking = list(zip(*searcher.search(query, context=context, k=depth)))
+            ranking_ = []
+
+            facts_pids = set([pid for pid, _ in facts])
+
+            for pid, rank, score in ranking:
+                # print(f'[{score}] \t\t {searcher.collection[pid]}')
+                if len(ranking_) < k and pid not in facts_pids:
+                    ranking_.append(pid)
+                
+                if len(pids_bag) < k * (hop_idx+1):
+                    pids_bag.add(pid)
+            
+            stage1_preds, facts, stage2_L3x = condenser.condense(query, backs=facts, ranking=ranking_)
+            context = ' [SEP] '.join([collectionX.get((pid, sid), '') for pid, sid in facts])
+
+        assert len(pids_bag) == depth
+
+        return stage2_L3x, pids_bag, stage1_preds
+
+            
+
+
+            
+                
+
+        
+            
+
+
+
+
+
diff --git a/baleen/hop_searcher.py b/baleen/hop_searcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7f266b3490c786b014014b10e39de4f828af938
--- /dev/null
+++ b/baleen/hop_searcher.py
@@ -0,0 +1,40 @@
+from typing import Union
+
+from colbert import Searcher
+from colbert.data import Queries
+from colbert.infra.config import ColBERTConfig
+
+
+TextQueries = Union[str, 'list[str]', 'dict[int, str]', Queries]
+
+
+class HopSearcher(Searcher):
+    def __init__(self, *args, config=None, interaction='flipr', **kw_args):
+        defaults = ColBERTConfig(query_maxlen=64, interaction=interaction)
+        config = ColBERTConfig.from_existing(defaults, config)
+
+        super().__init__(*args, config=config, **kw_args)
+
+    def encode(self, text: TextQueries, context: TextQueries):
+        queries = text if type(text) is list else [text]
+        context = context if context is None or type(context) is list else [context]
+        bsize = 128 if len(queries) > 128 else None
+
+        self.checkpoint.query_tokenizer.query_maxlen = self.config.query_maxlen
+        Q = self.checkpoint.queryFromText(queries, context=context, bsize=bsize, to_cpu=True)
+
+        return Q
+
+    def search(self, text: str, context: str, k=10):
+        return self.dense_search(self.encode(text, context), k)
+
+    def search_all(self, queries: TextQueries, context: TextQueries, k=10):
+        queries = Queries.cast(queries)
+        context = Queries.cast(context) if context is not None else context
+
+        queries_ = list(queries.values())
+        context_ = list(context.values()) if context is not None else context
+
+        Q = self.encode(queries_, context_)
+
+        return self._search_all_Q(queries, Q, k)
diff --git a/baleen/utils/annotate.py b/baleen/utils/annotate.py
new file mode 100644
index 0000000000000000000000000000000000000000..435f984a714322ed04eac70c906677788544fd1f
--- /dev/null
+++ b/baleen/utils/annotate.py
@@ -0,0 +1,37 @@
+import os
+import ujson
+
+from colbert.utils.utils import print_message, file_tqdm
+
+
+def annotate_to_file(qas_path, ranking_path):
+    output_path = f'{ranking_path}.annotated'
+    assert not os.path.exists(output_path), output_path
+
+    QID2pids = {}
+
+    with open(qas_path) as f:
+        print_message(f"#> Reading QAs from {f.name} ..")
+
+        for line in file_tqdm(f):
+            example = ujson.loads(line)
+            QID2pids[example['qid']] = example['support_pids']
+
+    with open(ranking_path) as f:
+        print_message(f"#> Reading ranked lists from {f.name} ..")
+
+        with open(output_path, 'w') as g:
+            for line in file_tqdm(f):
+                qid, pid, *other = line.strip().split('\t')
+                qid, pid = map(int, [qid, pid])
+
+                label = int(pid in QID2pids[qid])
+
+                line_ = [qid, pid, *other, label]
+                line_ = '\t'.join(map(str, line_)) + '\n'
+                g.write(line_)
+
+    print_message(g.name)
+    print_message("#> Done!")
+
+    return g.name
diff --git a/baleen/utils/loaders.py b/baleen/utils/loaders.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4cb7f17acfc0ee883657199db187b20fbc22cdf
--- /dev/null
+++ b/baleen/utils/loaders.py
@@ -0,0 +1,50 @@
+import os
+import time
+import torch
+import ujson
+
+from colbert.utils.utils import f7, print_message, timestamp
+
+
+def load_contexts(first_hop_topk_path):
+    qid2backgrounds = {}
+
+    with open(first_hop_topk_path) as f:
+        print_message(f"#> Loading backgrounds from {f.name} ..")
+
+        last = None
+        for line in f:
+            qid, facts = ujson.loads(line)
+            facts = [(tuple(f) if type(f) is list else f) for f in facts]
+            qid2backgrounds[qid] = facts
+            last = (qid, facts)
+
+    # assert len(qid2backgrounds) in [0, len(queries)], (len(qid2backgrounds), len(queries))
+    print_message(f"#> {first_hop_topk_path} has {len(qid2backgrounds)} qids. Last = {last}")
+
+    return qid2backgrounds
+
+def load_collectionX(collection_path, dict_in_dict=False):
+    print_message("#> Loading collection...")
+
+    collectionX = {}
+
+    with open(collection_path) as f:
+        for line_idx, line in enumerate(f):
+            line = ujson.loads(line)
+
+            assert type(line['text']) is list
+            assert line['pid'] == line_idx, (line_idx, line)
+
+            passage = [line['title'] + ' | ' + sentence for sentence in line['text']]
+
+            if dict_in_dict:
+                collectionX[line_idx] = {}
+
+            for idx, sentence in enumerate(passage):
+                if dict_in_dict:
+                    collectionX[line_idx][idx] = sentence
+                else:
+                    collectionX[(line_idx, idx)] = sentence
+
+    return collectionX
diff --git a/colbert/__init__.py b/colbert/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..907bec9739e2e70dcfb019c30313f9888d0ddd2e
--- /dev/null
+++ b/colbert/__init__.py
@@ -0,0 +1,5 @@
+from .trainer import Trainer
+from .indexer import Indexer
+from .searcher import Searcher
+
+from .modeling.checkpoint import Checkpoint
\ No newline at end of file
diff --git a/colbert/__pycache__/__init__.cpython-38.pyc b/colbert/__pycache__/__init__.cpython-38.pyc
new file mode 100644
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diff --git a/colbert/__pycache__/trainer.cpython-38.pyc b/colbert/__pycache__/trainer.cpython-38.pyc
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diff --git a/colbert/__pycache__/trainer.cpython-39.pyc b/colbert/__pycache__/trainer.cpython-39.pyc
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diff --git a/colbert/data/__init__.py b/colbert/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a52899680a7dc289f0efad154ff1d7528934a3b4
--- /dev/null
+++ b/colbert/data/__init__.py
@@ -0,0 +1,5 @@
+from .collection import *
+from .queries import *
+
+from .ranking import *
+from .examples import *
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diff --git a/colbert/data/collection.py b/colbert/data/collection.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5efc943095ffac226e194545c2502dac940fd14
--- /dev/null
+++ b/colbert/data/collection.py
@@ -0,0 +1,100 @@
+
+# Could be .tsv or .json. The latter always allows more customization via optional parameters.
+# I think it could be worth doing some kind of parallel reads too, if the file exceeds 1 GiBs.
+# Just need to use a datastructure that shares things across processes without too much pickling.
+# I think multiprocessing.Manager can do that!
+
+import os
+import itertools
+
+from colbert.evaluation.loaders import load_collection
+from colbert.infra.run import Run
+
+
+class Collection:
+    def __init__(self, path=None, data=None):
+        self.path = path
+        self.data = data or self._load_file(path)
+
+    def __iter__(self):
+        # TODO: If __data isn't there, stream from disk!
+        return self.data.__iter__()
+
+    def __getitem__(self, item):
+        # TODO: Load from disk the first time this is called. Unless self.data is already not None.
+        return self.data[item]
+
+    def __len__(self):
+        # TODO: Load here too. Basically, let's make data a property function and, on first call, either load or get __data.
+        return len(self.data)
+
+    def _load_file(self, path):
+        self.path = path
+        return self._load_tsv(path) if path.endswith('.tsv') else self._load_jsonl(path)
+
+    def _load_tsv(self, path):
+        return load_collection(path)
+
+    def _load_jsonl(self, path):
+        raise NotImplementedError()
+
+    def provenance(self):
+        return self.path
+    
+    def toDict(self):
+        return {'provenance': self.provenance()}
+
+    def save(self, new_path):
+        assert new_path.endswith('.tsv'), "TODO: Support .json[l] too."
+        assert not os.path.exists(new_path), new_path
+
+        with Run().open(new_path, 'w') as f:
+            # TODO: expects content to always be a string here; no separate title!
+            for pid, content in enumerate(self.data):
+                content = f'{pid}\t{content}\n'
+                f.write(content)
+            
+            return f.name
+
+    def enumerate(self, rank):
+        for _, offset, passages in self.enumerate_batches(rank=rank):
+            for idx, passage in enumerate(passages):
+                yield (offset + idx, passage)
+
+    def enumerate_batches(self, rank, chunksize=None):
+        assert rank is not None, "TODO: Add support for the rank=None case."
+
+        chunksize = chunksize or self.get_chunksize()
+
+        offset = 0
+        iterator = iter(self)
+
+        for chunk_idx, owner in enumerate(itertools.cycle(range(Run().nranks))):
+            L = [line for _, line in zip(range(chunksize), iterator)]
+
+            if len(L) > 0 and owner == rank:
+                yield (chunk_idx, offset, L)
+
+            offset += len(L)
+
+            if len(L) < chunksize:
+                return
+    
+    def get_chunksize(self):
+        return min(25_000, 1 + len(self) // Run().nranks)  # 25k is great, 10k allows things to reside on GPU??
+
+    @classmethod
+    def cast(cls, obj):
+        if type(obj) is str:
+            return cls(path=obj)
+
+        if type(obj) is list:
+            return cls(data=obj)
+
+        if type(obj) is cls:
+            return obj
+
+        assert False, f"obj has type {type(obj)} which is not compatible with cast()"
+
+
+# TODO: Look up path in some global [per-thread or thread-safe] list.
diff --git a/colbert/data/dataset.py b/colbert/data/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..a48b8af60ab62da9f86be8de60b8e00173a44b8b
--- /dev/null
+++ b/colbert/data/dataset.py
@@ -0,0 +1,14 @@
+
+
+# Not just the corpus, but also an arbitrary number of query sets, indexed by name in a dictionary/dotdict.
+# And also query sets with top-k PIDs.
+# QAs too? TripleSets too?
+
+
+class Dataset:
+    def __init__(self):
+        pass
+
+    def select(self, key):
+        # Select the {corpus, queryset, tripleset, rankingset} determined by uniqueness or by key and return a "unique" dataset (e.g., for key=train)
+        pass
diff --git a/colbert/data/examples.py b/colbert/data/examples.py
new file mode 100644
index 0000000000000000000000000000000000000000..543f6f8647ed9b8ffc5bbc755e2bf400b2f0af53
--- /dev/null
+++ b/colbert/data/examples.py
@@ -0,0 +1,82 @@
+from colbert.infra.run import Run
+import os
+import ujson
+
+from colbert.utils.utils import print_message
+from colbert.infra.provenance import Provenance
+from utility.utils.save_metadata import get_metadata_only
+
+
+class Examples:
+    def __init__(self, path=None, data=None, nway=None, provenance=None):
+        self.__provenance = provenance or path or Provenance()
+        self.nway = nway
+        self.path = path
+        self.data = data or self._load_file(path)
+
+    def provenance(self):
+        return self.__provenance
+    
+    def toDict(self):
+        return self.provenance()
+
+    def _load_file(self, path):
+        nway = self.nway + 1 if self.nway else self.nway
+        examples = []
+
+        with open(path) as f:
+            for line in f:
+                example = ujson.loads(line)[:nway]
+                examples.append(example)
+
+        return examples
+
+    def tolist(self, rank=None, nranks=None):
+        """
+        NOTE: For distributed sampling, this isn't equivalent to perfectly uniform sampling.
+        In particular, each subset is perfectly represented in every batch! However, since we never
+        repeat passes over the data, we never repeat any particular triple, and the split across
+        nodes is random (since the underlying file is pre-shuffled), there's no concern here.
+        """
+
+        if rank or nranks:
+            assert rank in range(nranks), (rank, nranks)
+            return [self.data[idx] for idx in range(0, len(self.data), nranks)]  # if line_idx % nranks == rank
+
+        return list(self.data)
+
+    def save(self, new_path):
+        assert 'json' in new_path.strip('/').split('/')[-1].split('.'), "TODO: Support .json[l] too."
+
+        print_message(f"#> Writing {len(self.data) / 1000_000.0}M examples to {new_path}")
+
+        with Run().open(new_path, 'w') as f:
+            for example in self.data:
+                ujson.dump(example, f)
+                f.write('\n')
+
+            output_path = f.name
+            print_message(f"#> Saved examples with {len(self.data)} lines to {f.name}")
+        
+        with Run().open(f'{new_path}.meta', 'w') as f:
+            d = {}
+            d['metadata'] = get_metadata_only()
+            d['provenance'] = self.provenance()
+            line = ujson.dumps(d, indent=4)
+            f.write(line)
+
+        return output_path
+
+    @classmethod
+    def cast(cls, obj, nway=None):
+        if type(obj) is str:
+            return cls(path=obj, nway=nway)
+
+        if isinstance(obj, list):
+            return cls(data=obj, nway=nway)
+
+        if type(obj) is cls:
+            assert nway is None, nway
+            return obj
+
+        assert False, f"obj has type {type(obj)} which is not compatible with cast()"
diff --git a/colbert/data/queries.py b/colbert/data/queries.py
new file mode 100644
index 0000000000000000000000000000000000000000..460a9f9ee31f86666049ee7c09def56e68b0b7f3
--- /dev/null
+++ b/colbert/data/queries.py
@@ -0,0 +1,163 @@
+from colbert.infra.run import Run
+import os
+import ujson
+
+from colbert.evaluation.loaders import load_queries
+
+# TODO: Look up path in some global [per-thread or thread-safe] list.
+# TODO: path could be a list of paths...? But then how can we tell it's not a list of queries..
+
+
+class Queries:
+    def __init__(self, path=None, data=None):
+        self.path = path
+
+        if data:
+            assert isinstance(data, dict), type(data)
+        self._load_data(data) or self._load_file(path)
+    
+    def __len__(self):
+        return len(self.data)
+
+    def __iter__(self):
+        return iter(self.data.items())
+
+    def provenance(self):
+        return self.path
+    
+    def toDict(self):
+        return {'provenance': self.provenance()}
+
+    def _load_data(self, data):
+        if data is None:
+            return None
+
+        self.data = {}
+        self._qas = {}
+
+        for qid, content in data.items():
+            if isinstance(content, dict):
+                self.data[qid] = content['question']
+                self._qas[qid] = content
+            else:
+                self.data[qid] = content
+
+        if len(self._qas) == 0:
+            del self._qas
+
+        return True
+
+    def _load_file(self, path):
+        if not path.endswith('.json'):
+            self.data = load_queries(path)
+            return True
+        
+        # Load QAs
+        self.data = {}
+        self._qas = {}
+
+        with open(path) as f:
+            for line in f:
+                qa = ujson.loads(line)
+
+                assert qa['qid'] not in self.data
+                self.data[qa['qid']] = qa['question']
+                self._qas[qa['qid']] = qa
+
+        return self.data
+
+    def qas(self):
+        return dict(self._qas)
+
+    def __getitem__(self, key):
+        return self.data[key]
+
+    def keys(self):
+        return self.data.keys()
+
+    def values(self):
+        return self.data.values()
+
+    def items(self):
+        return self.data.items()
+
+    def save(self, new_path):
+        assert new_path.endswith('.tsv')
+        assert not os.path.exists(new_path), new_path
+
+        with Run().open(new_path, 'w') as f:
+            for qid, content in self.data.items():
+                content = f'{qid}\t{content}\n'
+                f.write(content)
+            
+            return f.name
+
+    def save_qas(self, new_path):
+        assert new_path.endswith('.json')
+        assert not os.path.exists(new_path), new_path
+
+        with open(new_path, 'w') as f:
+            for qid, qa in self._qas.items():
+                qa['qid'] = qid
+                f.write(ujson.dumps(qa) + '\n')
+
+    def _load_tsv(self, path):
+        raise NotImplementedError
+
+    def _load_jsonl(self, path):
+        raise NotImplementedError
+
+    @classmethod
+    def cast(cls, obj):
+        if type(obj) is str:
+            return cls(path=obj)
+
+        if isinstance(obj, dict) or isinstance(obj, list):
+            return cls(data=obj)
+
+        if type(obj) is cls:
+            return obj
+
+        assert False, f"obj has type {type(obj)} which is not compatible with cast()"
+
+
+# class QuerySet:
+#     def __init__(self, *paths, renumber=False):
+#         self.paths = paths
+#         self.original_queries = [load_queries(path) for path in paths]
+
+#         if renumber:
+#             self.queries = flatten([q.values() for q in self.original_queries])
+#             self.queries = {idx: text for idx, text in enumerate(self.queries)}
+
+#         else:
+#             self.queries = {}
+
+#             for queries in self.original_queries:
+#                 assert len(set.intersection(set(queries.keys()), set(self.queries.keys()))) == 0, \
+#                     "renumber=False requires non-overlapping query IDs"
+
+#                 self.queries.update(queries)
+
+#         assert len(self.queries) == sum(map(len, self.original_queries))
+
+#     def todict(self):
+#         return dict(self.queries)
+
+#     def tolist(self):
+#         return list(self.queries.values())
+
+#     def query_sets(self):
+#         return self.original_queries
+
+#     def split_rankings(self, rankings):
+#         assert type(rankings) is list
+#         assert len(rankings) == len(self.queries)
+
+#         sub_rankings = []
+#         offset = 0
+#         for source in self.original_queries:
+#             sub_rankings.append(rankings[offset:offset+len(source)])
+#             offset += len(source)
+
+#         return sub_rankings
diff --git a/colbert/data/ranking.py b/colbert/data/ranking.py
new file mode 100644
index 0000000000000000000000000000000000000000..c334a52f6cb165bd22b5170dda1f2c6a80baa791
--- /dev/null
+++ b/colbert/data/ranking.py
@@ -0,0 +1,94 @@
+import os
+import tqdm
+import ujson
+from colbert.infra.provenance import Provenance
+
+from colbert.infra.run import Run
+from colbert.utils.utils import print_message, groupby_first_item
+from utility.utils.save_metadata import get_metadata_only
+
+
+def numericize(v):
+    if '.' in v:
+        return float(v)
+
+    return int(v)
+
+
+def load_ranking(path):  # works with annotated and un-annotated ranked lists
+    print_message("#> Loading the ranked lists from", path)
+
+    with open(path) as f:
+        return [list(map(numericize, line.strip().split('\t'))) for line in f]
+
+
+class Ranking:
+    def __init__(self, path=None, data=None, metrics=None, provenance=None):
+        self.__provenance = provenance or path or Provenance()
+        self.data = self._prepare_data(data or self._load_file(path))
+
+    def provenance(self):
+        return self.__provenance
+    
+    def toDict(self):
+        return {'provenance': self.provenance()}
+
+    def _prepare_data(self, data):
+        # TODO: Handle list of lists???
+        if isinstance(data, dict):
+            self.flat_ranking = [(qid, *rest) for qid, subranking in data.items() for rest in subranking]
+            return data
+
+        self.flat_ranking = data
+        return groupby_first_item(tqdm.tqdm(self.flat_ranking))
+
+    def _load_file(self, path):
+        return load_ranking(path)
+
+    def todict(self):
+        return dict(self.data)
+
+    def tolist(self):
+        return list(self.flat_ranking)
+
+    def items(self):
+        return self.data.items()
+
+    def _load_tsv(self, path):
+        raise NotImplementedError
+
+    def _load_jsonl(self, path):
+        raise NotImplementedError
+
+    def save(self, new_path):
+        assert 'tsv' in new_path.strip('/').split('/')[-1].split('.'), "TODO: Support .json[l] too."
+
+        with Run().open(new_path, 'w') as f:
+            for items in self.flat_ranking:
+                line = '\t'.join(map(lambda x: str(int(x) if type(x) is bool else x), items)) + '\n'
+                f.write(line)
+
+            output_path = f.name
+            print_message(f"#> Saved ranking of {len(self.data)} queries and {len(self.flat_ranking)} lines to {f.name}")
+        
+        with Run().open(f'{new_path}.meta', 'w') as f:
+            d = {}
+            d['metadata'] = get_metadata_only()
+            d['provenance'] = self.provenance()
+            line = ujson.dumps(d, indent=4)
+            f.write(line)
+        
+        return output_path
+
+    @classmethod
+    def cast(cls, obj):
+        if type(obj) is str:
+            return cls(path=obj)
+
+        if isinstance(obj, dict) or isinstance(obj, list):
+            return cls(data=obj)
+
+        if type(obj) is cls:
+            return obj
+
+        assert False, f"obj has type {type(obj)} which is not compatible with cast()"
diff --git a/colbert/distillation/ranking_scorer.py b/colbert/distillation/ranking_scorer.py
new file mode 100644
index 0000000000000000000000000000000000000000..90dee1ea868499ae032f24279e7eea9e4cf89098
--- /dev/null
+++ b/colbert/distillation/ranking_scorer.py
@@ -0,0 +1,52 @@
+import tqdm
+import ujson
+
+from collections import defaultdict
+
+from colbert.utils.utils import print_message, zipstar
+from utility.utils.save_metadata import get_metadata_only
+
+from colbert.infra import Run
+from colbert.data import Ranking
+from colbert.infra.provenance import Provenance
+from colbert.distillation.scorer import Scorer
+
+
+class RankingScorer:
+    def __init__(self, scorer: Scorer, ranking: Ranking):
+        self.scorer = scorer
+        self.ranking = ranking.tolist()
+        self.__provenance = Provenance()
+
+        print_message(f"#> Loaded ranking with {len(self.ranking)} qid--pid pairs!")
+    
+    def provenance(self):
+        return self.__provenance
+
+    def run(self):
+        print_message(f"#> Starting..")
+
+        qids, pids, *_ = zipstar(self.ranking)
+        distillation_scores = self.scorer.launch(qids, pids)
+
+        scores_by_qid = defaultdict(list)
+
+        for qid, pid, score in tqdm.tqdm(zip(qids, pids, distillation_scores)):
+            scores_by_qid[qid].append((score, pid))
+
+        with Run().open('distillation_scores.json', 'w') as f:
+            for qid in tqdm.tqdm(scores_by_qid):
+                obj = (qid, scores_by_qid[qid])
+                f.write(ujson.dumps(obj) + '\n')
+
+            output_path = f.name
+            print_message(f'#> Saved the distillation_scores to {output_path}')
+
+        with Run().open(f'{output_path}.meta', 'w') as f:
+            d = {}
+            d['metadata'] = get_metadata_only()
+            d['provenance'] = self.provenance()
+            line = ujson.dumps(d, indent=4)
+            f.write(line)
+
+        return output_path
diff --git a/colbert/distillation/scorer.py b/colbert/distillation/scorer.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e7975cad453b33c6b0283aef38bad6fd92d7dc6
--- /dev/null
+++ b/colbert/distillation/scorer.py
@@ -0,0 +1,68 @@
+import torch
+import tqdm
+
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+from colbert.infra.launcher import Launcher
+from colbert.infra import Run, RunConfig
+from colbert.modeling.reranker.electra import ElectraReranker
+from colbert.utils.utils import flatten
+
+
+DEFAULT_MODEL = 'cross-encoder/ms-marco-MiniLM-L-6-v2'
+
+
+class Scorer:
+    def __init__(self, queries, collection, model=DEFAULT_MODEL, maxlen=180, bsize=256):
+        self.queries = queries
+        self.collection = collection
+        self.model = model
+
+        self.maxlen = maxlen
+        self.bsize = bsize
+
+    def launch(self, qids, pids):
+        launcher = Launcher(self._score_pairs_process, return_all=True)
+        outputs = launcher.launch(Run().config, qids, pids)
+
+        return flatten(outputs)
+
+    def _score_pairs_process(self, config, qids, pids):
+        assert len(qids) == len(pids), (len(qids), len(pids))
+        share = 1 + len(qids) // config.nranks
+        offset = config.rank * share
+        endpos = (1 + config.rank) * share
+
+        return self._score_pairs(qids[offset:endpos], pids[offset:endpos], show_progress=(config.rank < 1))
+
+    def _score_pairs(self, qids, pids, show_progress=False):
+        tokenizer = AutoTokenizer.from_pretrained(self.model)
+        model = AutoModelForSequenceClassification.from_pretrained(self.model).cuda()
+
+        assert len(qids) == len(pids), (len(qids), len(pids))
+
+        scores = []
+
+        model.eval()
+        with torch.inference_mode():
+            with torch.cuda.amp.autocast():
+                for offset in tqdm.tqdm(range(0, len(qids), self.bsize), disable=(not show_progress)):
+                    endpos = offset + self.bsize
+
+                    queries_ = [self.queries[qid] for qid in qids[offset:endpos]]
+                    passages_ = [self.collection[pid] for pid in pids[offset:endpos]]
+
+                    features = tokenizer(queries_, passages_, padding='longest', truncation=True,
+                                            return_tensors='pt', max_length=self.maxlen).to(model.device)
+
+                    scores.append(model(**features).logits.flatten())
+
+        scores = torch.cat(scores)
+        scores = scores.tolist()
+
+        Run().print(f'Returning with {len(scores)} scores')
+
+        return scores
+
+
+# LONG-TERM TODO: This can be sped up by sorting by length in advance.
diff --git a/colbert/evaluation/__init__.py b/colbert/evaluation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
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diff --git a/colbert/evaluation/__pycache__/loaders.cpython-39.pyc b/colbert/evaluation/__pycache__/loaders.cpython-39.pyc
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diff --git a/colbert/evaluation/load_model.py b/colbert/evaluation/load_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..13cb5c82dfa3309d814a80fc4082eada44d0eedd
--- /dev/null
+++ b/colbert/evaluation/load_model.py
@@ -0,0 +1,28 @@
+import os
+import ujson
+import torch
+import random
+
+from collections import defaultdict, OrderedDict
+
+from colbert.parameters import DEVICE
+from colbert.modeling.colbert import ColBERT
+from colbert.utils.utils import print_message, load_checkpoint
+
+
+def load_model(args, do_print=True):
+    colbert = ColBERT.from_pretrained('bert-base-uncased',
+                                      query_maxlen=args.query_maxlen,
+                                      doc_maxlen=args.doc_maxlen,
+                                      dim=args.dim,
+                                      similarity_metric=args.similarity,
+                                      mask_punctuation=args.mask_punctuation)
+    colbert = colbert.to(DEVICE)
+
+    print_message("#> Loading model checkpoint.", condition=do_print)
+
+    checkpoint = load_checkpoint(args.checkpoint, colbert, do_print=do_print)
+
+    colbert.eval()
+
+    return colbert, checkpoint
diff --git a/colbert/evaluation/loaders.py b/colbert/evaluation/loaders.py
new file mode 100644
index 0000000000000000000000000000000000000000..9bc2296535e1ac9bcc09c24951d0c39083e99b17
--- /dev/null
+++ b/colbert/evaluation/loaders.py
@@ -0,0 +1,198 @@
+import os
+import ujson
+import torch
+import random
+
+from collections import defaultdict, OrderedDict
+
+from colbert.parameters import DEVICE
+from colbert.modeling.colbert import ColBERT
+from colbert.utils.utils import print_message, load_checkpoint
+from colbert.evaluation.load_model import load_model
+from colbert.utils.runs import Run
+
+
+def load_queries(queries_path):
+    queries = OrderedDict()
+
+    print_message("#> Loading the queries from", queries_path, "...")
+
+    with open(queries_path) as f:
+        for line in f:
+            qid, query, *_ = line.strip().split('\t')
+            qid = int(qid)
+
+            assert (qid not in queries), ("Query QID", qid, "is repeated!")
+            queries[qid] = query
+
+    print_message("#> Got", len(queries), "queries. All QIDs are unique.\n")
+
+    return queries
+
+
+def load_qrels(qrels_path):
+    if qrels_path is None:
+        return None
+
+    print_message("#> Loading qrels from", qrels_path, "...")
+
+    qrels = OrderedDict()
+    with open(qrels_path, mode='r', encoding="utf-8") as f:
+        for line in f:
+            qid, x, pid, y = map(int, line.strip().split('\t'))
+            assert x == 0 and y == 1
+            qrels[qid] = qrels.get(qid, [])
+            qrels[qid].append(pid)
+
+    # assert all(len(qrels[qid]) == len(set(qrels[qid])) for qid in qrels)
+    for qid in qrels:
+        qrels[qid] = list(set(qrels[qid]))
+
+    avg_positive = round(sum(len(qrels[qid]) for qid in qrels) / len(qrels), 2)
+
+    print_message("#> Loaded qrels for", len(qrels), "unique queries with",
+                  avg_positive, "positives per query on average.\n")
+
+    return qrels
+
+
+def load_topK(topK_path):
+    queries = OrderedDict()
+    topK_docs = OrderedDict()
+    topK_pids = OrderedDict()
+
+    print_message("#> Loading the top-k per query from", topK_path, "...")
+
+    with open(topK_path) as f:
+        for line_idx, line in enumerate(f):
+            if line_idx and line_idx % (10*1000*1000) == 0:
+                print(line_idx, end=' ', flush=True)
+
+            qid, pid, query, passage = line.split('\t')
+            qid, pid = int(qid), int(pid)
+
+            assert (qid not in queries) or (queries[qid] == query)
+            queries[qid] = query
+            topK_docs[qid] = topK_docs.get(qid, [])
+            topK_docs[qid].append(passage)
+            topK_pids[qid] = topK_pids.get(qid, [])
+            topK_pids[qid].append(pid)
+
+        print()
+
+    assert all(len(topK_pids[qid]) == len(set(topK_pids[qid])) for qid in topK_pids)
+
+    Ks = [len(topK_pids[qid]) for qid in topK_pids]
+
+    print_message("#> max(Ks) =", max(Ks), ", avg(Ks) =", round(sum(Ks) / len(Ks), 2))
+    print_message("#> Loaded the top-k per query for", len(queries), "unique queries.\n")
+
+    return queries, topK_docs, topK_pids
+
+
+def load_topK_pids(topK_path, qrels):
+    topK_pids = defaultdict(list)
+    topK_positives = defaultdict(list)
+
+    print_message("#> Loading the top-k PIDs per query from", topK_path, "...")
+
+    with open(topK_path) as f:
+        for line_idx, line in enumerate(f):
+            if line_idx and line_idx % (10*1000*1000) == 0:
+                print(line_idx, end=' ', flush=True)
+
+            qid, pid, *rest = line.strip().split('\t')
+            qid, pid = int(qid), int(pid)
+
+            topK_pids[qid].append(pid)
+
+            assert len(rest) in [1, 2, 3]
+
+            if len(rest) > 1:
+                *_, label = rest
+                label = int(label)
+                assert label in [0, 1]
+
+                if label >= 1:
+                    topK_positives[qid].append(pid)
+
+        print()
+
+    assert all(len(topK_pids[qid]) == len(set(topK_pids[qid])) for qid in topK_pids)
+    assert all(len(topK_positives[qid]) == len(set(topK_positives[qid])) for qid in topK_positives)
+
+    # Make them sets for fast lookups later
+    topK_positives = {qid: set(topK_positives[qid]) for qid in topK_positives}
+
+    Ks = [len(topK_pids[qid]) for qid in topK_pids]
+
+    print_message("#> max(Ks) =", max(Ks), ", avg(Ks) =", round(sum(Ks) / len(Ks), 2))
+    print_message("#> Loaded the top-k per query for", len(topK_pids), "unique queries.\n")
+
+    if len(topK_positives) == 0:
+        topK_positives = None
+    else:
+        assert len(topK_pids) >= len(topK_positives)
+
+        for qid in set.difference(set(topK_pids.keys()), set(topK_positives.keys())):
+            topK_positives[qid] = []
+
+        assert len(topK_pids) == len(topK_positives)
+
+        avg_positive = round(sum(len(topK_positives[qid]) for qid in topK_positives) / len(topK_pids), 2)
+
+        print_message("#> Concurrently got annotations for", len(topK_positives), "unique queries with",
+                      avg_positive, "positives per query on average.\n")
+
+    assert qrels is None or topK_positives is None, "Cannot have both qrels and an annotated top-K file!"
+
+    if topK_positives is None:
+        topK_positives = qrels
+
+    return topK_pids, topK_positives
+
+
+def load_collection(collection_path):
+    print_message("#> Loading collection...")
+
+    collection = []
+
+    with open(collection_path) as f:
+        for line_idx, line in enumerate(f):
+            if line_idx % (1000*1000) == 0:
+                print(f'{line_idx // 1000 // 1000}M', end=' ', flush=True)
+
+            pid, passage, *rest = line.strip('\n\r ').split('\t')
+            assert pid == 'id' or int(pid) == line_idx
+
+            if len(rest) >= 1:
+                title = rest[0]
+                passage = title + ' | ' + passage
+
+            collection.append(passage)
+
+    print()
+
+    return collection
+
+
+def load_colbert(args, do_print=True):
+    colbert, checkpoint = load_model(args, do_print)
+
+    # TODO: If the parameters below were not specified on the command line, their *checkpoint* values should be used.
+    # I.e., not their purely (i.e., training) default values.
+
+    for k in ['query_maxlen', 'doc_maxlen', 'dim', 'similarity', 'amp']:
+        if 'arguments' in checkpoint and hasattr(args, k):
+            if k in checkpoint['arguments'] and checkpoint['arguments'][k] != getattr(args, k):
+                a, b = checkpoint['arguments'][k], getattr(args, k)
+                Run.warn(f"Got checkpoint['arguments']['{k}'] != args.{k} (i.e., {a} != {b})")
+
+    if 'arguments' in checkpoint:
+        if args.rank < 1:
+            print(ujson.dumps(checkpoint['arguments'], indent=4))
+
+    if do_print:
+        print('\n')
+
+    return colbert, checkpoint
diff --git a/colbert/evaluation/metrics.py b/colbert/evaluation/metrics.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9d1f543c5a34cda004cb3afc5420b2ebb139cdc
--- /dev/null
+++ b/colbert/evaluation/metrics.py
@@ -0,0 +1,114 @@
+import ujson
+
+from collections import defaultdict
+from colbert.utils.runs import Run
+
+
+class Metrics:
+    def __init__(self, mrr_depths: set, recall_depths: set, success_depths: set, total_queries=None):
+        self.results = {}
+        self.mrr_sums = {depth: 0.0 for depth in mrr_depths}
+        self.recall_sums = {depth: 0.0 for depth in recall_depths}
+        self.success_sums = {depth: 0.0 for depth in success_depths}
+        self.total_queries = total_queries
+
+        self.max_query_idx = -1
+        self.num_queries_added = 0
+
+    def add(self, query_idx, query_key, ranking, gold_positives):
+        self.num_queries_added += 1
+
+        assert query_key not in self.results
+        assert len(self.results) <= query_idx
+        assert len(set(gold_positives)) == len(gold_positives)
+        assert len(set([pid for _, pid, _ in ranking])) == len(ranking)
+
+        self.results[query_key] = ranking
+
+        positives = [i for i, (_, pid, _) in enumerate(ranking) if pid in gold_positives]
+
+        if len(positives) == 0:
+            return
+
+        for depth in self.mrr_sums:
+            first_positive = positives[0]
+            self.mrr_sums[depth] += (1.0 / (first_positive+1.0)) if first_positive < depth else 0.0
+
+        for depth in self.success_sums:
+            first_positive = positives[0]
+            self.success_sums[depth] += 1.0 if first_positive < depth else 0.0
+
+        for depth in self.recall_sums:
+            num_positives_up_to_depth = len([pos for pos in positives if pos < depth])
+            self.recall_sums[depth] += num_positives_up_to_depth / len(gold_positives)
+
+    def print_metrics(self, query_idx):
+        for depth in sorted(self.mrr_sums):
+            print("MRR@" + str(depth), "=", self.mrr_sums[depth] / (query_idx+1.0))
+
+        for depth in sorted(self.success_sums):
+            print("Success@" + str(depth), "=", self.success_sums[depth] / (query_idx+1.0))
+
+        for depth in sorted(self.recall_sums):
+            print("Recall@" + str(depth), "=", self.recall_sums[depth] / (query_idx+1.0))
+
+    def log(self, query_idx):
+        assert query_idx >= self.max_query_idx
+        self.max_query_idx = query_idx
+
+        Run.log_metric("ranking/max_query_idx", query_idx, query_idx)
+        Run.log_metric("ranking/num_queries_added", self.num_queries_added, query_idx)
+
+        for depth in sorted(self.mrr_sums):
+            score = self.mrr_sums[depth] / (query_idx+1.0)
+            Run.log_metric("ranking/MRR." + str(depth), score, query_idx)
+
+        for depth in sorted(self.success_sums):
+            score = self.success_sums[depth] / (query_idx+1.0)
+            Run.log_metric("ranking/Success." + str(depth), score, query_idx)
+
+        for depth in sorted(self.recall_sums):
+            score = self.recall_sums[depth] / (query_idx+1.0)
+            Run.log_metric("ranking/Recall." + str(depth), score, query_idx)
+
+    def output_final_metrics(self, path, query_idx, num_queries):
+        assert query_idx + 1 == num_queries
+        assert num_queries == self.total_queries
+
+        if self.max_query_idx < query_idx:
+            self.log(query_idx)
+
+        self.print_metrics(query_idx)
+
+        output = defaultdict(dict)
+
+        for depth in sorted(self.mrr_sums):
+            score = self.mrr_sums[depth] / (query_idx+1.0)
+            output['mrr'][depth] = score
+
+        for depth in sorted(self.success_sums):
+            score = self.success_sums[depth] / (query_idx+1.0)
+            output['success'][depth] = score
+
+        for depth in sorted(self.recall_sums):
+            score = self.recall_sums[depth] / (query_idx+1.0)
+            output['recall'][depth] = score
+
+        with open(path, 'w') as f:
+            ujson.dump(output, f, indent=4)
+            f.write('\n')
+
+
+def evaluate_recall(qrels, queries, topK_pids):
+    if qrels is None:
+        return
+
+    assert set(qrels.keys()) == set(queries.keys())
+    recall_at_k = [len(set.intersection(set(qrels[qid]), set(topK_pids[qid]))) / max(1.0, len(qrels[qid]))
+                   for qid in qrels]
+    recall_at_k = sum(recall_at_k) / len(qrels)
+    recall_at_k = round(recall_at_k, 3)
+    print("Recall @ maximum depth =", recall_at_k)
+
+
+# TODO: If implicit qrels are used (for re-ranking), warn if a recall metric is requested + add an asterisk to output.
diff --git a/colbert/index.py b/colbert/index.py
new file mode 100644
index 0000000000000000000000000000000000000000..60d01a67c16e46d8e36718ebe90a723b7d541e1a
--- /dev/null
+++ b/colbert/index.py
@@ -0,0 +1,17 @@
+
+
+# TODO: This is the loaded index, underneath a searcher.
+
+
+"""
+## Operations:
+
+index = Index(index='/path/to/index')
+index.load_to_memory()
+
+batch_of_pids = [2324,32432,98743,23432]
+index.lookup(batch_of_pids, device='cuda:0') -> (N, doc_maxlen, dim)
+
+index.iterate_over_parts()
+
+"""
diff --git a/colbert/indexer.py b/colbert/indexer.py
new file mode 100644
index 0000000000000000000000000000000000000000..021191407d1e7c60ae3c7397c1c329a9a8fc8ade
--- /dev/null
+++ b/colbert/indexer.py
@@ -0,0 +1,84 @@
+import os
+import time
+
+import torch.multiprocessing as mp
+
+from colbert.infra.run import Run
+from colbert.infra.config import ColBERTConfig, RunConfig
+from colbert.infra.launcher import Launcher
+
+from colbert.utils.utils import create_directory, print_message
+
+from colbert.indexing.collection_indexer import encode
+
+
+class Indexer:
+    def __init__(self, checkpoint, config=None):
+        """
+           Use Run().context() to choose the run's configuration. They are NOT extracted from `config`.
+        """
+
+        self.index_path = None
+        self.checkpoint = checkpoint
+        self.checkpoint_config = ColBERTConfig.load_from_checkpoint(checkpoint)
+
+        self.config = ColBERTConfig.from_existing(self.checkpoint_config, config, Run().config)
+        self.configure(checkpoint=checkpoint)
+
+    def configure(self, **kw_args):
+        self.config.configure(**kw_args)
+
+    def get_index(self):
+        return self.index_path
+
+    def erase(self):
+        assert self.index_path is not None
+        directory = self.index_path
+        deleted = []
+
+        for filename in sorted(os.listdir(directory)):
+            filename = os.path.join(directory, filename)
+
+            delete = filename.endswith(".json")
+            delete = delete and ('metadata' in filename or 'doclen' in filename or 'plan' in filename)
+            delete = delete or filename.endswith(".pt")
+            
+            if delete:
+                deleted.append(filename)
+        
+        if len(deleted):
+            print_message(f"#> Will delete {len(deleted)} files already at {directory} in 20 seconds...")
+            time.sleep(20)
+
+            for filename in deleted:
+                os.remove(filename)
+
+        return deleted
+
+    def index(self, name, collection, overwrite=False):
+        assert overwrite in [True, False, 'reuse', 'resume']
+
+        self.configure(collection=collection, index_name=name, resume=overwrite=='resume')
+        self.configure(bsize=64, partitions=None)
+
+        self.index_path = self.config.index_path_
+        index_does_not_exist = (not os.path.exists(self.config.index_path_))
+
+        assert (overwrite in [True, 'reuse', 'resume']) or index_does_not_exist, self.config.index_path_
+        create_directory(self.config.index_path_)
+
+        if overwrite is True:
+            self.erase()
+
+        if index_does_not_exist or overwrite != 'reuse':
+            self.__launch(collection)
+
+        return self.index_path
+
+    def __launch(self, collection):
+        manager = mp.Manager()
+        shared_lists = [manager.list() for _ in range(self.config.nranks)]
+        shared_queues = [manager.Queue(maxsize=1) for _ in range(self.config.nranks)]
+
+        launcher = Launcher(encode)
+        launcher.launch(self.config, collection, shared_lists, shared_queues)
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diff --git a/colbert/indexing/codecs/__pycache__/residual_embeddings_strided.cpython-39.pyc b/colbert/indexing/codecs/__pycache__/residual_embeddings_strided.cpython-39.pyc
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diff --git a/colbert/indexing/codecs/decompress_residuals.cpp b/colbert/indexing/codecs/decompress_residuals.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..1c675a6c95edf5189cfe4b9b36f5e9b1907940fd
--- /dev/null
+++ b/colbert/indexing/codecs/decompress_residuals.cpp
@@ -0,0 +1,23 @@
+#include <torch/extension.h>
+
+torch::Tensor decompress_residuals_cuda(
+    const torch::Tensor binary_residuals, const torch::Tensor bucket_weights,
+    const torch::Tensor reversed_bit_map,
+    const torch::Tensor bucket_weight_combinations, const torch::Tensor codes,
+    const torch::Tensor centroids, const int dim, const int nbits);
+
+torch::Tensor decompress_residuals(
+    const torch::Tensor binary_residuals, const torch::Tensor bucket_weights,
+    const torch::Tensor reversed_bit_map,
+    const torch::Tensor bucket_weight_combinations, const torch::Tensor codes,
+    const torch::Tensor centroids, const int dim, const int nbits) {
+    // Add input verification
+    return decompress_residuals_cuda(
+        binary_residuals, bucket_weights, reversed_bit_map,
+        bucket_weight_combinations, codes, centroids, dim, nbits);
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("decompress_residuals_cpp", &decompress_residuals,
+          "Decompress residuals");
+}
diff --git a/colbert/indexing/codecs/decompress_residuals.cu b/colbert/indexing/codecs/decompress_residuals.cu
new file mode 100644
index 0000000000000000000000000000000000000000..5fc134b53feac9557b9a37ec4e2cd54cecbc5606
--- /dev/null
+++ b/colbert/indexing/codecs/decompress_residuals.cu
@@ -0,0 +1,75 @@
+#include <assert.h>
+#include <cuda.h>
+#include <cuda_fp16.h>
+#include <cuda_runtime.h>
+#include <stdint.h>
+#include <torch/extension.h>
+
+__global__ void decompress_residuals_kernel(
+    const uint8_t* binary_residuals,
+    const torch::PackedTensorAccessor32<at::Half, 1, torch::RestrictPtrTraits>
+        bucket_weights,
+    const torch::PackedTensorAccessor32<uint8_t, 1, torch::RestrictPtrTraits>
+        reversed_bit_map,
+    const torch::PackedTensorAccessor32<uint8_t, 2, torch::RestrictPtrTraits>
+        bucket_weight_combinations,
+    const torch::PackedTensorAccessor32<int, 1, torch::RestrictPtrTraits> codes,
+    const torch::PackedTensorAccessor32<at::Half, 2, torch::RestrictPtrTraits>
+        centroids,
+    const int n, const int dim, const int nbits, const int packed_size,
+    at::Half* output) {
+    const int packed_dim = (int)(dim * nbits / packed_size);
+    const int i = blockIdx.x;
+    const int j = threadIdx.x;
+
+    if (i >= n) return;
+    if (j >= dim * nbits / packed_size) return;
+
+    const int code = codes[i];
+
+    uint8_t x = binary_residuals[i * packed_dim + j];
+    x = reversed_bit_map[x];
+    int output_idx = (int)(j * packed_size / nbits);
+    for (int k = 0; k < packed_size / nbits; k++) {
+        assert(output_idx < dim);
+        const int bucket_weight_idx = bucket_weight_combinations[x][k];
+        output[i * dim + output_idx] = bucket_weights[bucket_weight_idx];
+        output[i * dim + output_idx] += centroids[code][output_idx];
+        output_idx++;
+    }
+}
+
+torch::Tensor decompress_residuals_cuda(
+    const torch::Tensor binary_residuals, const torch::Tensor bucket_weights,
+    const torch::Tensor reversed_bit_map,
+    const torch::Tensor bucket_weight_combinations, const torch::Tensor codes,
+    const torch::Tensor centroids, const int dim, const int nbits) {
+    auto options = torch::TensorOptions()
+                       .dtype(torch::kFloat16)
+                       .device(torch::kCUDA, 0)
+                       .requires_grad(false);
+    torch::Tensor output =
+        torch::zeros({(int)binary_residuals.size(0), (int)dim}, options);
+
+    // TODO: Set this automatically?
+    const int packed_size = 8;
+
+    const int threads = dim / (packed_size / nbits);
+    const int blocks =
+        (binary_residuals.size(0) * binary_residuals.size(1)) / threads;
+
+    decompress_residuals_kernel<<<blocks, threads>>>(
+        binary_residuals.data<uint8_t>(),
+        bucket_weights
+            .packed_accessor32<at::Half, 1, torch::RestrictPtrTraits>(),
+        reversed_bit_map
+            .packed_accessor32<uint8_t, 1, torch::RestrictPtrTraits>(),
+        bucket_weight_combinations
+            .packed_accessor32<uint8_t, 2, torch::RestrictPtrTraits>(),
+        codes.packed_accessor32<int, 1, torch::RestrictPtrTraits>(),
+        centroids.packed_accessor32<at::Half, 2, torch::RestrictPtrTraits>(),
+        binary_residuals.size(0), dim, nbits, packed_size,
+        output.data<at::Half>());
+
+    return output;
+}
diff --git a/colbert/indexing/codecs/packbits.cpp b/colbert/indexing/codecs/packbits.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..be7d25cbecddfaaf16e820ccab1271e1ebfd28d1
--- /dev/null
+++ b/colbert/indexing/codecs/packbits.cpp
@@ -0,0 +1,12 @@
+#include <torch/extension.h>
+
+torch::Tensor packbits_cuda(const torch::Tensor residuals);
+
+torch::Tensor packbits(const torch::Tensor residuals) {
+    return packbits_cuda(residuals);
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("packbits_cpp", &packbits, "Pack bits");
+}
+
diff --git a/colbert/indexing/codecs/packbits.cu b/colbert/indexing/codecs/packbits.cu
new file mode 100644
index 0000000000000000000000000000000000000000..e52564339e80cd246a417d870878c08a69e8462e
--- /dev/null
+++ b/colbert/indexing/codecs/packbits.cu
@@ -0,0 +1,57 @@
+#include <assert.h>
+#include <cuda.h>
+#include <cuda_fp16.h>
+#include <cuda_runtime.h>
+#include <stdint.h>
+#include <torch/extension.h>
+
+#define FULL_MASK 0xffffffff
+
+__global__ void packbits_kernel(
+        const uint8_t* residuals,
+        uint8_t* packed_residuals,
+        const int residuals_size) {
+    const int i = blockIdx.x;
+    const int j = threadIdx.x;
+
+    assert(blockDim.x == 32);
+
+    const int residuals_idx = i * blockDim.x + j;
+    if (residuals_idx >= residuals_size) {
+        return;
+    }
+
+    const int packed_residuals_idx = residuals_idx / 8;
+
+
+    uint32_t mask = __ballot_sync(FULL_MASK, residuals[residuals_idx]);
+
+    mask = __brev(mask);
+
+    if (residuals_idx % 32 == 0) {
+        for (int k = 0; k < 4; k++) {
+            packed_residuals[packed_residuals_idx + k] =
+                (mask >> (8 * (4 - k - 1))) & 0xff;
+        }
+    }
+}
+
+torch::Tensor packbits_cuda(const torch::Tensor residuals) {
+    auto options = torch::TensorOptions()
+                        .dtype(torch::kUInt8)
+                        .device(torch::kCUDA, residuals.device().index())
+                        .requires_grad(false);
+    assert(residuals.size(0) % 32 == 0);
+    torch::Tensor packed_residuals = torch::zeros({int(residuals.size(0) / 8)}, options);
+
+    const int threads = 32;
+    const int blocks = std::ceil(residuals.size(0) / (float) threads);
+
+    packbits_kernel<<<blocks, threads>>>(
+        residuals.data<uint8_t>(),
+        packed_residuals.data<uint8_t>(),
+        residuals.size(0)
+    );
+
+    return packed_residuals;
+}
diff --git a/colbert/indexing/codecs/residual.py b/colbert/indexing/codecs/residual.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ecbb0f6986d79a78413cea6a7baec44e126cddb
--- /dev/null
+++ b/colbert/indexing/codecs/residual.py
@@ -0,0 +1,276 @@
+"""
+EVENTUALLY: Tune the batch sizes selected here for a good balance of speed and generality.
+"""
+
+import os
+import torch
+import numpy as np
+from itertools import product
+
+from colbert.infra.config import ColBERTConfig
+from colbert.indexing.codecs.residual_embeddings import ResidualEmbeddings
+from colbert.utils.utils import print_message
+
+import pathlib
+from torch.utils.cpp_extension import load
+
+
+class ResidualCodec:
+    Embeddings = ResidualEmbeddings
+
+    def __init__(self, config, centroids, avg_residual=None, bucket_cutoffs=None, bucket_weights=None):
+        self.use_gpu = config.total_visible_gpus > 0
+
+        ResidualCodec.try_load_torch_extensions(self.use_gpu)
+
+        if self.use_gpu > 0:
+            self.centroids = centroids.cuda().half()
+        else:
+            self.centroids = centroids.float()
+        self.dim, self.nbits = config.dim, config.nbits
+        self.avg_residual = avg_residual
+
+        if torch.is_tensor(self.avg_residual):
+            if self.use_gpu:
+                self.avg_residual = self.avg_residual.cuda().half()
+
+        if torch.is_tensor(bucket_cutoffs):
+            if self.use_gpu:
+                bucket_cutoffs = bucket_cutoffs.cuda()
+                bucket_weights = bucket_weights.half().cuda()
+
+        self.bucket_cutoffs = bucket_cutoffs
+        self.bucket_weights = bucket_weights
+        if not self.use_gpu and self.bucket_weights is not None:
+            self.bucket_weights = self.bucket_weights.to(torch.float32)
+
+        self.arange_bits = torch.arange(0, self.nbits, device='cuda' if self.use_gpu else 'cpu', dtype=torch.uint8)
+
+        self.rank = config.rank
+
+        # We reverse the residual bits because arange_bits as
+        # currently constructed produces results with the reverse
+        # of the expected endianness
+        self.reversed_bit_map = []
+        mask = (1 << self.nbits) - 1
+        for i in range(256):
+            # The reversed byte
+            z = 0
+            for j in range(8, 0, -self.nbits):
+                # Extract a subsequence of length n bits
+                x = (i >> (j - self.nbits)) & mask
+
+                # Reverse the endianness of each bit subsequence (e.g. 10 -> 01)
+                y = 0
+                for k in range(self.nbits - 1, -1, -1):
+                    y += ((x >> (self.nbits - k - 1)) & 1) * (2 ** k)
+
+                # Set the corresponding bits in the output byte
+                z |= y
+                if j > self.nbits:
+                    z <<= self.nbits
+            self.reversed_bit_map.append(z)
+        self.reversed_bit_map = torch.tensor(self.reversed_bit_map).to(torch.uint8)
+
+        # A table of all possible lookup orders into bucket_weights
+        # given n bits per lookup
+        keys_per_byte = 8 // self.nbits
+        if self.bucket_weights is not None:
+            self.decompression_lookup_table = (
+                torch.tensor(
+                    list(
+                        product(
+                            list(range(len(self.bucket_weights))),
+                            repeat=keys_per_byte
+                        )
+                    )
+                )
+                .to(torch.uint8)
+            )
+        else:
+            self.decompression_lookup_table = None
+        if self.use_gpu:
+            self.reversed_bit_map = self.reversed_bit_map.cuda()
+            if self.decompression_lookup_table is not None:
+                self.decompression_lookup_table = self.decompression_lookup_table.cuda()
+
+    @classmethod
+    def try_load_torch_extensions(cls, use_gpu):
+        if hasattr(cls, "loaded_extensions") or not use_gpu:
+            return
+
+        print_message(f"Loading decompress_residuals_cpp extension (set COLBERT_LOAD_TORCH_EXTENSION_VERBOSE=True for more info)...")
+        decompress_residuals_cpp = load(
+            name="decompress_residuals_cpp",
+            sources=[
+                os.path.join(
+                    pathlib.Path(__file__).parent.resolve(), "decompress_residuals.cpp"
+                ),
+                os.path.join(
+                    pathlib.Path(__file__).parent.resolve(), "decompress_residuals.cu"
+                ),
+            ],
+            verbose=os.getenv("COLBERT_LOAD_TORCH_EXTENSION_VERBOSE", "False") == "True",
+        )
+        cls.decompress_residuals = decompress_residuals_cpp.decompress_residuals_cpp
+
+        print_message(f"Loading packbits_cpp extension (set COLBERT_LOAD_TORCH_EXTENSION_VERBOSE=True for more info)...")
+        packbits_cpp = load(
+            name="packbits_cpp",
+            sources=[
+                os.path.join(
+                    pathlib.Path(__file__).parent.resolve(), "packbits.cpp"
+                ),
+                os.path.join(
+                    pathlib.Path(__file__).parent.resolve(), "packbits.cu"
+                ),
+            ],
+            verbose=os.getenv("COLBERT_LOAD_TORCH_EXTENSION_VERBOSE", "False") == "True",
+        )
+        cls.packbits = packbits_cpp.packbits_cpp
+
+        cls.loaded_extensions = True
+
+    @classmethod
+    def load(cls, index_path):
+        config = ColBERTConfig.load_from_index(index_path)
+        centroids_path = os.path.join(index_path, 'centroids.pt')
+        avgresidual_path = os.path.join(index_path, 'avg_residual.pt')
+        buckets_path = os.path.join(index_path, 'buckets.pt')
+
+        centroids = torch.load(centroids_path, map_location='cpu')
+        avg_residual = torch.load(avgresidual_path, map_location='cpu')
+        bucket_cutoffs, bucket_weights = torch.load(buckets_path, map_location='cpu')
+
+        if avg_residual.dim() == 0:
+            avg_residual = avg_residual.item()
+
+        return cls(config=config, centroids=centroids, avg_residual=avg_residual, bucket_cutoffs=bucket_cutoffs, bucket_weights=bucket_weights)
+
+    def save(self, index_path):
+        assert self.avg_residual is not None
+        assert torch.is_tensor(self.bucket_cutoffs), self.bucket_cutoffs
+        assert torch.is_tensor(self.bucket_weights), self.bucket_weights
+
+        centroids_path = os.path.join(index_path, 'centroids.pt')
+        avgresidual_path = os.path.join(index_path, 'avg_residual.pt')
+        buckets_path = os.path.join(index_path, 'buckets.pt')
+
+        torch.save(self.centroids.half(), centroids_path)
+        torch.save((self.bucket_cutoffs, self.bucket_weights), buckets_path)
+
+        if torch.is_tensor(self.avg_residual):
+            torch.save(self.avg_residual, avgresidual_path)
+        else:
+            torch.save(torch.tensor([self.avg_residual]), avgresidual_path)
+
+    def compress(self, embs):
+        codes, residuals = [], []
+
+        for batch in embs.split(1 << 18):
+            if self.use_gpu:
+                batch = batch.cuda().half()
+            codes_ = self.compress_into_codes(batch, out_device=batch.device)
+            centroids_ = self.lookup_centroids(codes_, out_device=batch.device)
+
+            residuals_ = (batch - centroids_)
+
+            codes.append(codes_.cpu())
+            residuals.append(self.binarize(residuals_).cpu())
+
+        codes = torch.cat(codes)
+        residuals = torch.cat(residuals)
+
+        return ResidualCodec.Embeddings(codes, residuals)
+
+    def binarize(self, residuals):
+        residuals = torch.bucketize(residuals.float(), self.bucket_cutoffs).to(dtype=torch.uint8)
+        residuals = residuals.unsqueeze(-1).expand(*residuals.size(), self.nbits)  # add a new nbits-wide dim
+        residuals = residuals >> self.arange_bits  # divide by 2^bit for each bit position
+        residuals = residuals & 1  # apply mod 2 to binarize
+
+        assert self.dim % 8 == 0
+        assert self.dim % (self.nbits * 8) == 0, (self.dim, self.nbits)
+
+        if self.use_gpu:
+            residuals_packed = ResidualCodec.packbits(residuals.contiguous().flatten())
+        else:
+            residuals_packed = np.packbits(np.asarray(residuals.contiguous().flatten()))
+        residuals_packed = torch.as_tensor(residuals_packed, dtype=torch.uint8)
+        residuals_packed = residuals_packed.reshape(residuals.size(0), self.dim // 8 * self.nbits)
+
+        return residuals_packed
+
+    def compress_into_codes(self, embs, out_device):
+        """
+            EVENTUALLY: Fusing the kernels or otherwise avoiding materalizing the entire matrix before max(dim=0)
+                        seems like it would help here a lot.
+        """
+
+        codes = []
+
+        bsize = (1 << 29) // self.centroids.size(0)
+        for batch in embs.split(bsize):
+            if self.use_gpu:
+                indices = (self.centroids @ batch.T.cuda().half()).max(dim=0).indices.to(device=out_device)
+            else:
+                indices = (self.centroids @ batch.T.cpu().float()).max(dim=0).indices.to(device=out_device)
+            codes.append(indices)
+
+        return torch.cat(codes)
+
+    def lookup_centroids(self, codes, out_device):
+        """
+            Handles multi-dimensional codes too.
+
+            EVENTUALLY: The .split() below should happen on a flat view.
+        """
+
+        centroids = []
+
+        for batch in codes.split(1 << 20):
+            if self.use_gpu:
+                centroids.append(self.centroids[batch.cuda().long()].to(device=out_device))
+            else:
+                centroids.append(self.centroids[batch.long()].to(device=out_device))
+
+        return torch.cat(centroids)
+
+    #@profile
+    def decompress(self, compressed_embs: Embeddings):
+        """
+            We batch below even if the target device is CUDA to avoid large temporary buffers causing OOM.
+        """
+
+        codes, residuals = compressed_embs.codes, compressed_embs.residuals
+
+        D = []
+        for codes_, residuals_ in zip(codes.split(1 << 15), residuals.split(1 << 15)):
+            if self.use_gpu:
+                codes_, residuals_ = codes_.cuda(), residuals_.cuda()
+                centroids_ = ResidualCodec.decompress_residuals(
+                    residuals_,
+                    self.bucket_weights,
+                    self.reversed_bit_map,
+                    self.decompression_lookup_table,
+                    codes_,
+                    self.centroids,
+                    self.dim,
+                    self.nbits,
+                ).cuda()
+            else:
+                # TODO: Remove dead code
+                centroids_ = self.lookup_centroids(codes_, out_device='cpu')
+                residuals_ = self.reversed_bit_map[residuals_.long()]
+                residuals_ = self.decompression_lookup_table[residuals_.long()]
+                residuals_ = residuals_.reshape(residuals_.shape[0], -1)
+                residuals_ = self.bucket_weights[residuals_.long()]
+                centroids_.add_(residuals_)
+
+            if self.use_gpu:
+                D_ = torch.nn.functional.normalize(centroids_, p=2, dim=-1).half()
+            else:
+                D_ = torch.nn.functional.normalize(centroids_.to(torch.float32), p=2, dim=-1)
+            D.append(D_)
+
+        return torch.cat(D)
diff --git a/colbert/indexing/codecs/residual_embeddings.py b/colbert/indexing/codecs/residual_embeddings.py
new file mode 100644
index 0000000000000000000000000000000000000000..39c56d031fd435dc1aa4b4f3eb32b0f9933abfb1
--- /dev/null
+++ b/colbert/indexing/codecs/residual_embeddings.py
@@ -0,0 +1,95 @@
+import os
+import torch
+import ujson
+import tqdm
+
+from colbert.indexing.codecs.residual_embeddings_strided import ResidualEmbeddingsStrided
+from colbert.utils.utils import print_message
+
+
+class ResidualEmbeddings:
+    Strided = ResidualEmbeddingsStrided
+
+    def __init__(self, codes, residuals):
+        """
+            Supply the already compressed residuals.
+        """
+
+        # assert isinstance(residuals, bitarray), type(residuals)
+        assert codes.size(0) == residuals.size(0), (codes.size(), residuals.size())
+        assert codes.dim() == 1 and residuals.dim() == 2, (codes.size(), residuals.size())
+        assert residuals.dtype == torch.uint8
+
+        self.codes = codes.to(torch.int32)  # (num_embeddings,) int32
+        self.residuals = residuals   # (num_embeddings, compressed_dim) uint8
+
+    @classmethod
+    def load_chunks(cls, index_path, chunk_idxs, num_embeddings):
+        num_embeddings += 512  # pad for access with strides
+
+        dim, nbits = get_dim_and_nbits(index_path)
+
+        codes = torch.empty(num_embeddings, dtype=torch.int32)
+        residuals = torch.empty(num_embeddings, dim // 8 * nbits, dtype=torch.uint8)
+
+        codes_offset = 0
+
+        print_message("#> Loading codes and residuals...")
+
+        for chunk_idx in tqdm.tqdm(chunk_idxs):
+            chunk = cls.load(index_path, chunk_idx)
+
+            codes_endpos = codes_offset + chunk.codes.size(0)
+
+            # Copy the values over to the allocated space
+            codes[codes_offset:codes_endpos] = chunk.codes
+            residuals[codes_offset:codes_endpos] = chunk.residuals
+
+            codes_offset = codes_endpos
+
+        # codes, residuals = codes.cuda(), residuals.cuda()  # FIXME: REMOVE THIS LINE!
+
+        return cls(codes, residuals)
+
+    @classmethod
+    def load(cls, index_path, chunk_idx):
+        codes = cls.load_codes(index_path, chunk_idx)
+        residuals = cls.load_residuals(index_path, chunk_idx)
+
+        return cls(codes, residuals)
+
+    @classmethod
+    def load_codes(self, index_path, chunk_idx):
+        codes_path = os.path.join(index_path, f'{chunk_idx}.codes.pt')
+        return torch.load(codes_path, map_location='cpu')
+
+    @classmethod
+    def load_residuals(self, index_path, chunk_idx):
+        residuals_path = os.path.join(index_path, f'{chunk_idx}.residuals.pt')  # f'{chunk_idx}.residuals.bn'
+        # return _load_bitarray(residuals_path)
+
+        return torch.load(residuals_path, map_location='cpu')
+
+    def save(self, path_prefix):
+        codes_path = f'{path_prefix}.codes.pt'
+        residuals_path = f'{path_prefix}.residuals.pt'  # f'{path_prefix}.residuals.bn'
+
+        torch.save(self.codes, codes_path)
+        torch.save(self.residuals, residuals_path)
+        # _save_bitarray(self.residuals, residuals_path)
+
+    def __len__(self):
+        return self.codes.size(0)
+
+
+def get_dim_and_nbits(index_path):
+    # TODO: Ideally load this using ColBERTConfig.load_from_index!
+    with open(os.path.join(index_path, 'metadata.json')) as f:
+        metadata = ujson.load(f)['config']
+
+    dim = metadata['dim']
+    nbits = metadata['nbits']
+
+    assert (dim * nbits) % 8 == 0, (dim, nbits, dim * nbits)
+
+    return dim, nbits
diff --git a/colbert/indexing/codecs/residual_embeddings_strided.py b/colbert/indexing/codecs/residual_embeddings_strided.py
new file mode 100644
index 0000000000000000000000000000000000000000..d23b286d810c8e18e7df71b6b037813fd092d74d
--- /dev/null
+++ b/colbert/indexing/codecs/residual_embeddings_strided.py
@@ -0,0 +1,41 @@
+# from colbert.indexing.codecs.residual import ResidualCodec
+import colbert.indexing.codecs.residual_embeddings as residual_embeddings
+
+from colbert.search.strided_tensor import StridedTensor
+
+"""
+import line_profiler
+import atexit
+profile = line_profiler.LineProfiler()
+atexit.register(profile.print_stats)
+"""
+
+class ResidualEmbeddingsStrided:
+    def __init__(self, codec, embeddings, doclens):
+        self.codec = codec
+        self.codes = embeddings.codes
+        self.residuals = embeddings.residuals
+        self.use_gpu = self.codec.use_gpu
+
+        self.codes_strided = StridedTensor(self.codes, doclens, use_gpu=self.use_gpu)
+        self.residuals_strided = StridedTensor(self.residuals, doclens, use_gpu=self.use_gpu)
+
+    def lookup_eids(self, embedding_ids, codes=None, out_device='cuda'):
+        codes = self.codes[embedding_ids] if codes is None else codes
+        residuals = self.residuals[embedding_ids]
+
+        return self.codec.decompress(residual_embeddings.ResidualEmbeddings(codes, residuals))
+
+    # @profile
+    def lookup_pids(self, passage_ids, out_device='cuda'):
+        codes_packed, codes_lengths = self.codes_strided.lookup(passage_ids)#.as_packed_tensor()
+        residuals_packed, _ = self.residuals_strided.lookup(passage_ids)#.as_packed_tensor()
+
+        embeddings_packed = self.codec.decompress(residual_embeddings.ResidualEmbeddings(codes_packed, residuals_packed))
+
+        return embeddings_packed, codes_lengths
+        # return StridedTensor(embeddings_packed, codes_lengths).as_padded_tensor()
+        # return StridedTensor.pad_packed(embeddings_packed, codes_lengths)
+
+    def lookup_codes(self, passage_ids):
+        return self.codes_strided.lookup(passage_ids)#.as_packed_tensor()
diff --git a/colbert/indexing/collection_encoder.py b/colbert/indexing/collection_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..b4a63082a1bd34dc44efb8bf3451a3e7da722238
--- /dev/null
+++ b/colbert/indexing/collection_encoder.py
@@ -0,0 +1,45 @@
+import torch
+
+from colbert.infra.run import Run
+from colbert.utils.utils import print_message, batch
+
+
+class CollectionEncoder():
+    def __init__(self, config, checkpoint):
+        self.config = config
+        self.checkpoint = checkpoint
+        self.use_gpu = self.config.total_visible_gpus > 0
+
+    def encode_passages(self, passages):
+        Run().print(f"#> Encoding {len(passages)} passages..")
+
+        if len(passages) == 0:
+            return None, None
+
+        with torch.inference_mode():
+            embs, doclens = [], []
+
+            # Batch here to avoid OOM from storing intermediate embeddings on GPU.
+            # Storing on the GPU helps with speed of masking, etc.
+            # But ideally this batching happens internally inside docFromText.
+            for passages_batch in batch(passages, self.config.bsize * 50):
+                embs_, doclens_ = self.checkpoint.docFromText(passages_batch, bsize=self.config.bsize,
+                                                              keep_dims='flatten', showprogress=(not self.use_gpu))
+                embs.append(embs_)
+                doclens.extend(doclens_)
+
+            embs = torch.cat(embs)
+
+            # embs, doclens = self.checkpoint.docFromText(passages, bsize=self.config.bsize,
+            #                                                   keep_dims='flatten', showprogress=(self.config.rank < 1))
+
+        # with torch.inference_mode():
+        #     embs = self.checkpoint.docFromText(passages, bsize=self.config.bsize,
+        #                                        keep_dims=False, showprogress=(self.config.rank < 1))
+        #     assert type(embs) is list
+        #     assert len(embs) == len(passages)
+
+        #     doclens = [d.size(0) for d in embs]
+        #     embs = torch.cat(embs)
+
+        return embs, doclens
diff --git a/colbert/indexing/collection_indexer.py b/colbert/indexing/collection_indexer.py
new file mode 100644
index 0000000000000000000000000000000000000000..50102f6042fd499f2819375190d747d67e3b7258
--- /dev/null
+++ b/colbert/indexing/collection_indexer.py
@@ -0,0 +1,472 @@
+import os
+import tqdm
+import time
+import ujson
+import torch
+import random
+try:
+    import faiss
+except ImportError as e:
+    print("WARNING: faiss must be imported for indexing")
+
+import numpy as np
+import torch.multiprocessing as mp
+from colbert.infra.config.config import ColBERTConfig
+
+import colbert.utils.distributed as distributed
+
+from colbert.infra.run import Run
+from colbert.infra.launcher import print_memory_stats
+from colbert.modeling.checkpoint import Checkpoint
+from colbert.data.collection import Collection
+
+from colbert.indexing.collection_encoder import CollectionEncoder
+from colbert.indexing.index_saver import IndexSaver
+from colbert.indexing.utils import optimize_ivf
+from colbert.utils.utils import flatten, print_message
+
+from colbert.indexing.codecs.residual import ResidualCodec
+
+
+def encode(config, collection, shared_lists, shared_queues):
+    encoder = CollectionIndexer(config=config, collection=collection)
+    encoder.run(shared_lists)
+
+
+class CollectionIndexer():
+    def __init__(self, config: ColBERTConfig, collection):
+        self.config = config
+        self.rank, self.nranks = self.config.rank, self.config.nranks
+
+        self.use_gpu = self.config.total_visible_gpus > 0
+
+        if self.config.rank == 0:
+            self.config.help()
+
+        self.collection = Collection.cast(collection)
+        self.checkpoint = Checkpoint(self.config.checkpoint, colbert_config=self.config)
+        if self.use_gpu:
+            self.checkpoint = self.checkpoint.cuda()
+
+        self.encoder = CollectionEncoder(config, self.checkpoint)
+        self.saver = IndexSaver(config)
+
+        print_memory_stats(f'RANK:{self.rank}')
+
+    def run(self, shared_lists):
+        with torch.inference_mode():
+            self.setup()
+            distributed.barrier(self.rank)
+            print_memory_stats(f'RANK:{self.rank}')
+
+            if not self.config.resume or not self.saver.try_load_codec():
+                self.train(shared_lists)
+            distributed.barrier(self.rank)
+            print_memory_stats(f'RANK:{self.rank}')
+
+            self.index()
+            distributed.barrier(self.rank)
+            print_memory_stats(f'RANK:{self.rank}')
+
+            self.finalize()
+            distributed.barrier(self.rank)
+            print_memory_stats(f'RANK:{self.rank}')
+
+    def setup(self):
+        if self.config.resume:
+            if self._try_load_plan():
+                Run().print_main(f"#> Loaded plan from {self.plan_path}:")
+                Run().print_main(f"#> num_chunks = {self.num_chunks}")
+                Run().print_main(f"#> num_partitions = {self.num_chunks}")
+                Run().print_main(f"#> num_embeddings_est = {self.num_embeddings_est}")
+                Run().print_main(f"#> avg_doclen_est = {self.avg_doclen_est}")
+                return
+
+        self.num_chunks = int(np.ceil(len(self.collection) / self.collection.get_chunksize()))
+
+        sampled_pids = self._sample_pids()
+        avg_doclen_est = self._sample_embeddings(sampled_pids)
+
+        # Select the number of partitions
+        num_passages = len(self.collection)
+        self.num_embeddings_est = num_passages * avg_doclen_est
+        self.num_partitions = int(2 ** np.floor(np.log2(16 * np.sqrt(self.num_embeddings_est))))
+
+        Run().print_main(f'Creaing {self.num_partitions:,} partitions.')
+        Run().print_main(f'*Estimated* {int(self.num_embeddings_est):,} embeddings.')
+
+        self._save_plan()
+
+    def _sample_pids(self):
+        num_passages = len(self.collection)
+
+        # Simple alternative: < 100k: 100%, < 1M: 15%, < 10M: 7%, < 100M: 3%, > 100M: 1%
+        # Keep in mind that, say, 15% still means at least 100k.
+        # So the formula is max(100% * min(total, 100k), 15% * min(total, 1M), ...)
+        # Then we subsample the vectors to 100 * num_partitions
+
+        typical_doclen = 120  # let's keep sampling independent of the actual doc_maxlen
+        sampled_pids = 16 * np.sqrt(typical_doclen * num_passages)
+        # sampled_pids = int(2 ** np.floor(np.log2(1 + sampled_pids)))
+        sampled_pids = min(1 + int(sampled_pids), num_passages)
+
+        sampled_pids = random.sample(range(num_passages), sampled_pids)
+        Run().print_main(f"# of sampled PIDs = {len(sampled_pids)} \t sampled_pids[:3] = {sampled_pids[:3]}")
+
+        return set(sampled_pids)
+
+    def _sample_embeddings(self, sampled_pids):
+        local_pids = self.collection.enumerate(rank=self.rank)
+        local_sample = [passage for pid, passage in local_pids if pid in sampled_pids]
+
+        local_sample_embs, doclens = self.encoder.encode_passages(local_sample)
+
+        if torch.cuda.is_available():
+            self.num_sample_embs = torch.tensor([local_sample_embs.size(0)]).cuda()
+            torch.distributed.all_reduce(self.num_sample_embs)
+
+            avg_doclen_est = sum(doclens) / len(doclens) if doclens else 0
+            avg_doclen_est = torch.tensor([avg_doclen_est]).cuda()
+            torch.distributed.all_reduce(avg_doclen_est)
+
+            nonzero_ranks = torch.tensor([float(len(local_sample) > 0)]).cuda()
+            torch.distributed.all_reduce(nonzero_ranks)
+        else:
+            if torch.distributed.is_initialized():
+                self.num_sample_embs = torch.tensor([local_sample_embs.size(0)]).cpu()
+                torch.distributed.all_reduce(self.num_sample_embs)
+
+                avg_doclen_est = sum(doclens) / len(doclens) if doclens else 0
+                avg_doclen_est = torch.tensor([avg_doclen_est]).cpu()
+                torch.distributed.all_reduce(avg_doclen_est)
+
+                nonzero_ranks = torch.tensor([float(len(local_sample) > 0)]).cpu()
+                torch.distributed.all_reduce(nonzero_ranks)
+            else:
+                self.num_sample_embs = torch.tensor([local_sample_embs.size(0)]).cpu()
+
+                avg_doclen_est = sum(doclens) / len(doclens) if doclens else 0
+                avg_doclen_est = torch.tensor([avg_doclen_est]).cpu()
+
+                nonzero_ranks = torch.tensor([float(len(local_sample) > 0)]).cpu()
+
+        avg_doclen_est = avg_doclen_est.item() / nonzero_ranks.item()
+        self.avg_doclen_est = avg_doclen_est
+
+        Run().print(f'avg_doclen_est = {avg_doclen_est} \t len(local_sample) = {len(local_sample):,}')
+
+        torch.save(local_sample_embs.half(), os.path.join(self.config.index_path_, f'sample.{self.rank}.pt'))
+
+        return avg_doclen_est
+
+    def _try_load_plan(self):
+        config = self.config
+        self.plan_path = os.path.join(config.index_path_, 'plan.json')
+        if os.path.exists(self.plan_path):
+            with open(self.plan_path, 'r') as f:
+                try:
+                    plan = ujson.load(f)
+                except Exception as e:
+                    return False
+                if not ('num_chunks' in plan and
+                        'num_partitions' in plan and
+                        'num_embeddings_est' in plan and
+                        'avg_doclen_est' in plan):
+                    return False
+
+                # TODO: Verify config matches
+                self.num_chunks = plan['num_chunks']
+                self.num_partitions = plan['num_partitions']
+                self.num_embeddings_est = plan['num_embeddings_est']
+                self.avg_doclen_est = plan['avg_doclen_est']
+
+            return True
+        else:
+            return False
+
+    def _save_plan(self):
+        if self.rank < 1:
+            config = self.config
+            self.plan_path = os.path.join(config.index_path_, 'plan.json')
+            Run().print("#> Saving the indexing plan to", self.plan_path, "..")
+
+            with open(self.plan_path, 'w') as f:
+                d = {'config': config.export()}
+                d['num_chunks'] = self.num_chunks
+                d['num_partitions'] = self.num_partitions
+                d['num_embeddings_est'] = self.num_embeddings_est
+                d['avg_doclen_est'] = self.avg_doclen_est
+
+                f.write(ujson.dumps(d, indent=4) + '\n')
+
+
+    def train(self, shared_lists):
+        if self.rank > 0:
+            return
+
+        sample, heldout = self._concatenate_and_split_sample()
+
+        centroids = self._train_kmeans(sample, shared_lists)
+
+        print_memory_stats(f'RANK:{self.rank}')
+        del sample
+
+        bucket_cutoffs, bucket_weights, avg_residual = self._compute_avg_residual(centroids, heldout)
+
+        print_message(f'avg_residual = {avg_residual}')
+
+        codec = ResidualCodec(config=self.config, centroids=centroids, avg_residual=avg_residual,
+                              bucket_cutoffs=bucket_cutoffs, bucket_weights=bucket_weights)
+        self.saver.save_codec(codec)
+
+    def _concatenate_and_split_sample(self):
+        print_memory_stats(f'***1*** \t RANK:{self.rank}')
+
+        # TODO: Allocate a float16 array. Load the samples from disk, copy to array.
+        sample = torch.empty(self.num_sample_embs, self.config.dim, dtype=torch.float16)
+
+        offset = 0
+        for r in range(self.nranks):
+            sub_sample_path = os.path.join(self.config.index_path_, f'sample.{r}.pt')
+            sub_sample = torch.load(sub_sample_path)
+            os.remove(sub_sample_path)
+
+            endpos = offset + sub_sample.size(0)
+            sample[offset:endpos] = sub_sample
+            offset = endpos
+
+        assert endpos == sample.size(0), (endpos, sample.size())
+
+        print_memory_stats(f'***2*** \t RANK:{self.rank}')
+
+        # Shuffle and split out a 5% "heldout" sub-sample [up to 50k elements]
+        sample = sample[torch.randperm(sample.size(0))]
+
+        print_memory_stats(f'***3*** \t RANK:{self.rank}')
+
+        heldout_fraction = 0.05
+        heldout_size = int(min(heldout_fraction * sample.size(0), 50_000))
+        sample, sample_heldout = sample.split([sample.size(0) - heldout_size, heldout_size], dim=0)
+
+        print_memory_stats(f'***4*** \t RANK:{self.rank}')
+
+        return sample, sample_heldout
+
+    def _train_kmeans(self, sample, shared_lists):
+        if self.use_gpu:
+            torch.cuda.empty_cache()
+
+        do_fork_for_faiss = False  # set to True to free faiss GPU-0 memory at the cost of one more copy of `sample`.
+
+        args_ = [self.config.dim, self.num_partitions, self.config.kmeans_niters]
+
+        if do_fork_for_faiss:
+            # For this to work reliably, write the sample to disk. Pickle may not handle >4GB of data.
+            # Delete the sample file after work is done.
+
+            shared_lists[0][0] = sample
+            return_value_queue = mp.Queue()
+
+            args_ = args_ + [shared_lists, return_value_queue]
+            proc = mp.Process(target=compute_faiss_kmeans, args=args_)
+
+            proc.start()
+            centroids = return_value_queue.get()
+            proc.join()
+
+        else:
+            args_ = args_ + [[[sample]]]
+            centroids = compute_faiss_kmeans(*args_)
+
+        centroids = torch.nn.functional.normalize(centroids, dim=-1)
+        if self.use_gpu:
+            centroids = centroids.half()
+        else:
+            centroids = centroids.float()
+
+        return centroids
+
+    def _compute_avg_residual(self, centroids, heldout):
+        compressor = ResidualCodec(config=self.config, centroids=centroids, avg_residual=None)
+
+        heldout_reconstruct = compressor.compress_into_codes(heldout, out_device='cuda' if self.use_gpu else 'cpu')
+        heldout_reconstruct = compressor.lookup_centroids(heldout_reconstruct, out_device='cuda' if self.use_gpu else 'cpu')
+        if self.use_gpu:
+            heldout_avg_residual = heldout.cuda() - heldout_reconstruct
+        else:
+            heldout_avg_residual = heldout - heldout_reconstruct
+
+        avg_residual = torch.abs(heldout_avg_residual).mean(dim=0).cpu()
+        print([round(x, 3) for x in avg_residual.squeeze().tolist()])
+
+        num_options = 2 ** self.config.nbits
+        quantiles = torch.arange(0, num_options, device=heldout_avg_residual.device) * (1 / num_options)
+        bucket_cutoffs_quantiles, bucket_weights_quantiles = quantiles[1:], quantiles + (0.5 / num_options)
+
+        bucket_cutoffs = heldout_avg_residual.float().quantile(bucket_cutoffs_quantiles)
+        bucket_weights = heldout_avg_residual.float().quantile(bucket_weights_quantiles)
+
+        print_message(
+            f"#> Got bucket_cutoffs_quantiles = {bucket_cutoffs_quantiles} and bucket_weights_quantiles = {bucket_weights_quantiles}")
+        print_message(f"#> Got bucket_cutoffs = {bucket_cutoffs} and bucket_weights = {bucket_weights}")
+
+        return bucket_cutoffs, bucket_weights, avg_residual.mean()
+
+        # EVENTAULLY: Compare the above with non-heldout sample. If too different, we can do better!
+        # sample = sample[subsample_idxs]
+        # sample_reconstruct = get_centroids_for(centroids, sample)
+        # sample_avg_residual = (sample - sample_reconstruct).mean(dim=0)
+
+    def index(self):
+        with self.saver.thread():
+            batches = self.collection.enumerate_batches(rank=self.rank)
+            for chunk_idx, offset, passages in tqdm.tqdm(batches, disable=self.rank > 0):
+                if self.config.resume and self.saver.check_chunk_exists(chunk_idx):
+                    Run().print_main(f"#> Found chunk {chunk_idx} in the index already, skipping encoding...")
+                    continue
+                embs, doclens = self.encoder.encode_passages(passages)
+                if self.use_gpu:
+                    assert embs.dtype == torch.float16
+                else:
+                    assert embs.dtype == torch.float32
+                    embs = embs.half()
+
+                Run().print_main(f"#> Saving chunk {chunk_idx}: \t {len(passages):,} passages "
+                                 f"and {embs.size(0):,} embeddings. From #{offset:,} onward.")
+
+                self.saver.save_chunk(chunk_idx, offset, embs, doclens)
+                del embs, doclens
+
+    def finalize(self):
+        if self.rank > 0:
+            return
+
+        self._check_all_files_are_saved()
+        self._collect_embedding_id_offset()
+
+        self._build_ivf()
+        self._update_metadata()
+
+    def _check_all_files_are_saved(self):
+        Run().print_main("#> Checking all files were saved...")
+        success = True
+        for chunk_idx in range(self.num_chunks):
+            if not self.saver.check_chunk_exists(chunk_idx):
+                success = False
+                Run().print_main(f"#> ERROR: Could not find chunk {chunk_idx}!")
+                #TODO: Fail here?
+        if success:
+            Run().print_main("Found all files!")
+
+    def _collect_embedding_id_offset(self):
+        passage_offset = 0
+        embedding_offset = 0
+
+        self.embedding_offsets = []
+
+        for chunk_idx in range(self.num_chunks):
+            metadata_path = os.path.join(self.config.index_path_, f'{chunk_idx}.metadata.json')
+
+            with open(metadata_path) as f:
+                chunk_metadata = ujson.load(f)
+
+                chunk_metadata['embedding_offset'] = embedding_offset
+                self.embedding_offsets.append(embedding_offset)
+
+                assert chunk_metadata['passage_offset'] == passage_offset, (chunk_idx, passage_offset, chunk_metadata)
+
+                passage_offset += chunk_metadata['num_passages']
+                embedding_offset += chunk_metadata['num_embeddings']
+
+            with open(metadata_path, 'w') as f:
+                f.write(ujson.dumps(chunk_metadata, indent=4) + '\n')
+
+        self.num_embeddings = embedding_offset
+        assert len(self.embedding_offsets) == self.num_chunks
+
+    def _build_ivf(self):
+        # Maybe we should several small IVFs? Every 250M embeddings, so that's every 1 GB.
+        # It would save *memory* here and *disk space* regarding the int64.
+        # But we'd have to decide how many IVFs to use during retrieval: many (loop) or one?
+        # A loop seems nice if we can find a size that's large enough for speed yet small enough to fit on GPU!
+        # Then it would help nicely for batching later: 1GB.
+
+        Run().print_main("#> Building IVF...")
+
+        codes = torch.empty(self.num_embeddings,)
+        print_memory_stats(f'RANK:{self.rank}')
+
+        Run().print_main("#> Loading codes...")
+
+        for chunk_idx in tqdm.tqdm(range(self.num_chunks)):
+            offset = self.embedding_offsets[chunk_idx]
+            chunk_codes = ResidualCodec.Embeddings.load_codes(self.config.index_path_, chunk_idx)
+
+            codes[offset:offset+chunk_codes.size(0)] = chunk_codes
+
+        assert offset+chunk_codes.size(0) == codes.size(0), (offset, chunk_codes.size(0), codes.size())
+
+
+        Run().print_main(f"Sorting codes...")
+
+        print_memory_stats(f'RANK:{self.rank}')
+
+        codes = codes.sort()
+        ivf, values = codes.indices, codes.values
+
+        print_memory_stats(f'RANK:{self.rank}')
+
+        Run().print_main(f"Getting unique codes...")
+
+        partitions, ivf_lengths = values.unique_consecutive(return_counts=True)
+
+        # All partitions should be non-empty. (We can use torch.histc otherwise.)
+        assert partitions.size(0) == self.num_partitions, (partitions.size(), self.num_partitions)
+
+        print_memory_stats(f'RANK:{self.rank}')
+
+        _, _ = optimize_ivf(ivf, ivf_lengths, self.config.index_path_)
+
+    def _update_metadata(self):
+        config = self.config
+        self.metadata_path = os.path.join(config.index_path_, 'metadata.json')
+        Run().print("#> Saving the indexing metadata to", self.metadata_path, "..")
+
+        with open(self.metadata_path, 'w') as f:
+            d = {'config': config.export()}
+            d['num_chunks'] = self.num_chunks
+            d['num_partitions'] = self.num_partitions
+            d['num_embeddings'] = self.num_embeddings
+            d['avg_doclen'] = self.num_embeddings / len(self.collection)
+
+            f.write(ujson.dumps(d, indent=4) + '\n')
+
+
+def compute_faiss_kmeans(dim, num_partitions, kmeans_niters, shared_lists, return_value_queue=None):
+    use_gpu = torch.cuda.is_available()
+    kmeans = faiss.Kmeans(dim, num_partitions, niter=kmeans_niters, gpu=use_gpu, verbose=True, seed=123)
+
+    sample = shared_lists[0][0]
+    sample = sample.float().numpy()
+
+    kmeans.train(sample)
+
+    centroids = torch.from_numpy(kmeans.centroids)
+
+    print_memory_stats(f'RANK:0*')
+
+    if return_value_queue is not None:
+        return_value_queue.put(centroids)
+
+    return centroids
+
+
+"""
+TODOs:
+
+1. Notice we're using self.config.bsize.
+
+2. Consider saving/using heldout_avg_residual as a vector --- that is, using 128 averages!
+
+3. Consider the operations with .cuda() tensors. Are all of them good for OOM?
+"""
diff --git a/colbert/indexing/index_manager.py b/colbert/indexing/index_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..946cffcb2c439aff739fcf19411f7fe2bd8e1bfe
--- /dev/null
+++ b/colbert/indexing/index_manager.py
@@ -0,0 +1,38 @@
+import torch
+import numpy as np
+
+from bitarray import bitarray
+
+
+class IndexManager():
+    def __init__(self, dim):
+        self.dim = dim
+
+    def save(self, tensor, path_prefix):
+        torch.save(tensor, path_prefix)
+
+    def save_bitarray(self, bitarray, path_prefix):
+        with open(path_prefix, "wb") as f:
+            bitarray.tofile(f)
+
+
+def load_index_part(filename, verbose=True):
+    part = torch.load(filename)
+
+    if type(part) == list:  # for backward compatibility
+        part = torch.cat(part)
+
+    return part
+
+
+def load_compressed_index_part(filename, dim, bits):
+    a = bitarray()
+
+    with open(filename, "rb") as f:
+        a.fromfile(f)
+
+    n = len(a) // dim // bits
+    part = torch.tensor(np.frombuffer(a.tobytes(), dtype=np.uint8))  # TODO: isn't from_numpy(.) faster?
+    part = part.reshape((n, int(np.ceil(dim * bits / 8))))
+
+    return part
diff --git a/colbert/indexing/index_saver.py b/colbert/indexing/index_saver.py
new file mode 100644
index 0000000000000000000000000000000000000000..436d130b0c63e68a80166331a73a16d5cdd149d2
--- /dev/null
+++ b/colbert/indexing/index_saver.py
@@ -0,0 +1,90 @@
+import os
+import queue
+import ujson
+import threading
+
+from contextlib import contextmanager
+
+from colbert.indexing.codecs.residual import ResidualCodec
+
+from colbert.utils.utils import print_message
+
+
+class IndexSaver():
+    def __init__(self, config):
+        self.config = config
+
+    def save_codec(self, codec):
+        codec.save(index_path=self.config.index_path_)
+
+    def load_codec(self):
+        return ResidualCodec.load(index_path=self.config.index_path_)
+
+    def try_load_codec(self):
+        try:
+            ResidualCodec.load(index_path=self.config.index_path_)
+            return True
+        except Exception as e:
+            return False
+
+    def check_chunk_exists(self, chunk_idx):
+        # TODO: Verify that the chunk has the right amount of data?
+
+        doclens_path = os.path.join(self.config.index_path_, f'doclens.{chunk_idx}.json')
+        if not os.path.exists(doclens_path):
+            return False
+
+        metadata_path = os.path.join(self.config.index_path_, f'{chunk_idx}.metadata.json')
+        if not os.path.exists(metadata_path):
+            return False
+
+        path_prefix = os.path.join(self.config.index_path_, str(chunk_idx))
+        codes_path = f'{path_prefix}.codes.pt'
+        if not os.path.exists(codes_path):
+            return False
+
+        residuals_path = f'{path_prefix}.residuals.pt'  # f'{path_prefix}.residuals.bn'
+        if not os.path.exists(residuals_path):
+            return False
+
+        return True
+
+    @contextmanager
+    def thread(self):
+        self.codec = self.load_codec()
+
+        self.saver_queue = queue.Queue(maxsize=3)
+        thread = threading.Thread(target=self._saver_thread)
+        thread.start()
+
+        try:
+            yield
+
+        finally:
+            self.saver_queue.put(None)
+            thread.join()
+
+            del self.saver_queue
+            del self.codec
+
+    def save_chunk(self, chunk_idx, offset, embs, doclens):
+        compressed_embs = self.codec.compress(embs)
+
+        self.saver_queue.put((chunk_idx, offset, compressed_embs, doclens))
+
+    def _saver_thread(self):
+        for args in iter(self.saver_queue.get, None):
+            self._write_chunk_to_disk(*args)
+
+    def _write_chunk_to_disk(self, chunk_idx, offset, compressed_embs, doclens):
+        path_prefix = os.path.join(self.config.index_path_, str(chunk_idx))
+        compressed_embs.save(path_prefix)
+
+        doclens_path = os.path.join(self.config.index_path_, f'doclens.{chunk_idx}.json')
+        with open(doclens_path, 'w') as output_doclens:
+            ujson.dump(doclens, output_doclens)
+
+        metadata_path = os.path.join(self.config.index_path_, f'{chunk_idx}.metadata.json')
+        with open(metadata_path, 'w') as output_metadata:
+            metadata = {'passage_offset': offset, 'num_passages': len(doclens), 'num_embeddings': len(compressed_embs)}
+            ujson.dump(metadata, output_metadata)
diff --git a/colbert/indexing/loaders.py b/colbert/indexing/loaders.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c1edb63b1d38db39c03dccc9e48adef1b17d81d
--- /dev/null
+++ b/colbert/indexing/loaders.py
@@ -0,0 +1,66 @@
+import re
+import os
+import ujson
+
+
+def get_parts(directory):
+    extension = '.pt'
+
+    parts = sorted([int(filename[: -1 * len(extension)]) for filename in os.listdir(directory)
+                    if filename.endswith(extension)])
+
+    assert list(range(len(parts))) == parts, parts
+
+    # Integer-sortedness matters.
+    parts_paths = [os.path.join(directory, '{}{}'.format(filename, extension)) for filename in parts]
+    samples_paths = [os.path.join(directory, '{}.sample'.format(filename)) for filename in parts]
+
+    return parts, parts_paths, samples_paths
+
+
+def load_doclens(directory, flatten=True):
+    doclens_filenames = {}
+
+    for filename in os.listdir(directory):
+        match = re.match("doclens.(\d+).json", filename)
+
+        if match is not None:
+            doclens_filenames[int(match.group(1))] = filename
+
+    doclens_filenames = [os.path.join(directory, doclens_filenames[i]) for i in sorted(doclens_filenames.keys())]
+
+    all_doclens = [ujson.load(open(filename)) for filename in doclens_filenames]
+
+    if flatten:
+        all_doclens = [x for sub_doclens in all_doclens for x in sub_doclens]
+    
+    if len(all_doclens) == 0:
+        raise ValueError("Could not load doclens")
+
+    return all_doclens
+
+
+def get_deltas(directory):
+    extension = '.residuals.pt'
+
+    parts = sorted([int(filename[: -1 * len(extension)]) for filename in os.listdir(directory)
+                    if filename.endswith(extension)])
+
+    assert list(range(len(parts))) == parts, parts
+
+    # Integer-sortedness matters.
+    parts_paths = [os.path.join(directory, '{}{}'.format(filename, extension)) for filename in parts]
+
+    return parts, parts_paths
+
+
+# def load_compression_data(level, path):
+#     with open(path, "r") as f:
+#         for line in f:
+#             line = line.split(',')
+#             bits = int(line[0])
+
+#             if bits == level:
+#                 return [float(v) for v in line[1:]]
+
+#     raise ValueError(f"No data found for {level}-bit compression")
diff --git a/colbert/indexing/utils.py b/colbert/indexing/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a713adba05459615c8e1ebe1a0d63c372c6dd41
--- /dev/null
+++ b/colbert/indexing/utils.py
@@ -0,0 +1,54 @@
+import os
+import torch
+import tqdm
+
+from colbert.indexing.loaders import load_doclens
+from colbert.utils.utils import print_message, flatten
+
+def optimize_ivf(orig_ivf, orig_ivf_lengths, index_path):
+    print_message("#> Optimizing IVF to store map from centroids to list of pids..")
+
+    print_message("#> Building the emb2pid mapping..")
+    all_doclens = load_doclens(index_path, flatten=False)
+
+    # assert self.num_embeddings == sum(flatten(all_doclens))
+
+    all_doclens = flatten(all_doclens)
+    total_num_embeddings = sum(all_doclens)
+
+    emb2pid = torch.zeros(total_num_embeddings, dtype=torch.int)
+
+    """
+    EVENTUALLY: Use two tensors. emb2pid_offsets will have every 256th element.
+    emb2pid_delta will have the delta from the corresponding offset,
+    """
+
+    offset_doclens = 0
+    for pid, dlength in enumerate(all_doclens):
+        emb2pid[offset_doclens: offset_doclens + dlength] = pid
+        offset_doclens += dlength
+
+    print_message("len(emb2pid) =", len(emb2pid))
+
+    ivf = emb2pid[orig_ivf]
+    unique_pids_per_centroid = []
+    ivf_lengths = []
+
+    offset = 0
+    for length in tqdm.tqdm(orig_ivf_lengths.tolist()):
+        pids = torch.unique(ivf[offset:offset+length])
+        unique_pids_per_centroid.append(pids)
+        ivf_lengths.append(pids.shape[0])
+        offset += length
+    ivf = torch.cat(unique_pids_per_centroid)
+    ivf_lengths = torch.tensor(ivf_lengths)
+
+    original_ivf_path = os.path.join(index_path, 'ivf.pt')
+    optimized_ivf_path = os.path.join(index_path, 'ivf.pid.pt')
+    torch.save((ivf, ivf_lengths), optimized_ivf_path)
+    print_message(f"#> Saved optimized IVF to {optimized_ivf_path}")
+    if os.path.exists(original_ivf_path):
+        print_message(f"#> Original IVF at path \"{original_ivf_path}\" can now be removed")
+
+    return ivf, ivf_lengths
+
diff --git a/colbert/infra/__init__.py b/colbert/infra/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac1a6334343250f48a31e14da81e92e6521c0258
--- /dev/null
+++ b/colbert/infra/__init__.py
@@ -0,0 +1,2 @@
+from .run import *
+from .config import *
\ No newline at end of file
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diff --git a/colbert/infra/config/__init__.py b/colbert/infra/config/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..60d79da316fdd60747af1884b6751aa155522c2b
--- /dev/null
+++ b/colbert/infra/config/__init__.py
@@ -0,0 +1,2 @@
+from .config import *
+from .settings import *
\ No newline at end of file
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diff --git a/colbert/infra/config/base_config.py b/colbert/infra/config/base_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..38f3629a028489dffba0b2809d5621da8710c44f
--- /dev/null
+++ b/colbert/infra/config/base_config.py
@@ -0,0 +1,105 @@
+import os
+import torch
+import ujson
+import dataclasses
+
+from typing import Any
+from collections import defaultdict
+from dataclasses import dataclass, fields
+from colbert.utils.utils import timestamp, torch_load_dnn
+
+from utility.utils.save_metadata import get_metadata_only
+from .core_config import *
+
+
+@dataclass
+class BaseConfig(CoreConfig):
+    @classmethod
+    def from_existing(cls, *sources):
+        kw_args = {}
+
+        for source in sources:
+            if source is None:
+                continue
+                
+            local_kw_args = dataclasses.asdict(source)
+            local_kw_args = {k: local_kw_args[k] for k in source.assigned}
+            kw_args = {**kw_args, **local_kw_args}
+
+        obj = cls(**kw_args)
+
+        return obj
+
+    @classmethod
+    def from_deprecated_args(cls, args):
+        obj = cls()
+        ignored = obj.configure(ignore_unrecognized=True, **args)
+
+        return obj, ignored
+
+    @classmethod
+    def from_path(cls, name):
+        with open(name) as f:
+            args = ujson.load(f)
+
+            if 'config' in args:
+                args = args['config']
+
+        return cls.from_deprecated_args(args)  # the new, non-deprecated version functions the same at this level.
+    
+    @classmethod
+    def load_from_checkpoint(cls, checkpoint_path):
+        if checkpoint_path.endswith('.dnn'):
+            dnn = torch_load_dnn(checkpoint_path)
+            config, _ = cls.from_deprecated_args(dnn.get('arguments', {}))
+
+            # TODO: FIXME: Decide if the line below will have any unintended consequences. We don't want to overwrite those!
+            config.set('checkpoint', checkpoint_path)
+
+            return config
+
+        loaded_config_path = os.path.join(checkpoint_path, 'artifact.metadata')
+        if os.path.exists(loaded_config_path):
+            loaded_config, _ = cls.from_path(loaded_config_path)
+            loaded_config.set('checkpoint', checkpoint_path)
+
+            return loaded_config
+
+        return None  # can happen if checkpoint_path is something like 'bert-base-uncased'
+
+    @classmethod
+    def load_from_index(cls, index_path):
+        # FIXME: We should start here with initial_config = ColBERTConfig(config, Run().config).
+        # This should allow us to say initial_config.index_root. Then, below, set config = Config(..., initial_c)
+
+        # default_index_root = os.path.join(Run().root, Run().experiment, 'indexes/')
+        # index_path = os.path.join(default_index_root, index_path)
+
+        # CONSIDER: No more plan/metadata.json. Only metadata.json to avoid weird issues when loading an index.
+
+        try:
+            metadata_path = os.path.join(index_path, 'metadata.json')
+            loaded_config, _ = cls.from_path(metadata_path)
+        except:
+            metadata_path = os.path.join(index_path, 'plan.json')
+            loaded_config, _ = cls.from_path(metadata_path)
+        
+        return loaded_config
+
+    def save(self, path, overwrite=False):
+        assert overwrite or not os.path.exists(path), path
+
+        with open(path, 'w') as f:
+            args = self.export()  # dict(self.__config)
+            args['meta'] = get_metadata_only()
+            args['meta']['version'] = 'colbert-v0.4'
+            # TODO: Add git_status details.. It can't be too large! It should be a path that Runs() saves on exit, maybe!
+
+            f.write(ujson.dumps(args, indent=4) + '\n')
+
+    def save_for_checkpoint(self, checkpoint_path):
+        assert not checkpoint_path.endswith('.dnn'), \
+            f"{checkpoint_path}: We reserve *.dnn names for the deprecated checkpoint format."
+
+        output_config_path = os.path.join(checkpoint_path, 'artifact.metadata')
+        self.save(output_config_path, overwrite=True)
diff --git a/colbert/infra/config/config.py b/colbert/infra/config/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..61dab1d0ee4e66850b5834b7ed861b420168f1d3
--- /dev/null
+++ b/colbert/infra/config/config.py
@@ -0,0 +1,15 @@
+from dataclasses import dataclass
+
+from .base_config import BaseConfig
+from .settings import *
+
+
+@dataclass
+class RunConfig(BaseConfig, RunSettings):
+    pass
+
+
+@dataclass
+class ColBERTConfig(RunSettings, ResourceSettings, DocSettings, QuerySettings, TrainingSettings,
+                    IndexingSettings, SearchSettings, BaseConfig):
+    pass
diff --git a/colbert/infra/config/core_config.py b/colbert/infra/config/core_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa5f695664f901882f1014548c3a341cf957eec3
--- /dev/null
+++ b/colbert/infra/config/core_config.py
@@ -0,0 +1,86 @@
+import os
+import torch
+import ujson
+import dataclasses
+
+from typing import Any
+from collections import defaultdict
+from dataclasses import dataclass, fields
+from colbert.utils.utils import timestamp, torch_load_dnn
+
+from utility.utils.save_metadata import get_metadata_only
+
+
+@dataclass
+class DefaultVal:
+    val: Any
+
+
+@dataclass
+class CoreConfig:
+    def __post_init__(self):
+        """
+        Source: https://stackoverflow.com/a/58081120/1493011
+        """
+
+        self.assigned = {}
+
+        for field in fields(self):
+            field_val = getattr(self, field.name)
+
+            if isinstance(field_val, DefaultVal) or field_val is None:
+                setattr(self, field.name, field.default.val)
+
+            if not isinstance(field_val, DefaultVal):
+                self.assigned[field.name] = True
+    
+    def assign_defaults(self):
+        for field in fields(self):
+            setattr(self, field.name, field.default.val)
+            self.assigned[field.name] = True
+
+    def configure(self, ignore_unrecognized=True, **kw_args):
+        ignored = set()
+
+        for key, value in kw_args.items():
+            self.set(key, value, ignore_unrecognized) or ignored.update({key})
+
+        return ignored
+
+        """
+        # TODO: Take a config object, not kw_args.
+
+        for key in config.assigned:
+            value = getattr(config, key)
+        """
+
+    def set(self, key, value, ignore_unrecognized=False):
+        if hasattr(self, key):
+            setattr(self, key, value)
+            self.assigned[key] = True
+            return True
+
+        if not ignore_unrecognized:
+            raise Exception(f"Unrecognized key `{key}` for {type(self)}")
+
+    def help(self):
+        print(ujson.dumps(dataclasses.asdict(self), indent=4))
+
+    def __export_value(self, v):
+        v = v.provenance() if hasattr(v, 'provenance') else v
+
+        if isinstance(v, list) and len(v) > 100:
+            v = (f"list with {len(v)} elements starting with...", v[:3])
+
+        if isinstance(v, dict) and len(v) > 100:
+            v = (f"dict with {len(v)} keys starting with...", list(v.keys())[:3])
+
+        return v
+
+    def export(self):
+        d = dataclasses.asdict(self)
+
+        for k, v in d.items():
+            d[k] = self.__export_value(v)
+
+        return d
diff --git a/colbert/infra/config/settings.py b/colbert/infra/config/settings.py
new file mode 100644
index 0000000000000000000000000000000000000000..6653fbfa7445fdd3caf12441d323213ad562fae8
--- /dev/null
+++ b/colbert/infra/config/settings.py
@@ -0,0 +1,165 @@
+import os
+import torch
+
+import __main__
+from dataclasses import dataclass
+from colbert.utils.utils import timestamp
+
+from .core_config import DefaultVal
+
+
+@dataclass
+class RunSettings:
+    """
+        The defaults here have a special status in Run(), which initially calls assign_defaults(),
+        so these aren't soft defaults in that specific context.
+    """
+
+    overwrite: bool = DefaultVal(False)
+
+    root: str = DefaultVal(os.path.join(os.getcwd(), 'experiments'))
+    experiment: str = DefaultVal('default')
+
+    index_root: str = DefaultVal(None)
+    name: str = DefaultVal(timestamp(daydir=True))
+
+    rank: int = DefaultVal(0)
+    nranks: int = DefaultVal(1)
+    amp: bool = DefaultVal(True)
+
+    total_visible_gpus = torch.cuda.device_count()
+    gpus: int = DefaultVal(total_visible_gpus)
+
+    @property
+    def gpus_(self):
+        value = self.gpus
+
+        if isinstance(value, int):
+            value = list(range(value))
+
+        if isinstance(value, str):
+            value = value.split(',')
+
+        value = list(map(int, value))
+        value = sorted(list(set(value)))
+
+        assert all(device_idx in range(0, self.total_visible_gpus) for device_idx in value), value
+
+        return value
+
+    @property
+    def index_root_(self):
+        return self.index_root or os.path.join(self.root, self.experiment, 'indexes/')
+
+    @property
+    def script_name_(self):
+        if '__file__' in dir(__main__):
+            cwd = os.path.abspath(os.getcwd())
+            script_path = os.path.abspath(__main__.__file__)
+            root_path = os.path.abspath(self.root)
+
+            if script_path.startswith(cwd):
+                script_path = script_path[len(cwd):]
+
+            else:
+                try:
+                    commonpath = os.path.commonpath([script_path, root_path])
+                    script_path = script_path[len(commonpath):]
+                except:
+                    pass
+
+
+            assert script_path.endswith('.py')
+            script_name = script_path.replace('/', '.').strip('.')[:-3]
+
+            assert len(script_name) > 0, (script_name, script_path, cwd)
+
+            return script_name
+
+        return 'none'
+
+    @property
+    def path_(self):
+        return os.path.join(self.root, self.experiment, self.script_name_, self.name)
+
+    @property
+    def device_(self):
+        return self.gpus_[self.rank % self.nranks]
+
+
+@dataclass
+class ResourceSettings:
+    checkpoint: str = DefaultVal(None)
+    triples: str = DefaultVal(None)
+    collection: str = DefaultVal(None)
+    queries: str = DefaultVal(None)
+    index_name: str = DefaultVal(None)
+
+
+@dataclass
+class DocSettings:
+    dim: int = DefaultVal(128)
+    doc_maxlen: int = DefaultVal(220)
+    mask_punctuation: bool = DefaultVal(True)
+
+
+@dataclass
+class QuerySettings:
+    query_maxlen: int = DefaultVal(32)
+    attend_to_mask_tokens : bool = DefaultVal(False)
+    interaction: str = DefaultVal('colbert')
+
+
+@dataclass
+class TrainingSettings:
+    similarity: str = DefaultVal('cosine')
+
+    bsize: int = DefaultVal(32)
+
+    accumsteps: int = DefaultVal(1)
+
+    lr: float = DefaultVal(3e-06)
+
+    maxsteps: int = DefaultVal(500_000)
+
+    save_every: int = DefaultVal(None)
+
+    resume: bool = DefaultVal(False)
+
+    ## NEW:
+    warmup: int = DefaultVal(None)
+
+    warmup_bert: int = DefaultVal(None)
+
+    relu: bool = DefaultVal(False)
+
+    nway: int = DefaultVal(2)
+
+    use_ib_negatives: bool = DefaultVal(False)
+
+    reranker: bool = DefaultVal(False)
+
+    distillation_alpha: float = DefaultVal(1.0)
+
+    ignore_scores: bool = DefaultVal(False)
+
+
+@dataclass
+class IndexingSettings:
+    index_path: str = DefaultVal(None)
+
+    nbits: int = DefaultVal(1)
+
+    kmeans_niters: int = DefaultVal(4)
+
+    resume: bool = DefaultVal(False)
+
+    @property
+    def index_path_(self):
+        return self.index_path or os.path.join(self.index_root_, self.index_name)
+
+@dataclass
+class SearchSettings:
+    ncells: int = DefaultVal(None)
+    centroid_score_threshold: float = DefaultVal(None)
+    ndocs: int = DefaultVal(None)
diff --git a/colbert/infra/launcher.py b/colbert/infra/launcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..f55b2892a78a0714888e79412de36919eda93071
--- /dev/null
+++ b/colbert/infra/launcher.py
@@ -0,0 +1,147 @@
+import os
+import time
+import torch
+import random
+
+import torch.multiprocessing as mp
+import numpy as np
+
+try:
+    mp.set_start_method('spawn', force=True)
+except RuntimeError:
+    pass
+
+import colbert.utils.distributed as distributed
+
+from colbert.infra.run import Run
+from colbert.infra.config import BaseConfig, RunConfig, RunSettings
+
+from colbert.utils.utils import print_message
+
+
+class Launcher:
+    def __init__(self, callee, run_config=None, return_all=False):
+        self.callee = callee
+        self.return_all = return_all
+
+        self.run_config = RunConfig.from_existing(Run().config, run_config)
+        self.nranks = self.run_config.nranks
+
+    def launch(self, custom_config, *args):
+        return_value_queue = mp.Queue()
+
+        rng = random.Random(time.time())
+        port = str(12355 + rng.randint(0, 1000))  # randomize the port to avoid collision on launching several jobs.
+
+        all_procs = []
+        for new_rank in range(0, self.nranks):
+            assert isinstance(custom_config, BaseConfig)
+            assert isinstance(custom_config, RunSettings)
+
+            new_config = type(custom_config).from_existing(custom_config, self.run_config, RunConfig(rank=new_rank))
+
+            args_ = (self.callee, port, return_value_queue, new_config, *args)
+            all_procs.append(mp.Process(target=setup_new_process, args=args_))
+
+        # Clear GPU space (e.g., after a `Searcher` on GPU-0 is deleted)
+        # TODO: Generalize this from GPU-0 only!
+        # TODO: Move this to a function. And call that function from __del__ in a class that's inherited by Searcher, Indexer, etc.
+
+        # t = torch.cuda.get_device_properties(0).total_memory
+        # r = torch.cuda.memory_reserved(0)
+        # a = torch.cuda.memory_allocated(0)
+        # f = r-a
+
+        # print_message(f"[Pre-Emptying] GPU memory check: r={r}, a={a}, f={f}")
+
+        torch.cuda.empty_cache()
+
+        # t = torch.cuda.get_device_properties(0).total_memory
+        # r = torch.cuda.memory_reserved(0)
+        # a = torch.cuda.memory_allocated(0)
+        # f = r-a
+
+        # print_message(f"[Post-Emptying] GPU memory check: r={r}, a={a}, f={f}")
+
+        print_memory_stats('MAIN')
+
+        for proc in all_procs:
+            print("#> Starting...")
+            proc.start()
+
+        print_memory_stats('MAIN')
+
+        # TODO: If the processes crash upon join, raise an exception and don't block on .get() below!
+
+        return_values = sorted([return_value_queue.get() for _ in all_procs])
+        return_values = [val for rank, val in return_values]
+
+        if not self.return_all:
+            return_values = return_values[0]
+        
+        for proc in all_procs:
+            proc.join()
+            print("#> Joined...")
+
+        print_memory_stats('MAIN')
+        
+        return return_values
+
+
+def setup_new_process(callee, port, return_value_queue, config, *args):
+    print_memory_stats()
+
+    random.seed(12345)
+    np.random.seed(12345)
+    torch.manual_seed(12345)
+    torch.cuda.manual_seed_all(12345)
+
+    rank, nranks = config.rank, config.nranks
+
+    os.environ["MASTER_ADDR"] = "localhost"
+    os.environ["MASTER_PORT"] = port
+    os.environ["WORLD_SIZE"] = str(config.nranks)
+    os.environ["RANK"] = str(config.rank)
+
+    # TODO: Ideally the gpus "getter" handles this max-nranks thing!
+    os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(map(str, config.gpus_[:nranks]))
+
+    nranks_, distributed_ = distributed.init(rank)
+    assert nranks_ == nranks
+
+    # Run.init(args.rank, args.root, args.experiment, args.run)
+
+    with Run().context(config, inherit_config=False):
+        return_val = callee(config, *args)
+
+    return_value_queue.put((rank, return_val))
+
+
+def print_memory_stats(message=''):
+    return  # FIXME: Add this back before release.
+
+    import psutil  # Remove before releases? Or at least make optional with try/except.
+
+    global_info = psutil.virtual_memory()
+    total, available, used, free = global_info.total, global_info.available, global_info.used, global_info.free
+
+    info = psutil.Process().memory_info()
+    rss, vms, shared = info.rss, info.vms, info.shared
+    uss = psutil.Process().memory_full_info().uss
+
+    gib = 1024 ** 3
+
+    summary = f"""
+    "[PID: {os.getpid()}]
+    [{message}]
+    Available: {available / gib:,.1f} / {total / gib:,.1f}
+    Free: {free / gib:,.1f} / {total / gib:,.1f}
+    Usage: {used / gib:,.1f} / {total / gib:,.1f}
+
+    RSS: {rss  / gib:,.1f}
+    VMS: {vms  / gib:,.1f}
+    USS: {uss  / gib:,.1f}
+    SHARED: {shared  / gib:,.1f}
+    """.strip().replace('\n', '\t')
+
+    print_message(summary, pad=True)
diff --git a/colbert/infra/provenance.py b/colbert/infra/provenance.py
new file mode 100644
index 0000000000000000000000000000000000000000..114644871e762d9884a7024426b4afdc9ac1907b
--- /dev/null
+++ b/colbert/infra/provenance.py
@@ -0,0 +1,43 @@
+import sys
+import traceback
+import inspect
+
+
+class Provenance:
+    def __init__(self) -> None:
+        self.initial_stacktrace = self.stacktrace()
+
+    def stacktrace(self):
+        trace = inspect.stack()
+        output = []
+
+        for frame in trace[2:-1]:
+            try:
+                frame = f'{frame.filename}:{frame.lineno}:{frame.function}:   {frame.code_context[0].strip()}'
+                output.append(frame)
+            except:
+                output.append(None)
+
+        return output
+
+    def toDict(self):  # for ujson
+        self.serialization_stacktrace = self.stacktrace()
+        return dict(self.__dict__)
+
+
+if __name__ == '__main__':
+    p = Provenance()
+    print(p.toDict().keys())
+
+    import ujson
+    print(ujson.dumps(p, indent=4))
+
+
+    class X:
+        def __init__(self) -> None:
+            pass
+        
+        def toDict(self):
+            return {'key': 1}
+    
+    print(ujson.dumps(X()))
\ No newline at end of file
diff --git a/colbert/infra/run.py b/colbert/infra/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..c390494a3387128f776bd4b480879caeaeb4ff6d
--- /dev/null
+++ b/colbert/infra/run.py
@@ -0,0 +1,92 @@
+import os
+import atexit
+
+from colbert.utils.utils import create_directory, print_message, timestamp
+from contextlib import contextmanager
+
+from colbert.infra.config import RunConfig
+
+
+class Run(object):
+    _instance = None
+
+    os.environ["TOKENIZERS_PARALLELISM"] = "true"  # NOTE: If a deadlock arises, switch to false!!
+
+    def __new__(cls):
+        """
+        Singleton Pattern. See https://python-patterns.guide/gang-of-four/singleton/
+        """
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+            cls._instance.stack = []
+
+            # TODO: Save a timestamp here! And re-use it! But allow the user to override it on calling Run().context a second time.
+            run_config = RunConfig()
+            run_config.assign_defaults()
+            
+            cls._instance.__append(run_config)
+
+        # TODO: atexit.register(all_done)
+
+        return cls._instance
+
+    @property
+    def config(self):
+        return self.stack[-1]
+
+    def __getattr__(self, name):
+        if hasattr(self.config, name):
+            return getattr(self.config, name)
+
+        super().__getattr__(name)
+
+    def __append(self, runconfig: RunConfig):
+        # runconfig.disallow_writes(readonly=True)
+        self.stack.append(runconfig)
+
+    def __pop(self):
+        self.stack.pop()
+
+    @contextmanager
+    def context(self, runconfig: RunConfig, inherit_config=True):
+        if inherit_config:
+            runconfig = RunConfig.from_existing(self.config, runconfig)
+
+        self.__append(runconfig)
+
+        try:
+            yield
+        finally:
+            self.__pop()
+        
+    def open(self, path, mode='r'):
+        path = os.path.join(self.path_, path)
+
+        if not os.path.exists(self.path_):
+            create_directory(self.path_)
+
+        if ('w' in mode or 'a' in mode) and not self.overwrite:
+            assert not os.path.exists(path), (self.overwrite, path)
+
+        return open(path, mode=mode)
+    
+    def print(self, *args):
+        print_message("[" + str(self.rank) + "]", "\t\t", *args)
+
+    def print_main(self, *args):
+        if self.rank == 0:
+            self.print(*args)
+
+
+if __name__ == '__main__':
+    print(Run().root, '!')
+
+    with Run().context(RunConfig(rank=0, nranks=1)):
+        with Run().context(RunConfig(experiment='newproject')):
+            print(Run().nranks, '!')
+
+        print(Run().config, '!')
+        print(Run().rank)
+
+
+# TODO: Handle logging all prints to a file. There should be a way to determine the level of logs that go to stdout.
\ No newline at end of file
diff --git a/colbert/infra/utilities/annotate_em.py b/colbert/infra/utilities/annotate_em.py
new file mode 100644
index 0000000000000000000000000000000000000000..39c2b7b3c7d51ee8851c69ea5ce69f669da28244
--- /dev/null
+++ b/colbert/infra/utilities/annotate_em.py
@@ -0,0 +1,115 @@
+
+from colbert.infra.run import Run
+from colbert.data.collection import Collection
+import os
+import sys
+import git
+import tqdm
+import ujson
+import random
+
+from argparse import ArgumentParser
+from multiprocessing import Pool
+
+from colbert.utils.utils import groupby_first_item, print_message
+from utility.utils.qa_loaders import load_qas_, load_collection_
+from utility.utils.save_metadata import format_metadata, get_metadata
+from utility.evaluate.annotate_EM_helpers import *
+
+from colbert.data.ranking import Ranking
+
+
+class AnnotateEM:
+    def __init__(self, collection, qas):
+        # TODO: These should just be Queries! But Queries needs to support looking up answers as qid2answers below.
+        qas = load_qas_(qas)
+        collection = Collection.cast(collection)  # .tolist() #load_collection_(collection, retain_titles=True)
+
+        self.parallel_pool = Pool(30)
+
+        print_message('#> Tokenize the answers in the Q&As in parallel...')
+        qas = list(self.parallel_pool.map(tokenize_all_answers, qas))
+
+        qid2answers = {qid: tok_answers for qid, _, tok_answers in qas}
+        assert len(qas) == len(qid2answers), (len(qas), len(qid2answers))
+
+        self.qas, self.collection = qas, collection
+        self.qid2answers = qid2answers
+
+    def annotate(self, ranking):
+        rankings = Ranking.cast(ranking)
+
+        # print(len(rankings), rankings[0])
+
+        print_message('#> Lookup passages from PIDs...')
+        expanded_rankings = [(qid, pid, rank, self.collection[pid], self.qid2answers[qid])
+                             for qid, pid, rank, *_ in rankings.tolist()]
+
+        print_message('#> Assign labels in parallel...')
+        labeled_rankings = list(self.parallel_pool.map(assign_label_to_passage, enumerate(expanded_rankings)))
+
+        # Dump output.
+        self.qid2rankings = groupby_first_item(labeled_rankings)
+
+        self.num_judged_queries, self.num_ranked_queries = check_sizes(self.qid2answers, self.qid2rankings)
+
+        # Evaluation metrics and depths.
+        self.success, self.counts = self._compute_labels(self.qid2answers, self.qid2rankings)
+
+        print(rankings.provenance(), self.success)
+
+        return Ranking(data=self.qid2rankings, provenance=("AnnotateEM", rankings.provenance()))
+
+    def _compute_labels(self, qid2answers, qid2rankings):
+        cutoffs = [1, 5, 10, 20, 30, 50, 100, 1000, 'all']
+        success = {cutoff: 0.0 for cutoff in cutoffs}
+        counts = {cutoff: 0.0 for cutoff in cutoffs}
+
+        for qid in qid2answers:
+            if qid not in qid2rankings:
+                continue
+
+            prev_rank = 0  # ranks should start at one (i.e., and not zero)
+            labels = []
+
+            for pid, rank, label in qid2rankings[qid]:
+                assert rank == prev_rank+1, (qid, pid, (prev_rank, rank))
+                prev_rank = rank
+
+                labels.append(label)
+
+            for cutoff in cutoffs:
+                if cutoff != 'all':
+                    success[cutoff] += sum(labels[:cutoff]) > 0
+                    counts[cutoff] += sum(labels[:cutoff])
+                else:
+                    success[cutoff] += sum(labels) > 0
+                    counts[cutoff] += sum(labels)
+
+        return success, counts
+
+    def save(self, new_path):
+        print_message("#> Dumping output to", new_path, "...")
+
+        Ranking(data=self.qid2rankings).save(new_path)
+
+        # Dump metrics.
+        with Run().open(f'{new_path}.metrics', 'w') as f:
+            d = {'num_ranked_queries': self.num_ranked_queries, 'num_judged_queries': self.num_judged_queries}
+
+            extra = '__WARNING' if self.num_judged_queries != self.num_ranked_queries else ''
+            d[f'success{extra}'] = {k: v / self.num_judged_queries for k, v in self.success.items()}
+            d[f'counts{extra}'] = {k: v / self.num_judged_queries for k, v in self.counts.items()}
+            # d['arguments'] = get_metadata(args)  # TODO: Need arguments...
+
+            f.write(format_metadata(d) + '\n')
+
+
+if __name__ == '__main__':
+    r = '/future/u/okhattab/root/unit/experiments/2021.08/retrieve.py/2021-09-04_15.50.02/ranking.tsv'
+    r = '/future/u/okhattab/root/unit/experiments/2021.08/retrieve.py/2021-09-04_15.59.37/ranking.tsv'
+    r = sys.argv[1]
+
+    a = AnnotateEM(collection='/future/u/okhattab/root/unit/data/NQ-mini/collection.tsv',
+                   qas='/future/u/okhattab/root/unit/data/NQ-mini/dev/qas.json')
+    a.annotate(ranking=r)
diff --git a/colbert/infra/utilities/create_triples.py b/colbert/infra/utilities/create_triples.py
new file mode 100644
index 0000000000000000000000000000000000000000..3242ae09c2c6f1c84e661847dc5fac6f1a342177
--- /dev/null
+++ b/colbert/infra/utilities/create_triples.py
@@ -0,0 +1,52 @@
+import random
+
+from colbert.utils.utils import print_message
+from utility.utils.save_metadata import save_metadata
+from utility.supervision.triples import sample_for_query
+
+from colbert.data.ranking import Ranking
+from colbert.data.examples import Examples
+
+MAX_NUM_TRIPLES = 40_000_000
+
+
+class Triples:
+    def __init__(self, ranking, seed=12345):
+        random.seed(seed)  # TODO: Use internal RNG instead..
+        self.qid2rankings = Ranking.cast(ranking).todict()
+
+    def create(self, positives, depth):
+        assert all(len(x) == 2 for x in positives)
+        assert all(maxBest <= maxDepth for maxBest, maxDepth in positives), positives
+
+        Triples = []
+        NonEmptyQIDs = 0
+
+        for processing_idx, qid in enumerate(self.qid2rankings):
+            l = sample_for_query(qid, self.qid2rankings[qid], positives, depth, False, None)
+            NonEmptyQIDs += (len(l) > 0)
+            Triples.extend(l)
+
+            if processing_idx % (10_000) == 0:
+                print_message(f"#> Done with {processing_idx+1} questions!\t\t "
+                              f"{str(len(Triples) / 1000)}k triples for {NonEmptyQIDs} unqiue QIDs.")
+
+        print_message(f"#> Sub-sample the triples (if > {MAX_NUM_TRIPLES})..")
+        print_message(f"#> len(Triples) = {len(Triples)}")
+
+        if len(Triples) > MAX_NUM_TRIPLES:
+            Triples = random.sample(Triples, MAX_NUM_TRIPLES)
+
+        ### Prepare the triples ###
+        print_message("#> Shuffling the triples...")
+        random.shuffle(Triples)
+
+        self.Triples = Examples(data=Triples)
+
+        return Triples
+
+    def save(self, new_path):
+        Examples(data=self.Triples).save(new_path)
+
+        # save_metadata(f'{output}.meta', args)  # TODO: What args to save?? {seed, positives, depth, rankings if path or else whatever provenance the rankings object shares}
+
diff --git a/colbert/infra/utilities/minicorpus.py b/colbert/infra/utilities/minicorpus.py
new file mode 100644
index 0000000000000000000000000000000000000000..91f39b2b6bc5c013bc03f19adba9496c0564fefe
--- /dev/null
+++ b/colbert/infra/utilities/minicorpus.py
@@ -0,0 +1,64 @@
+import os
+import random
+
+from colbert.utils.utils import create_directory
+
+from colbert.data import Collection, Queries, Ranking
+
+
+def sample_minicorpus(name, factor, topk=30, maxdev=3000):
+    """
+    Factor:
+        * nano=1
+        * micro=10
+        * mini=100
+        * small=100 with topk=100
+        * medium=150 with topk=300
+    """
+
+    random.seed(12345)
+
+    # Load collection
+    collection = Collection(path='/dfs/scratch0/okhattab/OpenQA/collection.tsv')
+
+    # Load train and dev queries
+    qas_train = Queries(path='/dfs/scratch0/okhattab/OpenQA/NQ/train/qas.json').qas()
+    qas_dev = Queries(path='/dfs/scratch0/okhattab/OpenQA/NQ/dev/qas.json').qas()
+
+    # Load train and dev C3 rankings
+    ranking_train = Ranking(path='/dfs/scratch0/okhattab/OpenQA/NQ/train/rankings/C3.tsv.annotated').todict()
+    ranking_dev = Ranking(path='/dfs/scratch0/okhattab/OpenQA/NQ/dev/rankings/C3.tsv.annotated').todict()
+
+    # Sample NT and ND queries from each, keep only the top-k passages for those
+    sample_train = random.sample(list(qas_train.keys()), min(len(qas_train.keys()), 300*factor))
+    sample_dev = random.sample(list(qas_dev.keys()), min(len(qas_dev.keys()), maxdev, 30*factor))
+
+    train_pids = [pid for qid in sample_train for qpids in ranking_train[qid][:topk] for pid in qpids]
+    dev_pids = [pid for qid in sample_dev for qpids in ranking_dev[qid][:topk] for pid in qpids]
+
+    sample_pids = sorted(list(set(train_pids + dev_pids)))
+    print(f'len(sample_pids) = {len(sample_pids)}')
+
+    # Save the new query sets: train and dev
+    ROOT = f'/future/u/okhattab/root/unit/data/NQ-{name}'
+
+    create_directory(os.path.join(ROOT, 'train'))
+    create_directory(os.path.join(ROOT, 'dev'))
+
+    new_train = Queries(data={qid: qas_train[qid] for qid in sample_train})
+    new_train.save(os.path.join(ROOT, 'train/questions.tsv'))
+    new_train.save_qas(os.path.join(ROOT, 'train/qas.json'))
+
+    new_dev = Queries(data={qid: qas_dev[qid] for qid in sample_dev})
+    new_dev.save(os.path.join(ROOT, 'dev/questions.tsv'))
+    new_dev.save_qas(os.path.join(ROOT, 'dev/qas.json'))
+
+    # Save the new collection
+    print(f"Saving to {os.path.join(ROOT, 'collection.tsv')}")
+    Collection(data=[collection[pid] for pid in sample_pids]).save(os.path.join(ROOT, 'collection.tsv'))
+
+    print('#> Done!')
+
+
+if __name__ == '__main__':
+    sample_minicorpus('medium', 150, topk=300)
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diff --git a/colbert/modeling/base_colbert.py b/colbert/modeling/base_colbert.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c8b2709800faaf749ca980be7be85b6e4a1397c
--- /dev/null
+++ b/colbert/modeling/base_colbert.py
@@ -0,0 +1,107 @@
+import os
+import torch
+
+from colbert.utils.utils import torch_load_dnn
+
+from transformers import AutoTokenizer
+from colbert.modeling.hf_colbert import HF_ColBERT
+from colbert.infra.config import ColBERTConfig
+
+
+class BaseColBERT(torch.nn.Module):
+    """
+    Shallow module that wraps the ColBERT parameters, custom configuration, and underlying tokenizer.
+    This class provides direct instantiation and saving of the model/colbert_config/tokenizer package.
+
+    Like HF, evaluation mode is the default.
+    """
+
+    def __init__(self, name, colbert_config=None):
+        super().__init__()
+
+        self.name = name
+        self.colbert_config = ColBERTConfig.from_existing(ColBERTConfig.load_from_checkpoint(name), colbert_config)
+        self.model = HF_ColBERT.from_pretrained(name, colbert_config=self.colbert_config)
+        self.raw_tokenizer = AutoTokenizer.from_pretrained(self.model.base)
+
+        self.eval()
+
+    @property
+    def device(self):
+        return self.model.device
+
+    @property
+    def bert(self):
+        return self.model.bert
+
+    @property
+    def linear(self):
+        return self.model.linear
+    
+    @property
+    def score_scaler(self):
+        return self.model.score_scaler
+
+    def save(self, path):
+        assert not path.endswith('.dnn'), f"{path}: We reserve *.dnn names for the deprecated checkpoint format."
+
+        self.model.save_pretrained(path)
+        self.raw_tokenizer.save_pretrained(path)
+
+        self.colbert_config.save_for_checkpoint(path)
+
+
+if __name__ == '__main__':
+    import random
+    import numpy as np
+
+    from colbert.infra.run import Run
+    from colbert.infra.config import RunConfig
+
+    random.seed(12345)
+    np.random.seed(12345)
+    torch.manual_seed(12345)
+
+    with Run().context(RunConfig(gpus=2)):
+        m = BaseColBERT('bert-base-uncased', colbert_config=ColBERTConfig(Run().config, doc_maxlen=300, similarity='l2'))
+        m.colbert_config.help()
+        print(m.linear.weight)
+        m.save('/future/u/okhattab/tmp/2021/08/model.deleteme2/')
+
+    m2 = BaseColBERT('/future/u/okhattab/tmp/2021/08/model.deleteme2/')
+    m2.colbert_config.help()
+    print(m2.linear.weight)
+
+    exit()
+
+    m = BaseColBERT('/future/u/okhattab/tmp/2021/08/model.deleteme/')
+    print('BaseColBERT', m.linear.weight)
+    print('BaseColBERT', m.colbert_config)
+
+    exit()
+
+    # m = HF_ColBERT.from_pretrained('nreimers/MiniLMv2-L6-H768-distilled-from-BERT-Large')
+    m = HF_ColBERT.from_pretrained('/future/u/okhattab/tmp/2021/08/model.deleteme/')
+    print('HF_ColBERT', m.linear.weight)
+
+    m.save_pretrained('/future/u/okhattab/tmp/2021/08/model.deleteme/')
+
+    # old = OldColBERT.from_pretrained('bert-base-uncased')
+    # print(old.bert.encoder.layer[10].attention.self.value.weight)
+
+    # random.seed(12345)
+    # np.random.seed(12345)
+    # torch.manual_seed(12345)
+
+    dnn = torch_load_dnn(
+        "/future/u/okhattab/root/TACL21/experiments/Feb26.NQ/train.py/ColBERT.C3/checkpoints/colbert-60000.dnn")
+    # base = dnn.get('arguments', {}).get('model', 'bert-base-uncased')
+
+    # new = BaseColBERT.from_pretrained('bert-base-uncased', state_dict=dnn['model_state_dict'])
+
+    # print(new.bert.encoder.layer[10].attention.self.value.weight)
+
+    print(dnn['model_state_dict']['linear.weight'])
+    # print(dnn['model_state_dict']['bert.encoder.layer.10.attention.self.value.weight'])
+
+    # # base_model_prefix
diff --git a/colbert/modeling/checkpoint.py b/colbert/modeling/checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..321dda680f0a379ed90f7b01ff3004f762a4966b
--- /dev/null
+++ b/colbert/modeling/checkpoint.py
@@ -0,0 +1,146 @@
+import torch
+
+from tqdm import tqdm
+
+from colbert.modeling.tokenization import QueryTokenizer, DocTokenizer
+from colbert.utils.amp import MixedPrecisionManager
+
+from colbert.modeling.colbert import ColBERT
+
+
+class Checkpoint(ColBERT):
+    """
+        Easy inference with ColBERT.
+
+        TODO: Add .cast() accepting [also] an object instance-of(Checkpoint) as first argument.
+    """
+
+    def __init__(self, name, colbert_config=None):
+        super().__init__(name, colbert_config)
+        assert self.training is False
+
+        self.query_tokenizer = QueryTokenizer(self.colbert_config)
+        self.doc_tokenizer = DocTokenizer(self.colbert_config)
+
+        self.amp_manager = MixedPrecisionManager(True)
+
+    def query(self, *args, to_cpu=False, **kw_args):
+        with torch.no_grad():
+            with self.amp_manager.context():
+                Q = super().query(*args, **kw_args)
+                return Q.cpu() if to_cpu else Q
+
+    def doc(self, *args, to_cpu=False, **kw_args):
+        with torch.no_grad():
+            with self.amp_manager.context():
+                D = super().doc(*args, **kw_args)
+
+                if to_cpu:
+                    return (D[0].cpu(), *D[1:]) if isinstance(D, tuple) else D.cpu()
+
+                return D
+
+    def queryFromText(self, queries, bsize=None, to_cpu=False, context=None):
+        if bsize:
+            batches = self.query_tokenizer.tensorize(queries, context=context, bsize=bsize)
+            batches = [self.query(input_ids, attention_mask, to_cpu=to_cpu) for input_ids, attention_mask in batches]
+            return torch.cat(batches)
+
+        input_ids, attention_mask = self.query_tokenizer.tensorize(queries, context=context)
+        return self.query(input_ids, attention_mask)
+
+    def docFromText(self, docs, bsize=None, keep_dims=True, to_cpu=False, showprogress=False, return_tokens=False):
+        assert keep_dims in [True, False, 'flatten']
+
+        if bsize:
+            text_batches, reverse_indices = self.doc_tokenizer.tensorize(docs, bsize=bsize)
+
+            returned_text = []
+            if return_tokens:
+                returned_text = [text for batch in text_batches for text in batch[0]]
+                returned_text = [returned_text[idx] for idx in reverse_indices.tolist()]
+                returned_text = [returned_text]
+
+            keep_dims_ = 'return_mask' if keep_dims == 'flatten' else keep_dims
+            batches = [self.doc(input_ids, attention_mask, keep_dims=keep_dims_, to_cpu=to_cpu)
+                       for input_ids, attention_mask in tqdm(text_batches, disable=not showprogress)]
+
+            if keep_dims is True:
+                D = _stack_3D_tensors(batches)
+                return (D[reverse_indices], *returned_text)
+
+            elif keep_dims == 'flatten':
+                D, mask = [], []
+
+                for D_, mask_ in batches:
+                    D.append(D_)
+                    mask.append(mask_)
+
+                D, mask = torch.cat(D)[reverse_indices], torch.cat(mask)[reverse_indices]
+
+                doclens = mask.squeeze(-1).sum(-1).tolist()
+
+                D = D.view(-1, self.colbert_config.dim)
+                D = D[mask.bool().flatten()].cpu()
+
+                return (D, doclens, *returned_text)
+
+            assert keep_dims is False
+
+            D = [d for batch in batches for d in batch]
+            return ([D[idx] for idx in reverse_indices.tolist()], *returned_text)
+
+        input_ids, attention_mask = self.doc_tokenizer.tensorize(docs)
+        return self.doc(input_ids, attention_mask, keep_dims=keep_dims, to_cpu=to_cpu)
+
+    def lazy_rank(self, queries, docs):
+        Q = self.queryFromText(queries, bsize=128, to_cpu=True)
+        D = self.docFromText(docs, bsize=128, to_cpu=True)
+
+        assert False, "Implement scoring"
+
+    def score(self, Q, D, mask=None, lengths=None):
+        assert False, "Call colbert_score"
+        # EVENTUALLY: Just call the colbert_score function!
+
+        if lengths is not None:
+            assert mask is None, "don't supply both mask and lengths"
+
+            mask = torch.arange(D.size(1), device=self.device) + 1
+            mask = mask.unsqueeze(0) <= lengths.to(self.device).unsqueeze(-1)
+
+        scores = (D @ Q)
+        scores = scores if mask is None else scores * mask.unsqueeze(-1)
+        scores = scores.max(1)
+
+        return scores.values.sum(-1).cpu()
+
+
+def _stack_3D_tensors(groups):
+    bsize = sum([x.size(0) for x in groups])
+    maxlen = max([x.size(1) for x in groups])
+    hdim = groups[0].size(2)
+
+    output = torch.zeros(bsize, maxlen, hdim, device=groups[0].device, dtype=groups[0].dtype)
+
+    offset = 0
+    for x in groups:
+        endpos = offset + x.size(0)
+        output[offset:endpos, :x.size(1)] = x
+        offset = endpos
+
+    return output
+
+
+"""
+TODO:
+
+def tokenize_and_encode(checkpoint, passages):
+    embeddings, token_ids = checkpoint.docFromText(passages, bsize=128, keep_dims=False, showprogress=True, return_tokens=True)
+    tokens = [checkpoint.doc_tokenizer.tok.convert_ids_to_tokens(ids.tolist()) for ids in token_ids]
+    tokens = [tokens[:tokens.index('[PAD]') if '[PAD]' in tokens else -1] for tokens in tokens]
+    tokens = [[tok for tok in tokens if tok not in checkpoint.skiplist] for tokens in tokens]
+
+    return embeddings, tokens
+
+"""
diff --git a/colbert/modeling/colbert.py b/colbert/modeling/colbert.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c6c0945f18db8b69c2965b45f37986917d73284
--- /dev/null
+++ b/colbert/modeling/colbert.py
@@ -0,0 +1,202 @@
+from colbert.infra.config.config import ColBERTConfig
+from colbert.search.strided_tensor import StridedTensor
+from colbert.utils.utils import print_message, flatten
+from colbert.modeling.base_colbert import BaseColBERT
+
+import torch
+import string
+
+import os
+import pathlib
+from torch.utils.cpp_extension import load
+
+
+class ColBERT(BaseColBERT):
+    """
+        This class handles the basic encoding and scoring operations in ColBERT. It is used for training.
+    """
+
+    def __init__(self, name='bert-base-uncased', colbert_config=None):
+        super().__init__(name, colbert_config)
+        self.use_gpu = colbert_config.total_visible_gpus > 0
+
+        ColBERT.try_load_torch_extensions(self.use_gpu)
+
+        if self.colbert_config.mask_punctuation:
+            self.skiplist = {w: True
+                             for symbol in string.punctuation
+                             for w in [symbol, self.raw_tokenizer.encode(symbol, add_special_tokens=False)[0]]}
+
+    @classmethod
+    def try_load_torch_extensions(cls, use_gpu):
+        if hasattr(cls, "loaded_extensions") or use_gpu:
+            return
+
+        print_message(f"Loading segmented_maxsim_cpp extension (set COLBERT_LOAD_TORCH_EXTENSION_VERBOSE=True for more info)...")
+        segmented_maxsim_cpp = load(
+            name="segmented_maxsim_cpp",
+            sources=[
+                os.path.join(
+                    pathlib.Path(__file__).parent.resolve(), "segmented_maxsim.cpp"
+                ),
+            ],
+            extra_cflags=["-O3"],
+            verbose=os.getenv("COLBERT_LOAD_TORCH_EXTENSION_VERBOSE", "False") == "True",
+        )
+        cls.segmented_maxsim = segmented_maxsim_cpp.segmented_maxsim_cpp
+
+        cls.loaded_extensions = True
+
+    def forward(self, Q, D):
+        Q = self.query(*Q)
+        D, D_mask = self.doc(*D, keep_dims='return_mask')
+
+        # Repeat each query encoding for every corresponding document.
+        Q_duplicated = Q.repeat_interleave(self.colbert_config.nway, dim=0).contiguous()
+        scores = self.score(Q_duplicated, D, D_mask)
+
+        if self.colbert_config.use_ib_negatives:
+            ib_loss = self.compute_ib_loss(Q, D, D_mask)
+            return scores, ib_loss
+
+        return scores
+
+    def compute_ib_loss(self, Q, D, D_mask):
+        # TODO: Organize the code below! Quite messy.
+        scores = (D.unsqueeze(0) @ Q.permute(0, 2, 1).unsqueeze(1)).flatten(0, 1)  # query-major unsqueeze
+
+        scores = colbert_score_reduce(scores, D_mask.repeat(Q.size(0), 1, 1), self.colbert_config)
+
+        nway = self.colbert_config.nway
+        all_except_self_negatives = [list(range(qidx*D.size(0), qidx*D.size(0) + nway*qidx+1)) +
+                                     list(range(qidx*D.size(0) + nway * (qidx+1), qidx*D.size(0) + D.size(0)))
+                                     for qidx in range(Q.size(0))]
+
+        scores = scores[flatten(all_except_self_negatives)]
+        scores = scores.view(Q.size(0), -1)  # D.size(0) - self.colbert_config.nway + 1)
+
+        labels = torch.arange(0, Q.size(0), device=scores.device) * (self.colbert_config.nway)
+
+        return torch.nn.CrossEntropyLoss()(scores, labels)
+
+    def query(self, input_ids, attention_mask):
+        input_ids, attention_mask = input_ids.to(self.device), attention_mask.to(self.device)
+        Q = self.bert(input_ids, attention_mask=attention_mask)[0]
+        Q = self.linear(Q)
+
+        mask = torch.tensor(self.mask(input_ids, skiplist=[]), device=self.device).unsqueeze(2).float()
+        Q = Q * mask
+
+        return torch.nn.functional.normalize(Q, p=2, dim=2)
+
+    def doc(self, input_ids, attention_mask, keep_dims=True):
+        assert keep_dims in [True, False, 'return_mask']
+
+        input_ids, attention_mask = input_ids.to(self.device), attention_mask.to(self.device)
+        D = self.bert(input_ids, attention_mask=attention_mask)[0]
+        D = self.linear(D)
+
+        mask = torch.tensor(self.mask(input_ids, skiplist=self.skiplist), device=self.device).unsqueeze(2).float()
+        D = D * mask
+
+        D = torch.nn.functional.normalize(D, p=2, dim=2)
+        if self.use_gpu:
+            D = D.half()
+
+        if keep_dims is False:
+            D, mask = D.cpu(), mask.bool().cpu().squeeze(-1)
+            D = [d[mask[idx]] for idx, d in enumerate(D)]
+
+        elif keep_dims == 'return_mask':
+            return D, mask.bool()
+
+        return D
+
+    def score(self, Q, D_padded, D_mask):
+        # assert self.colbert_config.similarity == 'cosine'
+
+        if self.colbert_config.similarity == 'l2':
+            assert self.colbert_config.interaction == 'colbert'
+            return (-1.0 * ((Q.unsqueeze(2) - D_padded.unsqueeze(1))**2).sum(-1)).max(-1).values.sum(-1)
+
+        return colbert_score(Q, D_padded, D_mask, config=self.colbert_config)
+
+    def mask(self, input_ids, skiplist):
+        mask = [[(x not in skiplist) and (x != 0) for x in d] for d in input_ids.cpu().tolist()]
+        return mask
+
+
+# TODO: In Query/DocTokenizer, use colbert.raw_tokenizer
+
+# TODO: The masking below might also be applicable in the kNN part
+def colbert_score_reduce(scores_padded, D_mask, config: ColBERTConfig):
+    D_padding = ~D_mask.view(scores_padded.size(0), scores_padded.size(1)).bool()
+    scores_padded[D_padding] = -9999
+    scores = scores_padded.max(1).values
+
+    assert config.interaction in ['colbert', 'flipr'], config.interaction
+
+    if config.interaction == 'flipr':
+        assert config.query_maxlen == 64, ("for now", config)
+        # assert scores.size(1) == config.query_maxlen, scores.size()
+
+        K1 = config.query_maxlen // 2
+        K2 = 8
+
+        A = scores[:, :config.query_maxlen].topk(K1, dim=-1).values.sum(-1)
+        B = 0
+
+        if K2 <= scores.size(1) - config.query_maxlen:
+            B = scores[:, config.query_maxlen:].topk(K2, dim=-1).values.sum(1)
+
+        return A + B
+
+    return scores.sum(-1)
+
+
+# TODO: Wherever this is called, pass `config=`
+def colbert_score(Q, D_padded, D_mask, config=ColBERTConfig()):
+    """
+        Supply sizes Q = (1 | num_docs, *, dim) and D = (num_docs, *, dim).
+        If Q.size(0) is 1, the matrix will be compared with all passages.
+        Otherwise, each query matrix will be compared against the *aligned* passage.
+
+        EVENTUALLY: Consider masking with -inf for the maxsim (or enforcing a ReLU).
+    """
+
+    use_gpu = config.total_visible_gpus > 0
+    if use_gpu:
+        Q, D_padded, D_mask = Q.cuda(), D_padded.cuda(), D_mask.cuda()
+
+    assert Q.dim() == 3, Q.size()
+    assert D_padded.dim() == 3, D_padded.size()
+    assert Q.size(0) in [1, D_padded.size(0)]
+
+    scores = D_padded @ Q.to(dtype=D_padded.dtype).permute(0, 2, 1)
+
+    return colbert_score_reduce(scores, D_mask, config)
+
+
+def colbert_score_packed(Q, D_packed, D_lengths, config=ColBERTConfig()):
+    """
+        Works with a single query only.
+    """
+
+    use_gpu = config.total_visible_gpus > 0
+
+    if use_gpu:
+        Q, D_packed, D_lengths = Q.cuda(), D_packed.cuda(), D_lengths.cuda()
+
+    Q = Q.squeeze(0)
+
+    assert Q.dim() == 2, Q.size()
+    assert D_packed.dim() == 2, D_packed.size()
+
+    scores = D_packed @ Q.to(dtype=D_packed.dtype).T
+
+    if use_gpu or config.interaction == "flipr":
+        scores_padded, scores_mask = StridedTensor(scores, D_lengths, use_gpu=use_gpu).as_padded_tensor()
+
+        return colbert_score_reduce(scores_padded, scores_mask, config)
+    else:
+        return ColBERT.segmented_maxsim(scores, D_lengths)
diff --git a/colbert/modeling/hf_colbert.py b/colbert/modeling/hf_colbert.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebb1141812a161d9962ba109ddf2576540af1f3a
--- /dev/null
+++ b/colbert/modeling/hf_colbert.py
@@ -0,0 +1,69 @@
+import torch.nn as nn
+
+from transformers import BertPreTrainedModel, BertModel, AutoTokenizer
+from colbert.utils.utils import torch_load_dnn
+
+
+class HF_ColBERT(BertPreTrainedModel):
+    """
+        Shallow wrapper around HuggingFace transformers. All new parameters should be defined at this level.
+        
+        This makes sure `{from,save}_pretrained` and `init_weights` are applied to new parameters correctly.
+    """
+    _keys_to_ignore_on_load_unexpected = [r"cls"]
+
+    def __init__(self, config, colbert_config):
+        super().__init__(config)
+
+        self.dim = colbert_config.dim
+        self.bert = BertModel(config)
+        self.linear = nn.Linear(config.hidden_size, colbert_config.dim, bias=False)
+
+        # if colbert_config.relu:
+        #     self.score_scaler = nn.Linear(1, 1)
+
+        self.init_weights()
+
+        # if colbert_config.relu:
+        #     self.score_scaler.weight.data.fill_(1.0)
+        #     self.score_scaler.bias.data.fill_(-8.0)
+
+    @classmethod
+    def from_pretrained(cls, name_or_path, colbert_config):
+        if name_or_path.endswith('.dnn'):
+            dnn = torch_load_dnn(name_or_path)
+            base = dnn.get('arguments', {}).get('model', 'bert-base-uncased')
+
+            obj = super().from_pretrained(base, state_dict=dnn['model_state_dict'], colbert_config=colbert_config)
+            obj.base = base
+
+            return obj
+
+        obj = super().from_pretrained(name_or_path, colbert_config=colbert_config)
+        obj.base = name_or_path
+
+        return obj
+
+    @staticmethod
+    def raw_tokenizer_from_pretrained(name_or_path):
+        if name_or_path.endswith('.dnn'):
+            dnn = torch_load_dnn(name_or_path)
+            base = dnn.get('arguments', {}).get('model', 'bert-base-uncased')
+
+            obj = AutoTokenizer.from_pretrained(base)
+            obj.base = base
+
+            return obj
+
+        obj = AutoTokenizer.from_pretrained(name_or_path)
+        obj.base = name_or_path
+
+        return obj
+
+"""
+TODO: It's easy to write a class generator that takes "name_or_path" and loads AutoConfig to check the Architecture's
+      name, finds that name's *PreTrainedModel and *Model in dir(transformers), and then basically repeats the above.
+
+      It's easy for the BaseColBERT class to instantiate things from there.
+"""
+
diff --git a/colbert/modeling/reranker/__pycache__/electra.cpython-39.pyc b/colbert/modeling/reranker/__pycache__/electra.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..538509c75e23be427fd46e6086013a64f837dc11
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diff --git a/colbert/modeling/reranker/__pycache__/tokenizer.cpython-39.pyc b/colbert/modeling/reranker/__pycache__/tokenizer.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5d263c89ae5fe43fed51278bfb26441e1cb7d807
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diff --git a/colbert/modeling/reranker/electra.py b/colbert/modeling/reranker/electra.py
new file mode 100644
index 0000000000000000000000000000000000000000..e36a0d84badba72680490124d47cb591cb5b8f5a
--- /dev/null
+++ b/colbert/modeling/reranker/electra.py
@@ -0,0 +1,35 @@
+import torch.nn as nn
+
+from transformers import ElectraPreTrainedModel, ElectraModel, AutoTokenizer
+
+class ElectraReranker(ElectraPreTrainedModel):
+    """
+        Shallow wrapper around HuggingFace transformers. All new parameters should be defined at this level.
+        
+        This makes sure `{from,save}_pretrained` and `init_weights` are applied to new parameters correctly.
+    """
+    _keys_to_ignore_on_load_unexpected = [r"cls"]
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.electra = ElectraModel(config)
+        self.linear = nn.Linear(config.hidden_size, 1)
+        self.raw_tokenizer = AutoTokenizer.from_pretrained('google/electra-large-discriminator')
+
+        self.init_weights()
+
+    def forward(self, encoding):
+        outputs = self.electra(encoding.input_ids,
+                               attention_mask=encoding.attention_mask,
+                               token_type_ids=encoding.token_type_ids)[0]
+
+        scores = self.linear(outputs[:, 0]).squeeze(-1)
+
+        return scores
+    
+    def save(self, path):
+        assert not path.endswith('.dnn'), f"{path}: We reserve *.dnn names for the deprecated checkpoint format."
+
+        self.save_pretrained(path)
+        self.raw_tokenizer.save_pretrained(path)
\ No newline at end of file
diff --git a/colbert/modeling/reranker/tokenizer.py b/colbert/modeling/reranker/tokenizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8adf0807d073413d87001d11be30e91fc52f646
--- /dev/null
+++ b/colbert/modeling/reranker/tokenizer.py
@@ -0,0 +1,15 @@
+from transformers import AutoTokenizer
+
+class RerankerTokenizer():
+    def __init__(self, total_maxlen, base):
+        self.total_maxlen = total_maxlen
+        self.tok = AutoTokenizer.from_pretrained(base)
+
+    def tensorize(self, questions, passages):
+        assert type(questions) in [list, tuple], type(questions)
+        assert type(passages) in [list, tuple], type(passages)
+
+        encoding = self.tok(questions, passages, padding='longest', truncation='longest_first',
+                            return_tensors='pt', max_length=self.total_maxlen, add_special_tokens=True)
+
+        return encoding
diff --git a/colbert/modeling/segmented_maxsim.cpp b/colbert/modeling/segmented_maxsim.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..0bdac29d241e1b882eb1cf2317ff9da8f137c0c0
--- /dev/null
+++ b/colbert/modeling/segmented_maxsim.cpp
@@ -0,0 +1,97 @@
+#include <pthread.h>
+#include <torch/extension.h>
+
+#include <algorithm>
+#include <numeric>
+
+typedef struct {
+    int tid;
+    int nthreads;
+
+    int ndocs;
+    int ndoc_vectors;
+    int nquery_vectors;
+
+    int64_t* lengths;
+    float* scores;
+    int64_t* offsets;
+
+    float* max_scores;
+} max_args_t;
+
+void* max(void* args) {
+    max_args_t* max_args = (max_args_t*)args;
+
+    int ndocs_per_thread =
+        std::ceil(((float)max_args->ndocs) / max_args->nthreads);
+    int start = max_args->tid * ndocs_per_thread;
+    int end = std::min((max_args->tid + 1) * ndocs_per_thread, max_args->ndocs);
+
+    auto max_scores_offset =
+        max_args->max_scores + (start * max_args->nquery_vectors);
+    auto scores_offset =
+        max_args->scores + (max_args->offsets[start] * max_args->nquery_vectors);
+
+    for (int i = start; i < end; i++) {
+        for (int j = 0; j < max_args->lengths[i]; j++) {
+            std::transform(max_scores_offset,
+                           max_scores_offset + max_args->nquery_vectors,
+                           scores_offset, max_scores_offset,
+                           [](float a, float b) { return std::max(a, b); });
+            scores_offset += max_args->nquery_vectors;
+        }
+        max_scores_offset += max_args->nquery_vectors;
+    }
+
+    return NULL;
+}
+
+torch::Tensor segmented_maxsim(const torch::Tensor scores,
+                               const torch::Tensor lengths) {
+    auto lengths_a = lengths.data_ptr<int64_t>();
+    auto scores_a = scores.data_ptr<float>();
+    auto ndocs = lengths.size(0);
+    auto ndoc_vectors = scores.size(0);
+    auto nquery_vectors = scores.size(1);
+    auto nthreads = at::get_num_threads();
+
+    torch::Tensor max_scores =
+        torch::zeros({ndocs, nquery_vectors}, scores.options());
+
+    int64_t offsets[ndocs + 1];
+    offsets[0] = 0;
+    std::partial_sum(lengths_a, lengths_a + ndocs, offsets + 1);
+
+    pthread_t threads[nthreads];
+    max_args_t args[nthreads];
+
+    for (int i = 0; i < nthreads; i++) {
+        args[i].tid = i;
+        args[i].nthreads = nthreads;
+
+        args[i].ndocs = ndocs;
+        args[i].ndoc_vectors = ndoc_vectors;
+        args[i].nquery_vectors = nquery_vectors;
+
+        args[i].lengths = lengths_a;
+        args[i].scores = scores_a;
+        args[i].offsets = offsets;
+
+        args[i].max_scores = max_scores.data_ptr<float>();
+
+        int rc = pthread_create(&threads[i], NULL, max, (void*)&args[i]);
+        if (rc) {
+            fprintf(stderr, "Unable to create thread %d: %d\n", i, rc);
+        }
+    }
+
+    for (int i = 0; i < nthreads; i++) {
+        pthread_join(threads[i], NULL);
+    }
+
+    return max_scores.sum(1);
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("segmented_maxsim_cpp", &segmented_maxsim, "Segmented MaxSim");
+}
diff --git a/colbert/modeling/tokenization/__init__.py b/colbert/modeling/tokenization/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd590eb5a1dcf2248ebd4e6db47df94761dfaf0f
--- /dev/null
+++ b/colbert/modeling/tokenization/__init__.py
@@ -0,0 +1,3 @@
+from colbert.modeling.tokenization.query_tokenization import *
+from colbert.modeling.tokenization.doc_tokenization import *
+from colbert.modeling.tokenization.utils import tensorize_triples
diff --git a/colbert/modeling/tokenization/__pycache__/__init__.cpython-39.pyc b/colbert/modeling/tokenization/__pycache__/__init__.cpython-39.pyc
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new file mode 100644
index 0000000000000000000000000000000000000000..fd60ee10c13653e8393773b6e52e21f9d1c13a48
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diff --git a/colbert/modeling/tokenization/__pycache__/utils.cpython-39.pyc b/colbert/modeling/tokenization/__pycache__/utils.cpython-39.pyc
new file mode 100644
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diff --git a/colbert/modeling/tokenization/doc_tokenization.py b/colbert/modeling/tokenization/doc_tokenization.py
new file mode 100644
index 0000000000000000000000000000000000000000..43d56c2b1e2dfd0f7c78672f47b95f8522438c0e
--- /dev/null
+++ b/colbert/modeling/tokenization/doc_tokenization.py
@@ -0,0 +1,71 @@
+import torch
+
+
+# from transformers import BertTokenizerFast
+
+from colbert.modeling.hf_colbert import HF_ColBERT
+from colbert.infra import ColBERTConfig
+from colbert.modeling.tokenization.utils import _split_into_batches, _sort_by_length
+
+
+class DocTokenizer():
+    def __init__(self, config: ColBERTConfig):
+        self.tok = HF_ColBERT.raw_tokenizer_from_pretrained(config.checkpoint)
+
+        self.config = config
+        self.doc_maxlen = config.doc_maxlen
+
+        self.D_marker_token, self.D_marker_token_id = '[D]', self.tok.convert_tokens_to_ids('[unused1]')
+        self.cls_token, self.cls_token_id = self.tok.cls_token, self.tok.cls_token_id
+        self.sep_token, self.sep_token_id = self.tok.sep_token, self.tok.sep_token_id
+
+
+
+       # assert self.D_marker_token_id == 2
+
+    def tokenize(self, batch_text, add_special_tokens=False):
+        assert type(batch_text) in [list, tuple], (type(batch_text))
+
+        tokens = [self.tok.tokenize(x, add_special_tokens=False) for x in batch_text]
+
+        if not add_special_tokens:
+            return tokens
+
+        prefix, suffix = [self.cls_token, self.D_marker_token], [self.sep_token]
+        tokens = [prefix + lst + suffix for lst in tokens]
+
+        return tokens
+
+    def encode(self, batch_text, add_special_tokens=False):
+        assert type(batch_text) in [list, tuple], (type(batch_text))
+
+        ids = self.tok(batch_text, add_special_tokens=False)['input_ids']
+
+        if not add_special_tokens:
+            return ids
+
+        prefix, suffix = [self.cls_token_id, self.D_marker_token_id], [self.sep_token_id]
+        ids = [prefix + lst + suffix for lst in ids]
+
+        return ids
+
+    def tensorize(self, batch_text, bsize=None):
+        assert type(batch_text) in [list, tuple], (type(batch_text))
+
+        # add placehold for the [D] marker
+        batch_text = ['. ' + x for x in batch_text]
+
+        obj = self.tok(batch_text, padding='longest', truncation='longest_first',
+                       return_tensors='pt', max_length=self.doc_maxlen)
+
+        ids, mask = obj['input_ids'], obj['attention_mask']
+
+        # postprocess for the [D] marker
+        ids[:, 1] = self.D_marker_token_id
+
+        if bsize:
+            ids, mask, reverse_indices = _sort_by_length(ids, mask, bsize)
+            batches = _split_into_batches(ids, mask, bsize)
+            return batches, reverse_indices
+
+        return ids, mask
diff --git a/colbert/modeling/tokenization/query_tokenization.py b/colbert/modeling/tokenization/query_tokenization.py
new file mode 100644
index 0000000000000000000000000000000000000000..a6d7597a4785566d3766f26c4faf83c2269087a9
--- /dev/null
+++ b/colbert/modeling/tokenization/query_tokenization.py
@@ -0,0 +1,97 @@
+import torch
+
+from colbert.modeling.hf_colbert import HF_ColBERT
+from colbert.infra import ColBERTConfig
+from colbert.modeling.tokenization.utils import _split_into_batches
+from colbert.utils.utils import batch
+
+
+class QueryTokenizer():
+    def __init__(self, config: ColBERTConfig):
+        self.tok = HF_ColBERT.raw_tokenizer_from_pretrained(config.checkpoint)
+
+        self.config = config
+        self.query_maxlen = config.query_maxlen
+        self.background_maxlen = 512 - self.query_maxlen + 1  # FIXME: Make this configurable
+
+        self.Q_marker_token, self.Q_marker_token_id = '[Q]', self.tok.convert_tokens_to_ids('[unused0]')
+        self.cls_token, self.cls_token_id = self.tok.cls_token, self.tok.cls_token_id
+        self.sep_token, self.sep_token_id = self.tok.sep_token, self.tok.sep_token_id
+        self.mask_token, self.mask_token_id = self.tok.mask_token, self.tok.mask_token_id
+
+#        assert self.Q_marker_token_id == 1 and self.mask_token_id == 103
+        self.used = False
+
+    def tokenize(self, batch_text, add_special_tokens=False):
+        assert type(batch_text) in [list, tuple], (type(batch_text))
+
+        tokens = [self.tok.tokenize(x, add_special_tokens=False) for x in batch_text]
+
+        if not add_special_tokens:
+            return tokens
+
+        prefix, suffix = [self.cls_token, self.Q_marker_token], [self.sep_token]
+        tokens = [prefix + lst + suffix + [self.mask_token] * (self.query_maxlen - (len(lst)+3)) for lst in tokens]
+
+        return tokens
+
+    def encode(self, batch_text, add_special_tokens=False):
+        assert type(batch_text) in [list, tuple], (type(batch_text))
+
+        ids = self.tok(batch_text, add_special_tokens=False)['input_ids']
+
+        if not add_special_tokens:
+            return ids
+
+        prefix, suffix = [self.cls_token_id, self.Q_marker_token_id], [self.sep_token_id]
+        ids = [prefix + lst + suffix + [self.mask_token_id] * (self.query_maxlen - (len(lst)+3)) for lst in ids]
+
+        return ids
+
+    def tensorize(self, batch_text, bsize=None, context=None):
+        assert type(batch_text) in [list, tuple], (type(batch_text))
+
+        # add placehold for the [Q] marker
+        batch_text = ['. ' + x for x in batch_text]
+
+        obj = self.tok(batch_text, padding='max_length', truncation=True,
+                       return_tensors='pt', max_length=self.query_maxlen)
+
+        ids, mask = obj['input_ids'], obj['attention_mask']
+
+        # postprocess for the [Q] marker and the [MASK] augmentation
+        ids[:, 1] = self.Q_marker_token_id
+        ids[ids == 0] = self.mask_token_id
+
+        if context is not None:
+            assert len(context) == len(batch_text), (len(context), len(batch_text))
+
+            obj_2 = self.tok(context, padding='longest', truncation=True,
+                            return_tensors='pt', max_length=self.background_maxlen)
+
+            ids_2, mask_2 = obj_2['input_ids'][:, 1:], obj_2['attention_mask'][:, 1:]  # Skip the first [SEP]
+
+            ids = torch.cat((ids, ids_2), dim=-1)
+            mask = torch.cat((mask, mask_2), dim=-1)
+
+        if self.config.attend_to_mask_tokens:
+            mask[ids == self.mask_token_id] = 1
+            assert mask.sum().item() == mask.size(0) * mask.size(1), mask
+
+        if bsize:
+            batches = _split_into_batches(ids, mask, bsize)
+            return batches
+        
+        if self.used is False:
+            self.used = True
+
+            firstbg = (context is None) or context[0]
+
+            print()
+            print("#> QueryTokenizer.tensorize(batch_text[0], batch_background[0], bsize) ==")
+            print(f"#> Input: {batch_text[0]}, \t\t {firstbg}, \t\t {bsize}")
+            print(f"#> Output IDs: {ids[0].size()}, {ids[0]}")
+            print(f"#> Output Mask: {mask[0].size()}, {mask[0]}")
+            print()
+
+        return ids, mask
diff --git a/colbert/modeling/tokenization/utils.py b/colbert/modeling/tokenization/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..914a4f59d18b62efae9a7dc4c9f661a03c156de3
--- /dev/null
+++ b/colbert/modeling/tokenization/utils.py
@@ -0,0 +1,63 @@
+import torch
+
+
+def tensorize_triples(query_tokenizer, doc_tokenizer, queries, passages, scores, bsize, nway):
+    # assert len(passages) == len(scores) == bsize * nway
+    # assert bsize is None or len(queries) % bsize == 0
+
+    # N = len(queries)
+    Q_ids, Q_mask = query_tokenizer.tensorize(queries)
+    D_ids, D_mask = doc_tokenizer.tensorize(passages)
+    # D_ids, D_mask = D_ids.view(2, N, -1), D_mask.view(2, N, -1)
+
+    # # Compute max among {length of i^th positive, length of i^th negative} for i \in N
+    # maxlens = D_mask.sum(-1).max(0).values
+
+    # # Sort by maxlens
+    # indices = maxlens.sort().indices
+    # Q_ids, Q_mask = Q_ids[indices], Q_mask[indices]
+    # D_ids, D_mask = D_ids[:, indices], D_mask[:, indices]
+
+    # (positive_ids, negative_ids), (positive_mask, negative_mask) = D_ids, D_mask
+
+    query_batches = _split_into_batches(Q_ids, Q_mask, bsize)
+    doc_batches = _split_into_batches(D_ids, D_mask, bsize * nway)
+    # positive_batches = _split_into_batches(positive_ids, positive_mask, bsize)
+    # negative_batches = _split_into_batches(negative_ids, negative_mask, bsize)
+
+    if len(scores):
+        score_batches = _split_into_batches2(scores, bsize * nway)
+    else:
+        score_batches = [[] for _ in doc_batches]
+
+    batches = []
+    for Q, D, S in zip(query_batches, doc_batches, score_batches):
+        batches.append((Q, D, S))
+
+    return batches
+
+
+def _sort_by_length(ids, mask, bsize):
+    if ids.size(0) <= bsize:
+        return ids, mask, torch.arange(ids.size(0))
+
+    indices = mask.sum(-1).sort().indices
+    reverse_indices = indices.sort().indices
+
+    return ids[indices], mask[indices], reverse_indices
+
+
+def _split_into_batches(ids, mask, bsize):
+    batches = []
+    for offset in range(0, ids.size(0), bsize):
+        batches.append((ids[offset:offset+bsize], mask[offset:offset+bsize]))
+
+    return batches
+
+
+def _split_into_batches2(scores, bsize):
+    batches = []
+    for offset in range(0, len(scores), bsize):
+        batches.append(scores[offset:offset+bsize])
+
+    return batches
diff --git a/colbert/parameters.py b/colbert/parameters.py
new file mode 100644
index 0000000000000000000000000000000000000000..4802d7a05c2bb3fa462e7baa64373737a04e7c83
--- /dev/null
+++ b/colbert/parameters.py
@@ -0,0 +1,12 @@
+import torch
+
+DEVICE = torch.device("cuda")
+
+SAVED_CHECKPOINTS = [32*1000, 100*1000, 150*1000, 200*1000, 250*1000, 300*1000, 400*1000]
+SAVED_CHECKPOINTS += [10*1000, 20*1000, 30*1000, 40*1000, 50*1000, 60*1000, 70*1000, 80*1000, 90*1000]
+SAVED_CHECKPOINTS += [25*1000, 50*1000, 75*1000]
+
+SAVED_CHECKPOINTS = set(SAVED_CHECKPOINTS)
+
+
+# TODO:  final_ckpt    2k, 5k, 10k   20k, 50k, 100k  150k  200k, 500k, 1M       2M, 5M, 10M
\ No newline at end of file
diff --git a/colbert/ranking/__init__.py b/colbert/ranking/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
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diff --git a/colbert/search/candidate_generation.py b/colbert/search/candidate_generation.py
new file mode 100644
index 0000000000000000000000000000000000000000..96a1840f19640892725d86fafdd434675a75fdd7
--- /dev/null
+++ b/colbert/search/candidate_generation.py
@@ -0,0 +1,64 @@
+import torch
+
+from colbert.search.strided_tensor import StridedTensor
+from .strided_tensor_core import _create_mask, _create_view
+
+
+class CandidateGeneration:
+
+    def __init__(self, use_gpu=True):
+        self.use_gpu = use_gpu
+
+    def get_cells(self, Q, ncells):
+        scores = (self.codec.centroids @ Q.T)
+        if ncells == 1:
+            cells = scores.argmax(dim=0, keepdim=True).permute(1, 0)
+        else:
+            cells = scores.topk(ncells, dim=0, sorted=False).indices.permute(1, 0)  # (32, ncells)
+        cells = cells.flatten().contiguous()  # (32 * ncells,)
+        cells = cells.unique(sorted=False)
+        return cells, scores
+
+    def generate_candidate_eids(self, Q, ncells):
+        cells, scores = self.get_cells(Q, ncells)
+
+        eids, cell_lengths = self.ivf.lookup(cells)  # eids = (packedlen,)  lengths = (32 * ncells,)
+        eids = eids.long()
+        if self.use_gpu:
+            eids = eids.cuda()
+        return eids, scores
+
+    def generate_candidate_pids(self, Q, ncells):
+        cells, scores = self.get_cells(Q, ncells)
+
+        pids, cell_lengths = self.ivf.lookup(cells)
+        if self.use_gpu:
+            pids = pids.cuda()
+        return pids, scores
+
+    def generate_candidate_scores(self, Q, eids):
+        E = self.lookup_eids(eids)
+        if self.use_gpu:
+            E = E.cuda()
+        return (Q.unsqueeze(0) @ E.unsqueeze(2)).squeeze(-1).T
+
+    def generate_candidates(self, config, Q):
+        ncells = config.ncells
+
+        assert isinstance(self.ivf, StridedTensor)
+
+        Q = Q.squeeze(0)
+        if self.use_gpu:
+            Q = Q.cuda().half()
+        assert Q.dim() == 2
+
+        pids, centroid_scores = self.generate_candidate_pids(Q, ncells)
+
+        sorter = pids.sort()
+        pids = sorter.values
+
+        pids, pids_counts = torch.unique_consecutive(pids, return_counts=True)
+        if self.use_gpu:
+            pids, pids_counts = pids.cuda(), pids_counts.cuda()
+
+        return pids, centroid_scores
diff --git a/colbert/search/decompress_residuals.cpp b/colbert/search/decompress_residuals.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..fbca4f8024f83a92b7a28d7a79cb053aaf403bb2
--- /dev/null
+++ b/colbert/search/decompress_residuals.cpp
@@ -0,0 +1,160 @@
+#include <pthread.h>
+#include <torch/extension.h>
+
+typedef struct decompress_args {
+    int tid;
+    int nthreads;
+
+    int npids;
+    int dim;
+    int packed_dim;
+    int npacked_vals_per_byte;
+
+    int* pids;
+    int64_t* lengths;
+    int64_t* offsets;
+    float* bucket_weights;
+    uint8_t* reversed_bit_map;
+    uint8_t* bucket_weight_combinations;
+    uint8_t* binary_residuals;
+    int* codes;
+    float* centroids;
+    int64_t* cumulative_lengths;
+
+    float* output;
+} decompress_args_t;
+
+void* decompress(void* args) {
+    decompress_args_t* decompress_args = (decompress_args_t*)args;
+
+    int npids_per_thread = (int)std::ceil(((float)decompress_args->npids) /
+                                          decompress_args->nthreads);
+    int start = decompress_args->tid * npids_per_thread;
+    int end = std::min((decompress_args->tid + 1) * npids_per_thread,
+                       decompress_args->npids);
+
+    // Iterate over all documents
+    for (int i = start; i < end; i++) {
+        int pid = decompress_args->pids[i];
+
+        // Offset into packed list of token vectors for the given document
+        int64_t offset = decompress_args->offsets[pid];
+
+        // For each document, iterate over all token vectors
+        for (int j = 0; j < decompress_args->lengths[pid]; j++) {
+            const int code = decompress_args->codes[offset + j];
+
+            // For each token vector, iterate over the packed (8-bit) residual
+            // values
+            for (int k = 0; k < decompress_args->packed_dim; k++) {
+                uint8_t x =
+                    decompress_args->binary_residuals
+                        [(offset + j) * decompress_args->packed_dim + k];
+                x = decompress_args->reversed_bit_map[x];
+
+                // For each packed residual value, iterate over the bucket
+                // weight indices. If we use n-bit compression, that means there
+                // will be (8 / n) indices per packed value.
+                for (int l = 0; l < decompress_args->npacked_vals_per_byte;
+                     l++) {
+                    const int output_dim_idx =
+                        k * decompress_args->npacked_vals_per_byte + l;
+                    const int bucket_weight_idx =
+                        decompress_args->bucket_weight_combinations
+                            [x * decompress_args->npacked_vals_per_byte + l];
+                    decompress_args
+                        ->output[(decompress_args->cumulative_lengths[i] + j) *
+                                     decompress_args->dim +
+                                 output_dim_idx] =
+                        decompress_args->bucket_weights[bucket_weight_idx] +
+                        decompress_args->centroids[code * decompress_args->dim +
+                                                   output_dim_idx];
+                }
+            }
+        }
+    }
+
+    return NULL;
+}
+
+torch::Tensor decompress_residuals(
+    const torch::Tensor pids, const torch::Tensor lengths,
+    const torch::Tensor offsets, const torch::Tensor bucket_weights,
+    const torch::Tensor reversed_bit_map,
+    const torch::Tensor bucket_weight_combinations,
+    const torch::Tensor binary_residuals, const torch::Tensor codes,
+    const torch::Tensor centroids, const int dim, const int nbits) {
+    const int npacked_vals_per_byte = (8 / nbits);
+    const int packed_dim = (int)(dim / npacked_vals_per_byte);
+
+    int npids = pids.size(0);
+    int* pids_a = pids.data_ptr<int>();
+    int64_t* lengths_a = lengths.data_ptr<int64_t>();
+    int64_t* offsets_a = offsets.data_ptr<int64_t>();
+    float* bucket_weights_a = bucket_weights.data_ptr<float>();
+    uint8_t* reversed_bit_map_a = reversed_bit_map.data_ptr<uint8_t>();
+    uint8_t* bucket_weight_combinations_a =
+        bucket_weight_combinations.data_ptr<uint8_t>();
+    uint8_t* binary_residuals_a = binary_residuals.data_ptr<uint8_t>();
+    int* codes_a = codes.data_ptr<int>();
+    float* centroids_a = centroids.data_ptr<float>();
+
+    int64_t cumulative_lengths[npids + 1];
+    int noutputs = 0;
+    cumulative_lengths[0] = 0;
+    for (int i = 0; i < npids; i++) {
+        noutputs += lengths_a[pids_a[i]];
+        cumulative_lengths[i + 1] =
+            cumulative_lengths[i] + lengths_a[pids_a[i]];
+    }
+
+    auto options =
+        torch::TensorOptions().dtype(torch::kFloat32).requires_grad(false);
+    torch::Tensor output = torch::zeros({noutputs, dim}, options);
+    float* output_a = output.data_ptr<float>();
+
+    auto nthreads = at::get_num_threads();
+
+    pthread_t threads[nthreads];
+    decompress_args_t args[nthreads];
+
+    for (int i = 0; i < nthreads; i++) {
+        args[i].tid = i;
+        args[i].nthreads = nthreads;
+
+        args[i].npids = npids;
+        args[i].dim = dim;
+        args[i].packed_dim = packed_dim;
+        args[i].npacked_vals_per_byte = npacked_vals_per_byte;
+
+        args[i].pids = pids_a;
+        args[i].lengths = lengths_a;
+        args[i].offsets = offsets_a;
+        args[i].bucket_weights = bucket_weights_a;
+        args[i].reversed_bit_map = reversed_bit_map_a;
+        args[i].bucket_weight_combinations = bucket_weight_combinations_a;
+        args[i].binary_residuals = binary_residuals_a;
+        args[i].codes = codes_a;
+        args[i].centroids = centroids_a;
+        args[i].cumulative_lengths = cumulative_lengths;
+
+        args[i].output = output_a;
+
+        int rc = pthread_create(&threads[i], NULL, decompress, (void*)&args[i]);
+        if (rc) {
+            fprintf(stderr, "Unable to create thread %d: %d\n", i, rc);
+            std::exit(1);
+        }
+    }
+
+    for (int i = 0; i < nthreads; i++) {
+        pthread_join(threads[i], NULL);
+    }
+
+    return output;
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("decompress_residuals_cpp", &decompress_residuals,
+          "Decompress residuals");
+}
diff --git a/colbert/search/filter_pids.cpp b/colbert/search/filter_pids.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..6f9b0909910c7bfe6d418e50a914e0130a6bb4d0
--- /dev/null
+++ b/colbert/search/filter_pids.cpp
@@ -0,0 +1,169 @@
+#include <pthread.h>
+#include <torch/extension.h>
+
+#include <algorithm>
+#include <chrono>
+#include <numeric>
+#include <utility>
+
+typedef struct maxsim_args {
+    int tid;
+    int nthreads;
+
+    int ncentroids;
+    int nquery_vectors;
+    int npids;
+
+    int* pids;
+    float* centroid_scores;
+    int* codes;
+    int64_t* doclens;
+    int64_t* offsets;
+    bool* idx;
+
+    std::priority_queue<std::pair<float, int>> approx_scores;
+} maxsim_args_t;
+
+void* maxsim(void* args) {
+    maxsim_args_t* maxsim_args = (maxsim_args_t*)args;
+
+    float per_doc_approx_scores[maxsim_args->nquery_vectors];
+    for (int k = 0; k < maxsim_args->nquery_vectors; k++) {
+        per_doc_approx_scores[k] = -9999;
+    }
+
+    int ndocs_per_thread =
+        (int)std::ceil(((float)maxsim_args->npids) / maxsim_args->nthreads);
+    int start = maxsim_args->tid * ndocs_per_thread;
+    int end =
+        std::min((maxsim_args->tid + 1) * ndocs_per_thread, maxsim_args->npids);
+
+    std::unordered_set<int> seen_codes;
+
+    for (int i = start; i < end; i++) {
+        auto pid = maxsim_args->pids[i];
+        for (int j = 0; j < maxsim_args->doclens[pid]; j++) {
+            auto code = maxsim_args->codes[maxsim_args->offsets[pid] + j];
+            assert(code < maxsim_args->ncentroids);
+            if (maxsim_args->idx[code] &&
+                seen_codes.find(code) == seen_codes.end()) {
+                for (int k = 0; k < maxsim_args->nquery_vectors; k++) {
+                    per_doc_approx_scores[k] =
+                        std::max(per_doc_approx_scores[k],
+                                 maxsim_args->centroid_scores
+                                     [code * maxsim_args->nquery_vectors + k]);
+                }
+                seen_codes.insert(code);
+            }
+        }
+        float score = 0;
+        for (int k = 0; k < maxsim_args->nquery_vectors; k++) {
+            score += per_doc_approx_scores[k];
+            per_doc_approx_scores[k] = -9999;
+        }
+        maxsim_args->approx_scores.push(std::make_pair(score, pid));
+        seen_codes.clear();
+    }
+
+    return NULL;
+}
+
+void filter_pids_helper(int ncentroids, int nquery_vectors, int npids,
+                        int* pids, float* centroid_scores, int* codes,
+                        int64_t* doclens, int64_t* offsets, bool* idx,
+                        int nfiltered_docs, int* filtered_pids) {
+    auto nthreads = at::get_num_threads();
+
+    pthread_t threads[nthreads];
+    maxsim_args_t args[nthreads];
+
+    for (int i = 0; i < nthreads; i++) {
+        args[i].tid = i;
+        args[i].nthreads = nthreads;
+
+        args[i].ncentroids = ncentroids;
+        args[i].nquery_vectors = nquery_vectors;
+        args[i].npids = npids;
+
+        args[i].pids = pids;
+        args[i].centroid_scores = centroid_scores;
+        args[i].codes = codes;
+        args[i].doclens = doclens;
+        args[i].offsets = offsets;
+        args[i].idx = idx;
+
+        args[i].approx_scores = std::priority_queue<std::pair<float, int>>();
+
+        int rc = pthread_create(&threads[i], NULL, maxsim, (void*)&args[i]);
+        if (rc) {
+            fprintf(stderr, "Unable to create thread %d: %d\n", i, rc);
+            std::exit(1);
+        }
+    }
+
+    for (int i = 0; i < nthreads; i++) {
+        pthread_join(threads[i], NULL);
+    }
+
+    std::priority_queue<std::pair<float, int>> global_approx_scores;
+    for (int i = 0; i < nthreads; i++) {
+        for (int j = 0; j < nfiltered_docs; j++) {
+            if (args[i].approx_scores.empty()) {
+                break;
+            }
+            global_approx_scores.push(args[i].approx_scores.top());
+            args[i].approx_scores.pop();
+        }
+    }
+
+    for (int i = 0; i < nfiltered_docs; i++) {
+        std::pair<float, int> score_and_pid = global_approx_scores.top();
+        filtered_pids[i] = score_and_pid.second;
+        global_approx_scores.pop();
+    }
+}
+
+torch::Tensor filter_pids(const torch::Tensor pids,
+                          const torch::Tensor centroid_scores,
+                          const torch::Tensor codes,
+                          const torch::Tensor doclens,
+                          const torch::Tensor offsets, const torch::Tensor idx,
+                          int nfiltered_docs) {
+    auto ncentroids = centroid_scores.size(0);
+    auto nquery_vectors = centroid_scores.size(1);
+    auto npids = pids.size(0);
+
+    auto pids_a = pids.data_ptr<int>();
+    auto centroid_scores_a = centroid_scores.data_ptr<float>();
+    auto codes_a = codes.data_ptr<int>();
+    auto doclens_a = doclens.data_ptr<int64_t>();
+    auto offsets_a = offsets.data_ptr<int64_t>();
+    auto idx_a = idx.data_ptr<bool>();
+
+    int filtered_pids[nfiltered_docs];
+    filter_pids_helper(ncentroids, nquery_vectors, npids, pids_a,
+                       centroid_scores_a, codes_a, doclens_a, offsets_a, idx_a,
+                       nfiltered_docs, filtered_pids);
+
+    int nfinal_filtered_docs = (int)(nfiltered_docs / 4);
+    int final_filtered_pids[nfinal_filtered_docs];
+    bool ones[ncentroids];
+    for (int i = 0; i < ncentroids; i++) {
+        ones[i] = true;
+    }
+    filter_pids_helper(ncentroids, nquery_vectors, nfiltered_docs,
+                       filtered_pids, centroid_scores_a, codes_a, doclens_a,
+                       offsets_a, ones, nfinal_filtered_docs,
+                       final_filtered_pids);
+
+    auto options =
+        torch::TensorOptions().dtype(torch::kInt32).requires_grad(false);
+    return torch::from_blob(final_filtered_pids, {nfinal_filtered_docs},
+                            options)
+        .clone();
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("filter_pids_cpp", &filter_pids, "Filter pids");
+}
+
diff --git a/colbert/search/index_loader.py b/colbert/search/index_loader.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8f377e72a0adc37c38ca2d2bf3c0f4aed172099
--- /dev/null
+++ b/colbert/search/index_loader.py
@@ -0,0 +1,86 @@
+import os
+import ujson
+import torch
+import numpy as np
+import tqdm
+
+from colbert.utils.utils import lengths2offsets, print_message, dotdict, flatten
+from colbert.indexing.codecs.residual import ResidualCodec
+from colbert.indexing.utils import optimize_ivf
+from colbert.search.strided_tensor import StridedTensor
+
+
+class IndexLoader:
+    def __init__(self, index_path, use_gpu=True):
+        self.index_path = index_path
+        self.use_gpu = use_gpu
+
+        self._load_codec()
+        self._load_ivf()
+
+        self._load_doclens()
+        self._load_embeddings()
+
+    def _load_codec(self):
+        print_message(f"#> Loading codec...")
+        self.codec = ResidualCodec.load(self.index_path)
+
+    def _load_ivf(self):
+        print_message(f"#> Loading IVF...")
+
+        if os.path.exists(os.path.join(self.index_path, "ivf.pid.pt")):
+            ivf, ivf_lengths = torch.load(os.path.join(self.index_path, "ivf.pid.pt"), map_location='cpu')
+        else:
+            assert os.path.exists(os.path.join(self.index_path, "ivf.pt"))
+            ivf, ivf_lengths = torch.load(os.path.join(self.index_path, "ivf.pt"), map_location='cpu')
+            ivf, ivf_lengths = optimize_ivf(ivf, ivf_lengths, self.index_path)
+
+        if False:
+            ivf = ivf.tolist()
+            ivf = [ivf[offset:endpos] for offset, endpos in lengths2offsets(ivf_lengths)]
+        else:
+            # ivf, ivf_lengths = ivf.cuda(), torch.LongTensor(ivf_lengths).cuda()  # FIXME: REMOVE THIS LINE!
+            ivf = StridedTensor(ivf, ivf_lengths, use_gpu=self.use_gpu)
+
+        self.ivf = ivf
+
+    def _load_doclens(self):
+        doclens = []
+
+        print_message("#> Loading doclens...")
+
+        for chunk_idx in tqdm.tqdm(range(self.num_chunks)):
+            with open(os.path.join(self.index_path, f'doclens.{chunk_idx}.json')) as f:
+                chunk_doclens = ujson.load(f)
+                doclens.extend(chunk_doclens)
+
+        self.doclens = torch.tensor(doclens)
+
+    def _load_embeddings(self):
+        self.embeddings = ResidualCodec.Embeddings.load_chunks(self.index_path, range(self.num_chunks),
+                                                               self.num_embeddings)
+
+    @property
+    def metadata(self):
+        try:
+            self._metadata
+        except:
+            with open(os.path.join(self.index_path, 'metadata.json')) as f:
+                self._metadata = ujson.load(f)
+
+        return self._metadata
+
+    @property
+    def config(self):
+        raise NotImplementedError()  # load from dict at metadata['config']
+
+    @property
+    def num_chunks(self):
+        # EVENTUALLY: If num_chunks doesn't exist (i.e., old index), fall back to counting doclens.*.json files.
+        return self.metadata['num_chunks']
+
+    @property
+    def num_embeddings(self):
+        # EVENTUALLY: If num_embeddings doesn't exist (i.e., old index), sum the values in doclens.*.json files.
+        return self.metadata['num_embeddings']
+
diff --git a/colbert/search/index_storage.py b/colbert/search/index_storage.py
new file mode 100644
index 0000000000000000000000000000000000000000..228b9250057b3b877d62c0a7e98aa2b4c538e467
--- /dev/null
+++ b/colbert/search/index_storage.py
@@ -0,0 +1,172 @@
+import torch
+
+from colbert.utils.utils import flatten, print_message
+
+from colbert.indexing.loaders import load_doclens
+from colbert.indexing.codecs.residual_embeddings_strided import ResidualEmbeddingsStrided
+
+from colbert.search.strided_tensor import StridedTensor
+from colbert.search.candidate_generation import CandidateGeneration
+
+from .index_loader import IndexLoader
+from colbert.modeling.colbert import colbert_score, colbert_score_packed, colbert_score_reduce
+
+from math import ceil
+
+import os
+import pathlib
+from torch.utils.cpp_extension import load
+
+
+class IndexScorer(IndexLoader, CandidateGeneration):
+    def __init__(self, index_path, use_gpu=True):
+        super().__init__(index_path=index_path, use_gpu=use_gpu)
+
+        IndexScorer.try_load_torch_extensions(use_gpu)
+
+        self.embeddings_strided = ResidualEmbeddingsStrided(self.codec, self.embeddings, self.doclens)
+
+    @classmethod
+    def try_load_torch_extensions(cls, use_gpu):
+        if hasattr(cls, "loaded_extensions") or use_gpu:
+            return
+
+        print_message(f"Loading filter_pids_cpp extension (set COLBERT_LOAD_TORCH_EXTENSION_VERBOSE=True for more info)...")
+        filter_pids_cpp = load(
+            name="filter_pids_cpp",
+            sources=[
+                os.path.join(
+                    pathlib.Path(__file__).parent.resolve(), "filter_pids.cpp"
+                ),
+            ],
+            extra_cflags=["-O3"],
+            verbose=os.getenv("COLBERT_LOAD_TORCH_EXTENSION_VERBOSE", "False") == "True",
+        )
+        cls.filter_pids = filter_pids_cpp.filter_pids_cpp
+
+        print_message(f"Loading decompress_residuals_cpp extension (set COLBERT_LOAD_TORCH_EXTENSION_VERBOSE=True for more info)...")
+        decompress_residuals_cpp = load(
+            name="decompress_residuals_cpp",
+            sources=[
+                os.path.join(
+                    pathlib.Path(__file__).parent.resolve(), "decompress_residuals.cpp"
+                ),
+            ],
+            extra_cflags=["-O3"],
+            verbose=os.getenv("COLBERT_LOAD_TORCH_EXTENSION_VERBOSE", "False") == "True",
+        )
+        cls.decompress_residuals = decompress_residuals_cpp.decompress_residuals_cpp
+
+        cls.loaded_extensions = True
+    def lookup_eids(self, embedding_ids, codes=None, out_device='cuda'):
+        return self.embeddings_strided.lookup_eids(embedding_ids, codes=codes, out_device=out_device)
+
+    def lookup_pids(self, passage_ids, out_device='cuda', return_mask=False):
+        return self.embeddings_strided.lookup_pids(passage_ids, out_device)
+
+    def retrieve(self, config, Q):
+        Q = Q[:, :config.query_maxlen]   # NOTE: Candidate generation uses only the query tokens
+        embedding_ids, centroid_scores = self.generate_candidates(config, Q)
+
+        return embedding_ids, centroid_scores
+
+    def embedding_ids_to_pids(self, embedding_ids):
+        all_pids = torch.unique(self.emb2pid[embedding_ids.long()].cuda(), sorted=False)
+        return all_pids
+
+    def rank(self, config, Q, filter_fn=None):
+        with torch.inference_mode():
+            pids, centroid_scores = self.retrieve(config, Q)
+
+            if filter_fn is not None:
+                pids = filter_fn(pids)
+
+            scores, pids = self.score_pids(config, Q, pids, centroid_scores)
+
+            scores_sorter = scores.sort(descending=True)
+            pids, scores = pids[scores_sorter.indices].tolist(), scores_sorter.values.tolist()
+
+            return pids, scores
+
+    def score_pids(self, config, Q, pids, centroid_scores):
+        """
+            Always supply a flat list or tensor for `pids`.
+
+            Supply sizes Q = (1 | num_docs, *, dim) and D = (num_docs, *, dim).
+            If Q.size(0) is 1, the matrix will be compared with all passages.
+            Otherwise, each query matrix will be compared against the *aligned* passage.
+        """
+
+        # TODO: Remove batching?
+        batch_size = 2 ** 20
+
+        if self.use_gpu:
+            centroid_scores = centroid_scores.cuda()
+
+        idx = centroid_scores.max(-1).values >= config.centroid_score_threshold
+
+        if self.use_gpu:
+            approx_scores = []
+
+            # Filter docs using pruned centroid scores
+            for i in range(0, ceil(len(pids) / batch_size)):
+                pids_ = pids[i * batch_size : (i+1) * batch_size]
+                codes_packed, codes_lengths = self.embeddings_strided.lookup_codes(pids_)
+                idx_ = idx[codes_packed.long()]
+                pruned_codes_strided = StridedTensor(idx_, codes_lengths, use_gpu=self.use_gpu)
+                pruned_codes_padded, pruned_codes_mask = pruned_codes_strided.as_padded_tensor()
+                pruned_codes_lengths = (pruned_codes_padded * pruned_codes_mask).sum(dim=1)
+                codes_packed_ = codes_packed[idx_]
+                approx_scores_ = centroid_scores[codes_packed_.long()]
+                if approx_scores_.shape[0] == 0:
+                    approx_scores.append(torch.zeros((len(pids_),), dtype=approx_scores_.dtype))
+                    continue
+                approx_scores_strided = StridedTensor(approx_scores_, pruned_codes_lengths, use_gpu=self.use_gpu)
+                approx_scores_padded, approx_scores_mask = approx_scores_strided.as_padded_tensor()
+                approx_scores_ = colbert_score_reduce(approx_scores_padded, approx_scores_mask, config)
+                approx_scores.append(approx_scores_)
+            approx_scores = torch.cat(approx_scores, dim=0)
+            if config.ndocs < len(approx_scores):
+                pids = pids[torch.topk(approx_scores, k=config.ndocs).indices]
+
+            # Filter docs using full centroid scores
+            codes_packed, codes_lengths = self.embeddings_strided.lookup_codes(pids)
+            approx_scores = centroid_scores[codes_packed.long()]
+            approx_scores_strided = StridedTensor(approx_scores, codes_lengths, use_gpu=self.use_gpu)
+            approx_scores_padded, approx_scores_mask = approx_scores_strided.as_padded_tensor()
+            approx_scores = colbert_score_reduce(approx_scores_padded, approx_scores_mask, config)
+            if config.ndocs // 4 < len(approx_scores):
+                pids = pids[torch.topk(approx_scores, k=(config.ndocs // 4)).indices]
+        else:
+            pids = IndexScorer.filter_pids(
+                    pids, centroid_scores, self.embeddings.codes, self.doclens,
+                    self.embeddings_strided.codes_strided.offsets, idx, config.ndocs
+                )
+
+        # Rank final list of docs using full approximate embeddings (including residuals)
+        if self.use_gpu:
+            D_packed, D_mask = self.lookup_pids(pids)
+        else:
+            D_packed = IndexScorer.decompress_residuals(
+                    pids,
+                    self.doclens,
+                    self.embeddings_strided.codes_strided.offsets,
+                    self.codec.bucket_weights,
+                    self.codec.reversed_bit_map,
+                    self.codec.decompression_lookup_table,
+                    self.embeddings.residuals,
+                    self.embeddings.codes,
+                    self.codec.centroids,
+                    self.codec.dim,
+                    self.codec.nbits
+                )
+            D_packed = torch.nn.functional.normalize(D_packed.to(torch.float32), p=2, dim=-1)
+            D_mask = self.doclens[pids.long()]
+
+        if Q.size(0) == 1:
+            return colbert_score_packed(Q, D_packed, D_mask, config), pids
+
+        D_strided = StridedTensor(D_packed, D_mask, use_gpu=self.use_gpu)
+        D_padded, D_lengths = D_strided.as_padded_tensor()
+
+        return colbert_score(Q, D_padded, D_lengths, config), pids
diff --git a/colbert/search/segmented_lookup.cpp b/colbert/search/segmented_lookup.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..68be85661887a1eb0894d0c6b4e8091b00ad824d
--- /dev/null
+++ b/colbert/search/segmented_lookup.cpp
@@ -0,0 +1,148 @@
+#include <pthread.h>
+#include <torch/extension.h>
+
+#include <algorithm>
+#include <numeric>
+
+typedef struct {
+    int tid;
+    pthread_mutex_t* mutex;
+    std::queue<int>* queue;
+
+    int64_t ndocs;
+    int64_t noutputs;
+    int64_t dim;
+
+    void* input;
+    int64_t* lengths;
+    int64_t* offsets;
+    int64_t* cumulative_lengths;
+
+    void* output;
+} lookup_args_t;
+
+template <typename T>
+void* lookup(void* args) {
+    lookup_args_t* lookup_args = (lookup_args_t*)args;
+
+    int64_t* lengths = lookup_args->lengths;
+    int64_t* cumulative_lengths = lookup_args->cumulative_lengths;
+    int64_t* offsets = lookup_args->offsets;
+    int64_t dim = lookup_args->dim;
+
+    T* input = static_cast<T*>(lookup_args->input);
+    T* output = static_cast<T*>(lookup_args->output);
+
+    while (1) {
+        pthread_mutex_lock(lookup_args->mutex);
+        if (lookup_args->queue->empty()) {
+            pthread_mutex_unlock(lookup_args->mutex);
+            return NULL;
+        }
+        int i = lookup_args->queue->front();
+        lookup_args->queue->pop();
+        pthread_mutex_unlock(lookup_args->mutex);
+
+        std::memcpy(output + (cumulative_lengths[i] * dim),
+                    input + (offsets[i] * dim), lengths[i] * dim * sizeof(T));
+    }
+}
+
+template <typename T>
+torch::Tensor segmented_lookup_impl(const torch::Tensor input,
+                                    const torch::Tensor pids,
+                                    const torch::Tensor lengths,
+                                    const torch::Tensor offsets) {
+    auto lengths_a = lengths.data_ptr<int64_t>();
+    auto offsets_a = offsets.data_ptr<int64_t>();
+
+    int64_t ndocs = pids.size(0);
+    int64_t noutputs = std::accumulate(lengths_a, lengths_a + ndocs, 0);
+
+    int nthreads = at::get_num_threads();
+
+    int64_t dim;
+    torch::Tensor output;
+
+    if (input.dim() == 1) {
+        dim = 1;
+        output = torch::zeros({noutputs}, input.options());
+    } else {
+        assert(input.dim() == 2);
+        dim = input.size(1);
+        output = torch::zeros({noutputs, dim}, input.options());
+    }
+
+    int64_t cumulative_lengths[ndocs + 1];
+    cumulative_lengths[0] = 0;
+    std::partial_sum(lengths_a, lengths_a + ndocs, cumulative_lengths + 1);
+
+    pthread_mutex_t mutex;
+    int rc = pthread_mutex_init(&mutex, NULL);
+    if (rc) {
+        fprintf(stderr, "Unable to init mutex: %d\n", rc);
+    }
+
+    std::queue<int> queue;
+    for (int i = 0; i < ndocs; i++) {
+        queue.push(i);
+    }
+
+    pthread_t threads[nthreads];
+    lookup_args_t args[nthreads];
+    for (int i = 0; i < nthreads; i++) {
+        args[i].tid = i;
+        args[i].mutex = &mutex;
+        args[i].queue = &queue;
+
+        args[i].ndocs = ndocs;
+        args[i].noutputs = noutputs;
+        args[i].dim = dim;
+
+        args[i].input = (void*)input.data_ptr<T>();
+        args[i].lengths = lengths_a;
+        args[i].offsets = offsets_a;
+        args[i].cumulative_lengths = cumulative_lengths;
+
+        args[i].output = (void*)output.data_ptr<T>();
+
+        rc = pthread_create(&threads[i], NULL, lookup<T>, (void*)&args[i]);
+        if (rc) {
+            fprintf(stderr, "Unable to create thread %d: %d\n", i, rc);
+        }
+    }
+
+    for (int i = 0; i < nthreads; i++) {
+        pthread_join(threads[i], NULL);
+    }
+
+    rc = pthread_mutex_destroy(&mutex);
+    if (rc) {
+        fprintf(stderr, "Unable to destroy mutex: %d\n", rc);
+    }
+
+    return output;
+}
+
+torch::Tensor segmented_lookup(const torch::Tensor input,
+                               const torch::Tensor pids,
+                               const torch::Tensor lengths,
+                               const torch::Tensor offsets) {
+    if (input.dtype() == torch::kUInt8) {
+        return segmented_lookup_impl<uint8_t>(input, pids, lengths, offsets);
+    } else if (input.dtype() == torch::kInt32) {
+        return segmented_lookup_impl<int>(input, pids, lengths, offsets);
+    } else if (input.dtype() == torch::kInt64) {
+        return segmented_lookup_impl<int64_t>(input, pids, lengths, offsets);
+    } else if (input.dtype() == torch::kFloat32) {
+        return segmented_lookup_impl<float>(input, pids, lengths, offsets);
+    } else if (input.dtype() == torch::kFloat16) {
+        return segmented_lookup_impl<at::Half>(input, pids, lengths, offsets);
+    } else {
+        assert(false);
+    }
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("segmented_lookup_cpp", &segmented_lookup, "Segmented lookup");
+}
diff --git a/colbert/search/strided_tensor.py b/colbert/search/strided_tensor.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bdc13d13eaea795b697f586e2e57f9cda649492
--- /dev/null
+++ b/colbert/search/strided_tensor.py
@@ -0,0 +1,223 @@
+from struct import pack
+import torch
+from torch._C import device
+
+from colbert.utils.utils import flatten, print_message
+
+from .strided_tensor_core import StridedTensorCore, _create_mask, _create_view
+
+import os
+import pathlib
+from torch.utils.cpp_extension import load
+
+
+class StridedTensor(StridedTensorCore):
+    def __init__(self, packed_tensor, lengths, dim=None, use_gpu=True):
+        super().__init__(packed_tensor, lengths, dim=dim, use_gpu=use_gpu)
+
+        StridedTensor.try_load_torch_extensions(use_gpu)
+
+    @classmethod
+    def try_load_torch_extensions(cls, use_gpu):
+        if hasattr(cls, "loaded_extensions") or use_gpu:
+            return
+
+        print_message(f"Loading segmented_lookup_cpp extension (set COLBERT_LOAD_TORCH_EXTENSION_VERBOSE=True for more info)...")
+        segmented_lookup_cpp = load(
+            name="segmented_lookup_cpp",
+            sources=[
+                os.path.join(
+                    pathlib.Path(__file__).parent.resolve(), "segmented_lookup.cpp"
+                ),
+            ],
+            extra_cflags=["-O3"],
+            verbose=os.getenv("COLBERT_LOAD_TORCH_EXTENSION_VERBOSE", "False") == "True",
+        )
+        cls.segmented_lookup = segmented_lookup_cpp.segmented_lookup_cpp
+
+        cls.loaded_extensions = True
+
+    @classmethod
+    def pad_packed(cls, packed_tensor, lengths):
+        assert False, "This seems to be incorrect but I can't see why. Is it the inner_dims in the views?"
+
+        packed_tensor, lengths = packed_tensor.cuda().contiguous(), lengths.cuda()
+
+        inner_dims = packed_tensor.size()[1:]
+        stride = lengths.max().item()
+        offsets = torch.cumsum(lengths, dim=0) - lengths[0]
+
+        padding = torch.zeros(stride, *inner_dims, device=packed_tensor.device, dtype=packed_tensor.dtype)
+        packed_tensor = torch.cat((packed_tensor, padding))
+
+        view = _create_view(packed_tensor, stride, inner_dims)[offsets]
+        mask = _create_mask(lengths, stride, like=view)
+
+        return view, mask
+
+    def _prepare_lookup(self, pids):
+        if isinstance(pids, list):
+            pids = torch.tensor(pids)
+
+        assert pids.dim() == 1
+
+        if self.use_gpu:
+            pids = pids.cuda()
+        pids = pids.long()
+        lengths = self.lengths[pids]
+        if self.use_gpu:
+            lengths = lengths.cuda()
+        offsets = self.offsets[pids]
+
+        return pids, lengths, offsets
+
+    def lookup(self, pids, output='packed'):
+        pids, lengths, offsets = self._prepare_lookup(pids)
+
+        if self.use_gpu:
+            stride = lengths.max().item()
+            stride = next(s for s in self.strides if stride <= s)
+
+            tensor = self.views[stride][offsets]
+            if self.use_gpu:
+                tensor = tensor.cuda()
+
+            mask = _create_mask(lengths, stride, use_gpu=self.use_gpu)
+
+            if output == 'padded':
+                return tensor, mask
+
+            assert output == 'packed'
+
+            tensor = tensor[mask]
+        else:
+            tensor = StridedTensor.segmented_lookup(self.tensor, pids, lengths, offsets)
+
+        return tensor, lengths
+
+    def lookup_staggered(self, pids, output='packed'):
+        permute_idxs, unordered_tensors, unordered_lengths, unordered_masks = self.lookup_packed_unordered(pids)
+
+        output_tensor = torch.empty(permute_idxs.size(0), self.max_stride, *self.inner_dims,
+                                    dtype=unordered_tensors[0].dtype, device=unordered_tensors[0].device)
+
+        output_mask = torch.zeros(permute_idxs.size(0), self.max_stride,
+                                  dtype=unordered_masks[0].dtype, device=unordered_masks[0].device)
+
+        offset = 0
+        for tensor, mask in zip(unordered_tensors, unordered_masks):
+            endpos = offset + tensor.size(0)
+            output_tensor[offset:endpos, :tensor.size(1)] = tensor
+            output_mask[offset:endpos, :mask.size(1)] = mask
+            offset = endpos
+
+        output_mask = output_mask[permute_idxs]
+        output_tensor = output_tensor[permute_idxs]
+
+        if output == 'padded':
+            return output_tensor, output_mask
+
+        assert output == 'packed'
+
+        output_tensor = output_tensor[output_mask]
+
+        return output_tensor, unordered_lengths[permute_idxs]
+
+    def lookup_packed_unordered(self, pids):
+        pids, lengths, offsets = self._prepare_lookup(pids)
+
+        lengths2 = lengths.clone()
+        sentinel = self.strides[-1] + 1
+        order = torch.arange(pids.size(0), device='cuda' if self.use_gpu else 'cpu')
+
+        all_orders = []
+        all_tensors = []
+        all_lengths = []
+        all_masks = []
+
+        for stride in self.strides:
+            is_shorter = lengths2 <= stride
+
+            if is_shorter.sum() == 0:
+                continue
+
+            order_ = order[is_shorter]
+            tensor_, lengths_, mask_ = self._lookup_with_stride(stride, lengths[is_shorter], offsets[is_shorter])
+
+            all_orders.append(order_)
+            all_tensors.append(tensor_)
+            all_lengths.append(lengths_)
+            all_masks.append(mask_)
+
+            lengths2[is_shorter] = sentinel
+
+        assert lengths2.allclose(torch.tensor([sentinel], device='cuda' if self.use_gpu else 'cpu'))
+
+        all_orders = torch.cat(all_orders)
+        permute_idxs = torch.sort(all_orders).indices
+
+        return permute_idxs, all_tensors, torch.cat(all_lengths), all_masks
+
+    def _lookup_with_stride(self, stride, lengths, offsets):
+        tensor = self.views[stride][offsets]
+        if self.use_gpu:
+            tensor = tensor.cuda()
+
+        mask = _create_mask(lengths, stride, use_gpu=self.use_gpu)
+        # tensor = tensor[mask]
+
+        return tensor, lengths, mask
+
+
+if __name__ == '__main__':
+    # lst = []
+    # for _ in range(10):
+    #     lst.append(list(range(random.randint(0, 10))))
+
+    # print(lst)
+
+    # t = StridedTensor.from_nested_list(lst)
+    # print(t.lookup([9]))
+
+    import os
+    import pickle
+
+    index_path = '/future/u/okhattab/root/unit/indexes/2021/08/residual.NQ-micro'
+    with open(os.path.join(index_path, "centroid_idx_to_embedding_ids.pickle"), "rb") as f:
+        ivf_list = pickle.load(f)
+
+    assert len(ivf_list) == max(ivf_list.keys()) + 1
+    ivf_list = [ivf_list[i] for i in range(len(ivf_list))]
+
+    for x in ivf_list:
+        assert type(x) is list
+        assert type(x[0]) is int
+
+    ncentroids = len(ivf_list)
+
+    ivf = StridedTensor.from_nested_list(ivf_list)
+
+    import time
+
+    torch.cuda.synchronize()
+    t = time.time()
+
+    N = 100
+    for _ in range(N):
+        probed_centroids = torch.randint(0, ncentroids, size=(32, 8)).flatten()
+        emb_ids, emb_ids_lengths = ivf.lookup(probed_centroids).as_packed_tensor()
+
+    torch.cuda.synchronize()
+    print((time.time() - t) * 1000 / N, 'ms')
+
+    print(emb_ids_lengths)
+
+    slow_result = flatten([ivf_list[idx] for idx in probed_centroids.flatten().tolist()])
+    print(emb_ids.size(), len(slow_result))
+
+    for a, b in zip(slow_result, emb_ids.flatten().tolist()):
+        assert a == b, (a, b)
+
+    print("#> Done!")
+
+    print(ivf.lookup(probed_centroids).as_padded_tensor()[0].size())
diff --git a/colbert/search/strided_tensor_core.py b/colbert/search/strided_tensor_core.py
new file mode 100644
index 0000000000000000000000000000000000000000..49760d72866c19e4bbdf59ebe01cf99c97e34716
--- /dev/null
+++ b/colbert/search/strided_tensor_core.py
@@ -0,0 +1,130 @@
+import torch
+import random
+
+import numpy as np
+
+from colbert.utils.utils import flatten
+
+
+"""
+import line_profiler
+import atexit
+profile = line_profiler.LineProfiler()
+atexit.register(profile.print_stats)
+"""
+
+
+class StridedTensorCore:
+    # # @profile
+    def __init__(self, packed_tensor, lengths, dim=None, use_gpu=True):
+        self.dim = dim
+        self.tensor = packed_tensor
+        self.inner_dims = self.tensor.size()[1:]
+        self.use_gpu = use_gpu
+
+        self.lengths = lengths.long() if torch.is_tensor(lengths) else torch.LongTensor(lengths)
+
+        self.strides = _select_strides(self.lengths, [.5, .75, .9, .95]) + [self.lengths.max().item()]
+        self.max_stride = self.strides[-1]
+
+        zero = torch.zeros(1, dtype=torch.long, device=self.lengths.device)
+        self.offsets = torch.cat((zero, torch.cumsum(self.lengths, dim=0)))
+
+        if self.offsets[-2] + self.max_stride > self.tensor.size(0):
+            # if self.tensor.size(0) > 10_000_000:
+            #     print("#> WARNING: StridedTensor has to add padding, internally, to a large tensor.")
+            #     print("#> WARNING: Consider doing this padding in advance to save memory!")
+
+            padding = torch.zeros(self.max_stride, *self.inner_dims, dtype=self.tensor.dtype, device=self.tensor.device)
+            self.tensor = torch.cat((self.tensor, padding))
+
+        self.views = {stride: _create_view(self.tensor, stride, self.inner_dims) for stride in self.strides}
+
+    @classmethod
+    def from_packed_tensor(cls, tensor, lengths):
+        return cls(tensor, lengths)
+
+    @classmethod
+    def from_padded_tensor(cls, tensor, mask):
+        pass
+
+    @classmethod
+    def from_nested_list(cls, lst):
+        flat_lst = flatten(lst)
+
+        tensor = torch.Tensor(flat_lst)
+        lengths = [len(sublst) for sublst in lst]
+
+        return cls(tensor, lengths, dim=0)
+
+    @classmethod
+    def from_tensors_list(cls, tensors):
+        # torch.cat(tensors)
+        # lengths.
+        # cls(tensor, lengths)
+        raise NotImplementedError()
+
+    def as_packed_tensor(self, return_offsets=False):
+        unpadded_packed_tensor = self.tensor  # [:self.offsets[-1]]
+
+        return_vals = [unpadded_packed_tensor, self.lengths]
+
+        if return_offsets:
+            return_vals.append(self.offsets)
+
+        return tuple(return_vals)
+
+    # # @profile
+    def as_padded_tensor(self):
+        if self.use_gpu:
+            view = _create_view(self.tensor.cuda(), self.max_stride, self.inner_dims)[self.offsets[:-1]]
+            mask = _create_mask(self.lengths.cuda(), self.max_stride, like=view, use_gpu=self.use_gpu)
+        else:
+            #import pdb
+            #pdb.set_trace()
+            view = _create_view(self.tensor, self.max_stride, self.inner_dims)
+            view = view[self.offsets[:-1]]
+            mask = _create_mask(self.lengths, self.max_stride, like=view, use_gpu=self.use_gpu)
+
+        return view, mask
+
+    def as_tensors_list(self):
+        raise NotImplementedError()
+
+
+
+def _select_strides(lengths, quantiles):
+    if lengths.size(0) < 5_000:
+        return _get_quantiles(lengths, quantiles)
+    
+    sample = torch.randint(0, lengths.size(0), size=(2_000,))
+
+    return _get_quantiles(lengths[sample], quantiles)
+
+def _get_quantiles(lengths, quantiles):
+    return torch.quantile(lengths.float(), torch.tensor(quantiles, device=lengths.device)).int().tolist()
+
+
+def _create_view(tensor, stride, inner_dims):
+    outdim = tensor.size(0) - stride + 1
+    size = (outdim, stride, *inner_dims)
+
+    inner_dim_prod = int(np.prod(inner_dims))
+    multidim_stride = [inner_dim_prod, inner_dim_prod] + [1] * len(inner_dims)
+
+    return torch.as_strided(tensor, size=size, stride=multidim_stride)
+
+
+def _create_mask(lengths, stride, like=None, use_gpu=True):
+    if use_gpu:
+        mask = torch.arange(stride).cuda() + 1
+        mask = mask.unsqueeze(0) <= lengths.cuda().unsqueeze(-1)
+    else:
+        mask = torch.arange(stride) + 1
+        mask = mask.unsqueeze(0) <= lengths.unsqueeze(-1)
+
+    if like is not None:
+        for _ in range(like.dim() - mask.dim()):
+            mask = mask.unsqueeze(-1)
+
+    return mask
diff --git a/colbert/searcher.py b/colbert/searcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..6588712dcaa619b4a48dafc1e819b009585dc4c8
--- /dev/null
+++ b/colbert/searcher.py
@@ -0,0 +1,110 @@
+import os
+import torch
+
+from tqdm import tqdm
+from typing import Union
+
+from colbert.data import Collection, Queries, Ranking
+
+from colbert.modeling.checkpoint import Checkpoint
+from colbert.search.index_storage import IndexScorer
+
+from colbert.infra.provenance import Provenance
+from colbert.infra.run import Run
+from colbert.infra.config import ColBERTConfig, RunConfig
+from colbert.infra.launcher import print_memory_stats
+
+import time
+
+TextQueries = Union[str, 'list[str]', 'dict[int, str]', Queries]
+
+
+class Searcher:
+    def __init__(self, index, checkpoint=None, collection=None, config=None):
+        print_memory_stats()
+
+        initial_config = ColBERTConfig.from_existing(config, Run().config)
+
+        default_index_root = initial_config.index_root_
+        self.index = os.path.join(default_index_root, index)
+        self.index_config = ColBERTConfig.load_from_index(self.index)
+
+        self.checkpoint = checkpoint or self.index_config.checkpoint
+        self.checkpoint_config = ColBERTConfig.load_from_checkpoint(self.checkpoint)
+        self.config = ColBERTConfig.from_existing(self.checkpoint_config, self.index_config, initial_config)
+
+        self.collection = Collection.cast(collection or self.config.collection)
+        self.configure(checkpoint=self.checkpoint, collection=self.collection)
+
+        self.checkpoint = Checkpoint(self.checkpoint, colbert_config=self.config)
+        use_gpu = self.config.total_visible_gpus > 0
+        if use_gpu:
+            self.checkpoint = self.checkpoint.cuda()
+        self.ranker = IndexScorer(self.index, use_gpu)
+
+        print_memory_stats()
+
+    def configure(self, **kw_args):
+        self.config.configure(**kw_args)
+
+    def encode(self, text: TextQueries):
+        queries = text if type(text) is list else [text]
+        bsize = 128 if len(queries) > 128 else None
+
+        self.checkpoint.query_tokenizer.query_maxlen = self.config.query_maxlen
+        Q = self.checkpoint.queryFromText(queries, bsize=bsize, to_cpu=True)
+
+        return Q
+
+    def search(self, text: str, k=10, filter_fn=None):
+        Q = self.encode(text)
+        return self.dense_search(Q, k, filter_fn=filter_fn)
+
+    def search_all(self, queries: TextQueries, k=10, filter_fn=None):
+        queries = Queries.cast(queries)
+        queries_ = list(queries.values())
+
+        Q = self.encode(queries_)
+
+        return self._search_all_Q(queries, Q, k, filter_fn=filter_fn)
+
+    def _search_all_Q(self, queries, Q, k, filter_fn=None):
+        all_scored_pids = [list(zip(*self.dense_search(Q[query_idx:query_idx+1], k, filter_fn=filter_fn)))
+                           for query_idx in tqdm(range(Q.size(0)))]
+
+        data = {qid: val for qid, val in zip(queries.keys(), all_scored_pids)}
+
+        provenance = Provenance()
+        provenance.source = 'Searcher::search_all'
+        provenance.queries = queries.provenance()
+        provenance.config = self.config.export()
+        provenance.k = k
+
+        return Ranking(data=data, provenance=provenance)
+
+    def dense_search(self, Q: torch.Tensor, k=10, filter_fn=None):
+        if k <= 10:
+            if self.config.ncells is None:
+                self.configure(ncells=1)
+            if self.config.centroid_score_threshold is None:
+                self.configure(centroid_score_threshold=0.5)
+            if self.config.ndocs is None:
+                self.configure(ndocs=256)
+        elif k <= 100:
+            if self.config.ncells is None:
+                self.configure(ncells=2)
+            if self.config.centroid_score_threshold is None:
+                self.configure(centroid_score_threshold=0.45)
+            if self.config.ndocs is None:
+                self.configure(ndocs=1024)
+        else:
+            if self.config.ncells is None:
+                self.configure(ncells=4)
+            if self.config.centroid_score_threshold is None:
+                self.configure(centroid_score_threshold=0.4)
+            if self.config.ndocs is None:
+                self.configure(ndocs=max(k * 4, 4096))
+
+        pids, scores = self.ranker.rank(self.config, Q, filter_fn=filter_fn)
+
+        return pids[:k], list(range(1, k+1)), scores[:k]
diff --git a/colbert/tests/e2e_test.py b/colbert/tests/e2e_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9cd129a41e8e404117b7dfba5e5ff867c855828
--- /dev/null
+++ b/colbert/tests/e2e_test.py
@@ -0,0 +1,95 @@
+import os
+import argparse
+from collections import namedtuple
+from datasets import load_dataset
+from utility.utils.dpr import has_answer, DPR_normalize
+import tqdm
+
+from colbert import Indexer, Searcher
+from colbert.infra import ColBERTConfig, RunConfig, Run
+
+SquadExample = namedtuple("SquadExample", "id title context question answers")
+
+
+def build_index_and_init_searcher(checkpoint, collection, experiment_dir):
+    nbits = 1  # encode each dimension with 1 bits
+    doc_maxlen = 180  # truncate passages at 180 tokens
+    experiment = f"e2etest.nbits={nbits}"
+
+    with Run().context(RunConfig(nranks=1)):
+        config = ColBERTConfig(
+            doc_maxlen=doc_maxlen,
+            nbits=nbits,
+            root=experiment_dir,
+            experiment=experiment,
+        )
+        indexer = Indexer(checkpoint, config=config)
+        indexer.index(name=experiment, collection=collection, overwrite=True)
+
+        config = ColBERTConfig(
+            root=experiment_dir,
+            experiment=experiment,
+        )
+        searcher = Searcher(
+            index=experiment,
+            config=config,
+        )
+
+        return searcher
+
+
+def success_at_k(searcher, examples, k):
+    scores = []
+    for ex in tqdm.tqdm(examples):
+        scores.append(evaluate_retrieval_example(searcher, ex, k))
+    return sum(scores) / len(scores)
+
+
+def evaluate_retrieval_example(searcher, ex, k):
+    results = searcher.search(ex.question, k=k)
+    for passage_id, passage_rank, passage_score in zip(*results):
+        passage = searcher.collection[passage_id]
+        score = has_answer([DPR_normalize(ans) for ans in ex.answers], passage)
+        if score:
+            return 1
+    return 0
+
+
+def get_squad_split(squad, split="validation"):
+    fields = squad[split].features
+    data = zip(*[squad[split][field] for field in fields])
+    return [
+        SquadExample(eid, title, context, question, answers["text"])
+        for eid, title, context, question, answers in data
+    ]
+
+
+def main(args):
+    checkpoint = args.checkpoint
+    collection = args.collection
+    experiment_dir = args.expdir
+
+    # Start the test
+    k = 5
+    searcher = build_index_and_init_searcher(checkpoint, collection, experiment_dir)
+
+    squad = load_dataset("squad")
+    squad_dev = get_squad_split(squad)
+    success_rate = success_at_k(searcher, squad_dev[:1000], k)
+    assert success_rate > 0.93, f"success rate at {success_rate} is lower than expected"
+    print(f"test passed with success rate {success_rate}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Start end-to-end test.")
+    parser.add_argument(
+        "--checkpoint", type=str, required=True, help="Model checkpoint"
+    )
+    parser.add_argument(
+        "--collection", type=str, required=True, help="Path to collection"
+    )
+    parser.add_argument(
+        "--expdir", type=str, required=True, help="Experiment directory"
+    )
+    args = parser.parse_args()
+    main(args)
diff --git a/colbert/trainer.py b/colbert/trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b0ac566577c016ff1dade2a90266764c1c6dafe
--- /dev/null
+++ b/colbert/trainer.py
@@ -0,0 +1,36 @@
+from colbert.infra.run import Run
+from colbert.infra.launcher import Launcher
+from colbert.infra.config import ColBERTConfig, RunConfig
+
+from colbert.training.training import train
+
+
+class Trainer:
+    def __init__(self, triples, queries, collection, config=None):
+        self.config = ColBERTConfig.from_existing(config, Run().config)
+
+        self.triples = triples
+        self.queries = queries
+        self.collection = collection
+
+    def configure(self, **kw_args):
+        self.config.configure(**kw_args)
+
+    def train(self, checkpoint='bert-base-uncased'):
+        """
+            Note that config.checkpoint is ignored. Only the supplied checkpoint here is used.
+        """
+
+        # Resources don't come from the config object. They come from the input parameters.
+        # TODO: After the API stabilizes, make this "self.config.assign()" to emphasize this distinction.
+        self.configure(triples=self.triples, queries=self.queries, collection=self.collection)
+        self.configure(checkpoint=checkpoint)
+
+        launcher = Launcher(train)
+
+        self._best_checkpoint_path = launcher.launch(self.config, self.triples, self.queries, self.collection)
+
+
+    def best_checkpoint_path(self):
+        return self._best_checkpoint_path
+
diff --git a/colbert/training/__init__.py b/colbert/training/__init__.py
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diff --git a/colbert/training/eager_batcher.py b/colbert/training/eager_batcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..2edec7a18dcd61c3bbb2dbd6596fd64e1ccdcf4c
--- /dev/null
+++ b/colbert/training/eager_batcher.py
@@ -0,0 +1,62 @@
+import os
+import ujson
+
+from functools import partial
+from colbert.utils.utils import print_message
+from colbert.modeling.tokenization import QueryTokenizer, DocTokenizer, tensorize_triples
+
+from colbert.utils.runs import Run
+
+
+class EagerBatcher():
+    def __init__(self, args, rank=0, nranks=1):
+        self.rank, self.nranks = rank, nranks
+        self.bsize, self.accumsteps = args.bsize, args.accumsteps
+
+        self.query_tokenizer = QueryTokenizer(args.query_maxlen)
+        self.doc_tokenizer = DocTokenizer(args.doc_maxlen)
+        self.tensorize_triples = partial(tensorize_triples, self.query_tokenizer, self.doc_tokenizer)
+
+        self.triples_path = args.triples
+        self._reset_triples()
+
+    def _reset_triples(self):
+        self.reader = open(self.triples_path, mode='r', encoding="utf-8")
+        self.position = 0
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        queries, positives, negatives = [], [], []
+
+        for line_idx, line in zip(range(self.bsize * self.nranks), self.reader):
+            if (self.position + line_idx) % self.nranks != self.rank:
+                continue
+
+            query, pos, neg = line.strip().split('\t')
+
+            queries.append(query)
+            positives.append(pos)
+            negatives.append(neg)
+
+        self.position += line_idx + 1
+
+        if len(queries) < self.bsize:
+            raise StopIteration
+
+        return self.collate(queries, positives, negatives)
+
+    def collate(self, queries, positives, negatives):
+        assert len(queries) == len(positives) == len(negatives) == self.bsize
+
+        return self.tensorize_triples(queries, positives, negatives, self.bsize // self.accumsteps)
+
+    def skip_to_batch(self, batch_idx, intended_batch_size):
+        self._reset_triples()
+
+        Run.warn(f'Skipping to batch #{batch_idx} (with intended_batch_size = {intended_batch_size}) for training.')
+
+        _ = [self.reader.readline() for _ in range(batch_idx * intended_batch_size)]
+
+        return None
diff --git a/colbert/training/lazy_batcher.py b/colbert/training/lazy_batcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..a41c3bff99e4a72f41d734e0ba01e663eb0c56e8
--- /dev/null
+++ b/colbert/training/lazy_batcher.py
@@ -0,0 +1,75 @@
+import os
+import ujson
+
+from functools import partial
+from colbert.infra.config.config import ColBERTConfig
+from colbert.utils.utils import print_message, zipstar
+from colbert.modeling.tokenization import QueryTokenizer, DocTokenizer, tensorize_triples
+from colbert.evaluation.loaders import load_collection
+
+from colbert.data.collection import Collection
+from colbert.data.queries import Queries
+from colbert.data.examples import Examples
+
+# from colbert.utils.runs import Run
+
+
+class LazyBatcher():
+    def __init__(self, config: ColBERTConfig, triples, queries, collection, rank=0, nranks=1):
+        self.bsize, self.accumsteps = config.bsize, config.accumsteps
+        self.nway = config.nway
+
+        self.query_tokenizer = QueryTokenizer(config)
+        self.doc_tokenizer = DocTokenizer(config)
+        self.tensorize_triples = partial(tensorize_triples, self.query_tokenizer, self.doc_tokenizer)
+        self.position = 0
+
+        self.triples = Examples.cast(triples, nway=self.nway).tolist(rank, nranks)
+        self.queries = Queries.cast(queries)
+        self.collection = Collection.cast(collection)
+
+    def __iter__(self):
+        return self
+
+    def __len__(self):
+        return len(self.triples)
+
+    def __next__(self):
+        offset, endpos = self.position, min(self.position + self.bsize, len(self.triples))
+        self.position = endpos
+
+        if offset + self.bsize > len(self.triples):
+            raise StopIteration
+
+        all_queries, all_passages, all_scores = [], [], []
+
+        for position in range(offset, endpos):
+            query, *pids = self.triples[position]
+            pids = pids[:self.nway]
+
+            query = self.queries[query]
+
+            try:
+                pids, scores = zipstar(pids)
+            except:
+                scores = []
+
+            passages = [self.collection[pid] for pid in pids]
+
+            all_queries.append(query)
+            all_passages.extend(passages)
+            all_scores.extend(scores)
+        
+        assert len(all_scores) in [0, len(all_passages)], len(all_scores)
+
+        return self.collate(all_queries, all_passages, all_scores)
+
+    def collate(self, queries, passages, scores):
+        assert len(queries) == self.bsize
+        assert len(passages) == self.nway * self.bsize
+
+        return self.tensorize_triples(queries, passages, scores, self.bsize // self.accumsteps, self.nway)
+
+    # def skip_to_batch(self, batch_idx, intended_batch_size):
+    #     Run.warn(f'Skipping to batch #{batch_idx} (with intended_batch_size = {intended_batch_size}) for training.')
+    #     self.position = intended_batch_size * batch_idx
diff --git a/colbert/training/rerank_batcher.py b/colbert/training/rerank_batcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..192dbc01d0df3dbbd945725e0fef3dbede0b3387
--- /dev/null
+++ b/colbert/training/rerank_batcher.py
@@ -0,0 +1,75 @@
+import os
+import ujson
+
+from functools import partial
+from colbert.infra.config.config import ColBERTConfig
+from colbert.utils.utils import flatten, print_message, zipstar
+from colbert.modeling.reranker.tokenizer import RerankerTokenizer
+
+from colbert.data.collection import Collection
+from colbert.data.queries import Queries
+from colbert.data.examples import Examples
+
+# from colbert.utils.runs import Run
+
+
+class RerankBatcher():
+    def __init__(self, config: ColBERTConfig, triples, queries, collection, rank=0, nranks=1):
+        self.bsize, self.accumsteps = config.bsize, config.accumsteps
+        self.nway = config.nway
+        
+        assert self.accumsteps == 1, "The tensorizer doesn't support larger accumsteps yet --- but it's easy to add."
+
+        self.tokenizer = RerankerTokenizer(total_maxlen=config.doc_maxlen, base=config.checkpoint)
+        self.position = 0
+
+        self.triples = Examples.cast(triples, nway=self.nway).tolist(rank, nranks)
+        self.queries = Queries.cast(queries)
+        self.collection = Collection.cast(collection)
+
+    def __iter__(self):
+        return self
+
+    def __len__(self):
+        return len(self.triples)
+
+    def __next__(self):
+        offset, endpos = self.position, min(self.position + self.bsize, len(self.triples))
+        self.position = endpos
+
+        if offset + self.bsize > len(self.triples):
+            raise StopIteration
+
+        all_queries, all_passages, all_scores = [], [], []
+
+        for position in range(offset, endpos):
+            query, *pids = self.triples[position]
+            pids = pids[:self.nway]
+
+            query = self.queries[query]
+
+            try:
+                pids, scores = zipstar(pids)
+            except:
+                scores = []
+
+            passages = [self.collection[pid] for pid in pids]
+
+            all_queries.append(query)
+            all_passages.extend(passages)
+            all_scores.extend(scores)
+        
+        assert len(all_scores) in [0, len(all_passages)], len(all_scores)
+
+        return self.collate(all_queries, all_passages, all_scores)
+
+    def collate(self, queries, passages, scores):
+        assert len(queries) == self.bsize
+        assert len(passages) == self.nway * self.bsize
+
+        queries = flatten([[query] * self.nway for query in queries])
+        return [(self.tokenizer.tensorize(queries, passages), scores)]
+
+    # def skip_to_batch(self, batch_idx, intended_batch_size):
+    #     Run.warn(f'Skipping to batch #{batch_idx} (with intended_batch_size = {intended_batch_size}) for training.')
+    #     self.position = intended_batch_size * batch_idx
diff --git a/colbert/training/training.py b/colbert/training/training.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3264fc1796aafb654ff3cc0176904aa5f02d68b
--- /dev/null
+++ b/colbert/training/training.py
@@ -0,0 +1,158 @@
+import time
+import torch
+import random
+import torch.nn as nn
+import numpy as np
+
+from transformers import AdamW, get_linear_schedule_with_warmup
+from colbert.infra import ColBERTConfig
+from colbert.training.rerank_batcher import RerankBatcher
+
+from colbert.utils.amp import MixedPrecisionManager
+from colbert.training.lazy_batcher import LazyBatcher
+from colbert.parameters import DEVICE
+
+from colbert.modeling.colbert import ColBERT
+from colbert.modeling.reranker.electra import ElectraReranker
+
+from colbert.utils.utils import print_message
+from colbert.training.utils import print_progress, manage_checkpoints
+
+
+
+def train(config: ColBERTConfig, triples, queries=None, collection=None):
+    config.checkpoint = config.checkpoint or 'bert-base-uncased'
+
+    if config.rank < 1:
+        config.help()
+
+    random.seed(12345)
+    np.random.seed(12345)
+    torch.manual_seed(12345)
+    torch.cuda.manual_seed_all(12345)
+
+    assert config.bsize % config.nranks == 0, (config.bsize, config.nranks)
+    config.bsize = config.bsize // config.nranks
+
+    print("Using config.bsize =", config.bsize, "(per process) and config.accumsteps =", config.accumsteps)
+
+    if collection is not None:
+        if config.reranker:
+            reader = RerankBatcher(config, triples, queries, collection, (0 if config.rank == -1 else config.rank), config.nranks)
+        else:
+            reader = LazyBatcher(config, triples, queries, collection, (0 if config.rank == -1 else config.rank), config.nranks)
+    else:
+        raise NotImplementedError()
+
+    if not config.reranker:
+        colbert = ColBERT(name=config.checkpoint, colbert_config=config)
+    else:
+        colbert = ElectraReranker.from_pretrained(config.checkpoint)
+
+    colbert = colbert.to(DEVICE)
+    colbert.train()
+
+    colbert = torch.nn.parallel.DistributedDataParallel(colbert, device_ids=[config.rank],
+                                                        output_device=config.rank,
+                                                        find_unused_parameters=True)
+
+    optimizer = AdamW(filter(lambda p: p.requires_grad, colbert.parameters()), lr=config.lr, eps=1e-8)
+    optimizer.zero_grad()
+
+    scheduler = None
+    if config.warmup is not None:
+        print(f"#> LR will use {config.warmup} warmup steps and linear decay over {config.maxsteps} steps.")
+        scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=config.warmup,
+                                                    num_training_steps=config.maxsteps)
+    
+    warmup_bert = config.warmup_bert
+    if warmup_bert is not None:
+        set_bert_grad(colbert, False)
+
+    amp = MixedPrecisionManager(config.amp)
+    labels = torch.zeros(config.bsize, dtype=torch.long, device=DEVICE)
+
+    start_time = time.time()
+    train_loss = None
+    train_loss_mu = 0.999
+
+    start_batch_idx = 0
+
+    # if config.resume:
+    #     assert config.checkpoint is not None
+    #     start_batch_idx = checkpoint['batch']
+
+    #     reader.skip_to_batch(start_batch_idx, checkpoint['arguments']['bsize'])
+
+    for batch_idx, BatchSteps in zip(range(start_batch_idx, config.maxsteps), reader):
+        if (warmup_bert is not None) and warmup_bert <= batch_idx:
+            set_bert_grad(colbert, True)
+            warmup_bert = None
+
+        this_batch_loss = 0.0
+
+        for batch in BatchSteps:
+            with amp.context():
+                try:
+                    queries, passages, target_scores = batch
+                    encoding = [queries, passages]
+                except:
+                    encoding, target_scores = batch
+                    encoding = [encoding.to(DEVICE)]
+
+                scores = colbert(*encoding)
+
+                if config.use_ib_negatives:
+                    scores, ib_loss = scores
+
+                scores = scores.view(-1, config.nway)
+
+                if len(target_scores) and not config.ignore_scores:
+                    target_scores = torch.tensor(target_scores).view(-1, config.nway).to(DEVICE)
+                    target_scores = target_scores * config.distillation_alpha
+                    target_scores = torch.nn.functional.log_softmax(target_scores, dim=-1)
+
+                    log_scores = torch.nn.functional.log_softmax(scores, dim=-1)
+                    loss = torch.nn.KLDivLoss(reduction='batchmean', log_target=True)(log_scores, target_scores)
+                else:
+                    loss = nn.CrossEntropyLoss()(scores, labels[:scores.size(0)])
+                
+                if config.use_ib_negatives:
+                    if config.rank < 1:
+                        print('\t\t\t\t', loss.item(), ib_loss.item())
+
+                    loss += ib_loss
+
+                loss = loss / config.accumsteps
+
+            if config.rank < 1:
+                print_progress(scores)
+
+            amp.backward(loss)
+
+            this_batch_loss += loss.item()
+
+        train_loss = this_batch_loss if train_loss is None else train_loss
+        train_loss = train_loss_mu * train_loss + (1 - train_loss_mu) * this_batch_loss
+
+        amp.step(colbert, optimizer, scheduler)
+
+        if config.rank < 1:
+            print_message(batch_idx, train_loss)
+            manage_checkpoints(config, colbert, optimizer, batch_idx+1, savepath=None)
+
+    if config.rank < 1:
+        print_message("#> Done with all triples!")
+        ckpt_path = manage_checkpoints(config, colbert, optimizer, batch_idx+1, savepath=None, consumed_all_triples=True)
+
+        return ckpt_path  # TODO: This should validate and return the best checkpoint, not just the last one.
+    
+
+
+def set_bert_grad(colbert, value):
+    try:
+        for p in colbert.bert.parameters():
+            assert p.requires_grad is (not value)
+            p.requires_grad = value
+    except AttributeError:
+        set_bert_grad(colbert.module, value)
diff --git a/colbert/training/utils.py b/colbert/training/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d3c7fe6d231a2ad2cd38f0c951369a9b4d473d8
--- /dev/null
+++ b/colbert/training/utils.py
@@ -0,0 +1,55 @@
+import os
+import torch
+
+# from colbert.utils.runs import Run
+from colbert.utils.utils import print_message, save_checkpoint
+from colbert.parameters import SAVED_CHECKPOINTS
+from colbert.infra.run import Run
+
+
+def print_progress(scores):
+    positive_avg, negative_avg = round(scores[:, 0].mean().item(), 2), round(scores[:, 1].mean().item(), 2)
+    print("#>>>   ", positive_avg, negative_avg, '\t\t|\t\t', positive_avg - negative_avg)
+
+
+def manage_checkpoints(args, colbert, optimizer, batch_idx, savepath=None, consumed_all_triples=False):
+    # arguments = dict(args)
+
+    # TODO: Call provenance() on the values that support it??
+
+    checkpoints_path = savepath or os.path.join(Run().path_, 'checkpoints')
+    name = None
+
+    try:
+        save = colbert.save
+    except:
+        save = colbert.module.save
+
+    if not os.path.exists(checkpoints_path):
+        os.makedirs(checkpoints_path)
+    
+    path_save = None
+
+    if consumed_all_triples or (batch_idx % 2000 == 0):
+        # name = os.path.join(path, "colbert.dnn")
+        # save_checkpoint(name, 0, batch_idx, colbert, optimizer, arguments)
+        path_save = os.path.join(checkpoints_path, "colbert")
+
+    if batch_idx in SAVED_CHECKPOINTS:
+        # name = os.path.join(path, "colbert-{}.dnn".format(batch_idx))
+        # save_checkpoint(name, 0, batch_idx, colbert, optimizer, arguments)
+        path_save = os.path.join(checkpoints_path, f"colbert-{batch_idx}")
+
+    if path_save:
+        print(f"#> Saving a checkpoint to {path_save} ..")
+
+        checkpoint = {}
+        checkpoint['batch'] = batch_idx
+        # checkpoint['epoch'] = 0
+        # checkpoint['model_state_dict'] = model.state_dict()
+        # checkpoint['optimizer_state_dict'] = optimizer.state_dict()
+        # checkpoint['arguments'] = arguments
+
+        save(path_save)
+
+    return path_save
diff --git a/colbert/utilities/annotate_em.py b/colbert/utilities/annotate_em.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2c4cea6126a1eab657057ccadd9c5c8724f84a2
--- /dev/null
+++ b/colbert/utilities/annotate_em.py
@@ -0,0 +1,113 @@
+
+import os
+import sys
+import git
+import tqdm
+import ujson
+import random
+
+from argparse import ArgumentParser
+from multiprocessing import Pool
+
+from colbert.utils.utils import groupby_first_item, print_message
+from utility.utils.qa_loaders import load_qas_, load_collection_
+from utility.utils.save_metadata import format_metadata, get_metadata
+from utility.evaluate.annotate_EM_helpers import *
+
+from colbert.infra.run import Run
+from colbert.data.collection import Collection
+from colbert.data.ranking import Ranking
+
+
+class AnnotateEM:
+    def __init__(self, collection, qas):
+        # TODO: These should just be Queries! But Queries needs to support looking up answers as qid2answers below.
+        qas = load_qas_(qas)
+        collection = Collection.cast(collection)  # .tolist() #load_collection_(collection, retain_titles=True)
+
+        self.parallel_pool = Pool(30)
+
+        print_message('#> Tokenize the answers in the Q&As in parallel...')
+        qas = list(self.parallel_pool.map(tokenize_all_answers, qas))
+
+        qid2answers = {qid: tok_answers for qid, _, tok_answers in qas}
+        assert len(qas) == len(qid2answers), (len(qas), len(qid2answers))
+
+        self.qas, self.collection = qas, collection
+        self.qid2answers = qid2answers
+
+    def annotate(self, ranking):
+        rankings = Ranking.cast(ranking)
+
+        # print(len(rankings), rankings[0])
+
+        print_message('#> Lookup passages from PIDs...')
+        expanded_rankings = [(qid, pid, rank, self.collection[pid], self.qid2answers[qid])
+                             for qid, pid, rank, *_ in rankings.tolist()]
+
+        print_message('#> Assign labels in parallel...')
+        labeled_rankings = list(self.parallel_pool.map(assign_label_to_passage, enumerate(expanded_rankings)))
+
+        # Dump output.
+        self.qid2rankings = groupby_first_item(labeled_rankings)
+
+        self.num_judged_queries, self.num_ranked_queries = check_sizes(self.qid2answers, self.qid2rankings)
+
+        # Evaluation metrics and depths.
+        self.success, self.counts = self._compute_labels(self.qid2answers, self.qid2rankings)
+
+        print(rankings.provenance(), self.success)
+
+        return Ranking(data=self.qid2rankings, provenance=("AnnotateEM", rankings.provenance()))
+
+    def _compute_labels(self, qid2answers, qid2rankings):
+        cutoffs = [1, 5, 10, 20, 30, 50, 100, 1000, 'all']
+        success = {cutoff: 0.0 for cutoff in cutoffs}
+        counts = {cutoff: 0.0 for cutoff in cutoffs}
+
+        for qid in qid2answers:
+            if qid not in qid2rankings:
+                continue
+
+            prev_rank = 0  # ranks should start at one (i.e., and not zero)
+            labels = []
+
+            for pid, rank, label in qid2rankings[qid]:
+                assert rank == prev_rank+1, (qid, pid, (prev_rank, rank))
+                prev_rank = rank
+
+                labels.append(label)
+
+            for cutoff in cutoffs:
+                if cutoff != 'all':
+                    success[cutoff] += sum(labels[:cutoff]) > 0
+                    counts[cutoff] += sum(labels[:cutoff])
+                else:
+                    success[cutoff] += sum(labels) > 0
+                    counts[cutoff] += sum(labels)
+
+        return success, counts
+
+    def save(self, new_path):
+        print_message("#> Dumping output to", new_path, "...")
+
+        Ranking(data=self.qid2rankings).save(new_path)
+
+        # Dump metrics.
+        with Run().open(f'{new_path}.metrics', 'w') as f:
+            d = {'num_ranked_queries': self.num_ranked_queries, 'num_judged_queries': self.num_judged_queries}
+
+            extra = '__WARNING' if self.num_judged_queries != self.num_ranked_queries else ''
+            d[f'success{extra}'] = {k: v / self.num_judged_queries for k, v in self.success.items()}
+            d[f'counts{extra}'] = {k: v / self.num_judged_queries for k, v in self.counts.items()}
+            # d['arguments'] = get_metadata(args)  # TODO: Need arguments...
+
+            f.write(format_metadata(d) + '\n')
+
+
+if __name__ == '__main__':
+    r = sys.argv[2]
+
+    a = AnnotateEM(collection='/dfs/scratch0/okhattab/OpenQA/collection.tsv',
+                   qas=sys.argv[1])
+    a.annotate(ranking=r)
diff --git a/colbert/utilities/create_triples.py b/colbert/utilities/create_triples.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ed2686a87d5687eb715e392c6f9979ef67a4470
--- /dev/null
+++ b/colbert/utilities/create_triples.py
@@ -0,0 +1,65 @@
+import random
+from colbert.infra.provenance import Provenance
+
+from utility.utils.save_metadata import save_metadata
+from utility.supervision.triples import sample_for_query
+
+from colbert.utils.utils import print_message
+
+from colbert.data.ranking import Ranking
+from colbert.data.examples import Examples
+
+MAX_NUM_TRIPLES = 40_000_000
+
+
+class Triples:
+    def __init__(self, ranking, seed=12345):
+        random.seed(seed)  # TODO: Use internal RNG instead..
+        self.seed = seed
+
+        ranking = Ranking.cast(ranking)
+        self.ranking_provenance = ranking.provenance()
+        self.qid2rankings = ranking.todict()
+
+    def create(self, positives, depth):
+        assert all(len(x) == 2 for x in positives)
+        assert all(maxBest <= maxDepth for maxBest, maxDepth in positives), positives
+
+        self.positives = positives
+        self.depth = depth
+
+        Triples = []
+        NonEmptyQIDs = 0
+
+        for processing_idx, qid in enumerate(self.qid2rankings):
+            l = sample_for_query(qid, self.qid2rankings[qid], positives, depth, False, None)
+            NonEmptyQIDs += (len(l) > 0)
+            Triples.extend(l)
+
+            if processing_idx % (10_000) == 0:
+                print_message(f"#> Done with {processing_idx+1} questions!\t\t "
+                              f"{str(len(Triples) / 1000)}k triples for {NonEmptyQIDs} unqiue QIDs.")
+
+        print_message(f"#> Sub-sample the triples (if > {MAX_NUM_TRIPLES})..")
+        print_message(f"#> len(Triples) = {len(Triples)}")
+
+        if len(Triples) > MAX_NUM_TRIPLES:
+            Triples = random.sample(Triples, MAX_NUM_TRIPLES)
+
+        ### Prepare the triples ###
+        print_message("#> Shuffling the triples...")
+        random.shuffle(Triples)
+
+        self.Triples = Examples(data=Triples)
+
+        return Triples
+
+    def save(self, new_path):
+        provenance = Provenance()
+        provenance.source = 'Triples::create'
+        provenance.seed = self.seed
+        provenance.positives = self.positives
+        provenance.depth = self.depth
+        provenance.ranking = self.ranking_provenance
+
+        Examples(data=self.Triples, provenance=provenance).save(new_path)
diff --git a/colbert/utilities/minicorpus.py b/colbert/utilities/minicorpus.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b11eb7836f5ede25f0eb6c037177917f032ccf3
--- /dev/null
+++ b/colbert/utilities/minicorpus.py
@@ -0,0 +1,66 @@
+import os
+import random
+
+from colbert.utils.utils import create_directory
+
+from colbert.data.collection import Collection
+from colbert.data.queries import Queries
+from colbert.data.ranking import Ranking
+
+
+def sample_minicorpus(name, factor, topk=30, maxdev=3000):
+    """
+    Factor:
+        * nano=1
+        * micro=10
+        * mini=100
+        * small=100 with topk=100
+        * medium=150 with topk=300
+    """
+
+    random.seed(12345)
+
+    # Load collection
+    collection = Collection(path='/dfs/scratch0/okhattab/OpenQA/collection.tsv')
+
+    # Load train and dev queries
+    qas_train = Queries(path='/dfs/scratch0/okhattab/OpenQA/NQ/train/qas.json').qas()
+    qas_dev = Queries(path='/dfs/scratch0/okhattab/OpenQA/NQ/dev/qas.json').qas()
+
+    # Load train and dev C3 rankings
+    ranking_train = Ranking(path='/dfs/scratch0/okhattab/OpenQA/NQ/train/rankings/C3.tsv.annotated').todict()
+    ranking_dev = Ranking(path='/dfs/scratch0/okhattab/OpenQA/NQ/dev/rankings/C3.tsv.annotated').todict()
+
+    # Sample NT and ND queries from each, keep only the top-k passages for those
+    sample_train = random.sample(list(qas_train.keys()), min(len(qas_train.keys()), 300*factor))
+    sample_dev = random.sample(list(qas_dev.keys()), min(len(qas_dev.keys()), maxdev, 30*factor))
+
+    train_pids = [pid for qid in sample_train for qpids in ranking_train[qid][:topk] for pid in qpids]
+    dev_pids = [pid for qid in sample_dev for qpids in ranking_dev[qid][:topk] for pid in qpids]
+
+    sample_pids = sorted(list(set(train_pids + dev_pids)))
+    print(f'len(sample_pids) = {len(sample_pids)}')
+
+    # Save the new query sets: train and dev
+    ROOT = f'/future/u/okhattab/root/unit/data/NQ-{name}'
+
+    create_directory(os.path.join(ROOT, 'train'))
+    create_directory(os.path.join(ROOT, 'dev'))
+
+    new_train = Queries(data={qid: qas_train[qid] for qid in sample_train})
+    new_train.save(os.path.join(ROOT, 'train/questions.tsv'))
+    new_train.save_qas(os.path.join(ROOT, 'train/qas.json'))
+
+    new_dev = Queries(data={qid: qas_dev[qid] for qid in sample_dev})
+    new_dev.save(os.path.join(ROOT, 'dev/questions.tsv'))
+    new_dev.save_qas(os.path.join(ROOT, 'dev/qas.json'))
+
+    # Save the new collection
+    print(f"Saving to {os.path.join(ROOT, 'collection.tsv')}")
+    Collection(data=[collection[pid] for pid in sample_pids]).save(os.path.join(ROOT, 'collection.tsv'))
+
+    print('#> Done!')
+
+
+if __name__ == '__main__':
+    sample_minicorpus('medium', 150, topk=300)
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diff --git a/colbert/utils/amp.py b/colbert/utils/amp.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a0dadc141cde1056398b24430bc49ad76ef406c
--- /dev/null
+++ b/colbert/utils/amp.py
@@ -0,0 +1,37 @@
+import torch
+
+from contextlib import contextmanager
+from colbert.utils.utils import NullContextManager
+
+
+class MixedPrecisionManager():
+    def __init__(self, activated):
+        self.activated = activated
+
+        if self.activated:
+            self.scaler = torch.cuda.amp.GradScaler()
+
+    def context(self):
+        return torch.cuda.amp.autocast() if self.activated else NullContextManager()
+
+    def backward(self, loss):
+        if self.activated:
+            self.scaler.scale(loss).backward()
+        else:
+            loss.backward()
+
+    def step(self, colbert, optimizer, scheduler=None):
+        if self.activated:
+            self.scaler.unscale_(optimizer)
+            torch.nn.utils.clip_grad_norm_(colbert.parameters(), 2.0, error_if_nonfinite=False)
+
+            self.scaler.step(optimizer)
+            self.scaler.update()
+        else:
+            torch.nn.utils.clip_grad_norm_(colbert.parameters(), 2.0)
+            optimizer.step()
+        
+        if scheduler is not None:
+            scheduler.step()
+
+        optimizer.zero_grad()
diff --git a/colbert/utils/distributed.py b/colbert/utils/distributed.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b7bb720ccfdcdaf3c0532f2498f91500e9a9249
--- /dev/null
+++ b/colbert/utils/distributed.py
@@ -0,0 +1,37 @@
+import os
+import random
+import torch
+import numpy as np
+
+ALREADY_INITALIZED = False
+
+# TODO: Consider torch.distributed.is_initialized() instead
+
+
+def init(rank):
+    nranks = 'WORLD_SIZE' in os.environ and int(os.environ['WORLD_SIZE'])
+    nranks = max(1, nranks)
+    is_distributed = (nranks > 1) or ('WORLD_SIZE' in os.environ)
+
+    global ALREADY_INITALIZED
+    if ALREADY_INITALIZED:
+        return nranks, is_distributed
+
+    ALREADY_INITALIZED = True
+
+    if is_distributed and torch.cuda.is_available():
+        num_gpus = torch.cuda.device_count()
+        print(f'nranks = {nranks} \t num_gpus = {num_gpus} \t device={rank % num_gpus}')
+
+        torch.cuda.set_device(rank % num_gpus)
+        torch.distributed.init_process_group(backend='nccl', init_method='env://')
+
+    return nranks, is_distributed
+
+
+def barrier(rank):
+    nranks = 'WORLD_SIZE' in os.environ and int(os.environ['WORLD_SIZE'])
+    nranks = max(1, nranks)
+
+    if rank >= 0 and nranks > 1:
+        torch.distributed.barrier(device_ids=[rank % torch.cuda.device_count()])
diff --git a/colbert/utils/logging.py b/colbert/utils/logging.py
new file mode 100644
index 0000000000000000000000000000000000000000..f08e85465e642305fb6fbeb39144aedabfe4564c
--- /dev/null
+++ b/colbert/utils/logging.py
@@ -0,0 +1,100 @@
+import os
+import sys
+import ujson
+# import mlflow
+import traceback
+
+# from torch.utils.tensorboard import SummaryWriter
+from colbert.utils.utils import print_message, create_directory
+
+
+class Logger():
+    def __init__(self, rank, run):
+        self.rank = rank
+        self.is_main = self.rank in [-1, 0]
+        self.run = run
+        self.logs_path = os.path.join(self.run.path, "logs/")
+
+        if self.is_main:
+            # self._init_mlflow()
+            # self.initialized_tensorboard = False
+            create_directory(self.logs_path)
+
+    # def _init_mlflow(self):
+    #     mlflow.set_tracking_uri('file://' + os.path.join(self.run.experiments_root, "logs/mlruns/"))
+    #     mlflow.set_experiment('/'.join([self.run.experiment, self.run.script]))
+        
+    #     mlflow.set_tag('experiment', self.run.experiment)
+    #     mlflow.set_tag('name', self.run.name)
+    #     mlflow.set_tag('path', self.run.path)
+
+    # def _init_tensorboard(self):
+    #     root = os.path.join(self.run.experiments_root, "logs/tensorboard/")
+    #     logdir = '__'.join([self.run.experiment, self.run.script, self.run.name])
+    #     logdir = os.path.join(root, logdir)
+
+    #     self.writer = SummaryWriter(log_dir=logdir)
+    #     self.initialized_tensorboard = True
+
+    def _log_exception(self, etype, value, tb):
+        if not self.is_main:
+            return
+
+        output_path = os.path.join(self.logs_path, 'exception.txt')
+        trace = ''.join(traceback.format_exception(etype, value, tb)) + '\n'
+        print_message(trace, '\n\n')
+
+        self.log_new_artifact(output_path, trace)
+
+    def _log_all_artifacts(self):
+        if not self.is_main:
+            return
+
+        # mlflow.log_artifacts(self.logs_path)
+
+    def _log_args(self, args):
+        if not self.is_main:
+            return
+
+        # for key in vars(args):
+        #     value = getattr(args, key)
+        #     if type(value) in [int, float, str, bool]:
+        #         mlflow.log_param(key, value)
+
+        # with open(os.path.join(self.logs_path, 'args.json'), 'w') as output_metadata:
+        #     # TODO: Call provenance() on the values that support it
+        #     ujson.dump(args.input_arguments.__dict__, output_metadata, indent=4)
+        #     output_metadata.write('\n')
+
+        with open(os.path.join(self.logs_path, 'args.txt'), 'w') as output_metadata:
+            output_metadata.write(' '.join(sys.argv) + '\n')
+
+    def log_metric(self, name, value, step, log_to_mlflow=True):
+        if not self.is_main:
+            return
+
+        # if not self.initialized_tensorboard:
+        #     self._init_tensorboard()
+
+        # if log_to_mlflow:
+        #     mlflow.log_metric(name, value, step=step)
+        # self.writer.add_scalar(name, value, step)
+
+    def log_new_artifact(self, path, content):
+        with open(path, 'w') as f:
+            f.write(content)
+
+        # mlflow.log_artifact(path)
+
+    def warn(self, *args):
+        msg = print_message('[WARNING]', '\t', *args)
+
+        with open(os.path.join(self.logs_path, 'warnings.txt'), 'a') as output_metadata:
+            output_metadata.write(msg + '\n\n\n')
+
+    def info_all(self, *args):
+        print_message('[' + str(self.rank) + ']', '\t', *args)
+
+    def info(self, *args):
+        if self.is_main:
+            print_message(*args)
diff --git a/colbert/utils/parser.py b/colbert/utils/parser.py
new file mode 100644
index 0000000000000000000000000000000000000000..63cbf1e1f4702453ffed5561e9b2621846c13c8f
--- /dev/null
+++ b/colbert/utils/parser.py
@@ -0,0 +1,119 @@
+import os
+import copy
+import faiss
+
+from argparse import ArgumentParser
+
+import colbert.utils.distributed as distributed
+from colbert.utils.runs import Run
+from colbert.utils.utils import print_message, timestamp, create_directory
+
+
+class Arguments():
+    def __init__(self, description):
+        self.parser = ArgumentParser(description=description)
+        self.checks = []
+
+        self.add_argument('--root', dest='root', default='experiments')
+        self.add_argument('--experiment', dest='experiment', default='dirty')
+        self.add_argument('--run', dest='run', default=Run.name)
+
+        self.add_argument('--local_rank', dest='rank', default=-1, type=int)
+
+    def add_model_parameters(self):
+        # Core Arguments
+        self.add_argument('--similarity', dest='similarity', default='cosine', choices=['cosine', 'l2'])
+        self.add_argument('--dim', dest='dim', default=128, type=int)
+        self.add_argument('--query_maxlen', dest='query_maxlen', default=32, type=int)
+        self.add_argument('--doc_maxlen', dest='doc_maxlen', default=180, type=int)
+
+        # Filtering-related Arguments
+        self.add_argument('--mask-punctuation', dest='mask_punctuation', default=False, action='store_true')
+
+    def add_model_training_parameters(self):
+        # NOTE: Providing a checkpoint is one thing, --resume is another, --resume_optimizer is yet another.
+        self.add_argument('--resume', dest='resume', default=False, action='store_true')
+        self.add_argument('--resume_optimizer', dest='resume_optimizer', default=False, action='store_true')
+        self.add_argument('--checkpoint', dest='checkpoint', default=None, required=False)
+
+        self.add_argument('--lr', dest='lr', default=3e-06, type=float)
+        self.add_argument('--maxsteps', dest='maxsteps', default=400000, type=int)
+        self.add_argument('--bsize', dest='bsize', default=32, type=int)
+        self.add_argument('--accum', dest='accumsteps', default=2, type=int)
+        self.add_argument('--amp', dest='amp', default=False, action='store_true')
+
+    def add_model_inference_parameters(self):
+        self.add_argument('--checkpoint', dest='checkpoint', required=True)
+        self.add_argument('--bsize', dest='bsize', default=128, type=int)
+        self.add_argument('--amp', dest='amp', default=False, action='store_true')
+
+    def add_training_input(self):
+        self.add_argument('--triples', dest='triples', required=True)
+        self.add_argument('--queries', dest='queries', default=None)
+        self.add_argument('--collection', dest='collection', default=None)
+
+        def check_training_input(args):
+            assert (args.collection is None) == (args.queries is None), \
+                "For training, both (or neither) --collection and --queries must be supplied." \
+                "If neither is supplied, the --triples file must contain texts (not PIDs)."
+
+        self.checks.append(check_training_input)
+
+    def add_ranking_input(self):
+        self.add_argument('--queries', dest='queries', default=None)
+        self.add_argument('--collection', dest='collection', default=None)
+        self.add_argument('--qrels', dest='qrels', default=None)
+
+    def add_reranking_input(self):
+        self.add_ranking_input()
+        self.add_argument('--topk', dest='topK', required=True)
+        self.add_argument('--shortcircuit', dest='shortcircuit', default=False, action='store_true')
+
+    def add_indexing_input(self):
+        self.add_argument('--collection', dest='collection', required=True)
+        self.add_argument('--index_root', dest='index_root', required=True)
+        self.add_argument('--index_name', dest='index_name', required=True)
+
+    def add_compressed_index_input(self):
+        self.add_argument('--compression_level', dest='compression_level',
+                          choices=[1, 2], type=int, default=None)
+
+
+    def add_index_use_input(self):
+        self.add_argument('--index_root', dest='index_root', required=True)
+        self.add_argument('--index_name', dest='index_name', required=True)
+        self.add_argument('--partitions', dest='partitions', default=None, type=int, required=False)
+
+    def add_retrieval_input(self):
+        self.add_index_use_input()
+        self.add_argument('--nprobe', dest='nprobe', default=10, type=int)
+        self.add_argument('--retrieve_only', dest='retrieve_only', default=False, action='store_true')
+
+    def add_argument(self, *args, **kw_args):
+        return self.parser.add_argument(*args, **kw_args)
+
+    def check_arguments(self, args):
+        for check in self.checks:
+            check(args)
+
+    def parse(self):
+        args = self.parser.parse_args()
+        self.check_arguments(args)
+
+        args.input_arguments = copy.deepcopy(args)
+
+        args.nranks, args.distributed = distributed.init(args.rank)
+
+        args.nthreads = int(max(os.cpu_count(), faiss.omp_get_max_threads()) * 0.8)
+        args.nthreads = max(1, args.nthreads // args.nranks)
+
+        if args.nranks > 1:
+            print_message(f"#> Restricting number of threads for FAISS to {args.nthreads} per process",
+                          condition=(args.rank == 0))
+            faiss.omp_set_num_threads(args.nthreads)
+
+        Run.init(args.rank, args.root, args.experiment, args.run)
+        Run._log_args(args)
+        Run.info(args.input_arguments.__dict__, '\n')
+
+        return args
diff --git a/colbert/utils/runs.py b/colbert/utils/runs.py
new file mode 100644
index 0000000000000000000000000000000000000000..43eebeee4255d96ec4e30671d7539ec8b8e1b084
--- /dev/null
+++ b/colbert/utils/runs.py
@@ -0,0 +1,104 @@
+import os
+import sys
+import time
+import __main__
+import traceback
+# import mlflow
+
+import colbert.utils.distributed as distributed
+
+from contextlib import contextmanager
+from colbert.utils.logging import Logger
+from colbert.utils.utils import timestamp, create_directory, print_message
+
+
+class _RunManager():
+    def __init__(self):
+        self.experiments_root = None
+        self.experiment = None
+        self.path = None
+        self.script = self._get_script_name()
+        self.name = self._generate_default_run_name()
+        self.original_name = self.name
+        self.exit_status = 'FINISHED'
+
+        self._logger = None
+        self.start_time = time.time()
+
+    def init(self, rank, root, experiment, name):
+        assert '/' not in experiment, experiment
+        assert '/' not in name, name
+
+        self.experiments_root = os.path.abspath(root)
+        self.experiment = experiment
+        self.name = name
+        self.path = os.path.join(self.experiments_root, self.experiment, self.script, self.name)
+
+        if rank < 1:
+            if os.path.exists(self.path):
+                print('\n\n')
+                print_message("It seems that ", self.path, " already exists.")
+                print_message("Do you want to overwrite it? \t yes/no \n")
+
+                # TODO: This should timeout and exit (i.e., fail) given no response for 60 seconds.
+
+                response = input()
+                if response.strip() != 'yes':
+                    assert not os.path.exists(self.path), self.path
+            else:
+                create_directory(self.path)
+
+        distributed.barrier(rank)
+
+        self._logger = Logger(rank, self)
+        self._log_args = self._logger._log_args
+        self.warn = self._logger.warn
+        self.info = self._logger.info
+        self.info_all = self._logger.info_all
+        self.log_metric = self._logger.log_metric
+        self.log_new_artifact = self._logger.log_new_artifact
+
+    def _generate_default_run_name(self):
+        return timestamp()
+
+    def _get_script_name(self):
+        return os.path.basename(__main__.__file__) if '__file__' in dir(__main__) else 'none'
+
+    @contextmanager
+    def context(self, consider_failed_if_interrupted=True):
+        try:
+            yield
+
+        except KeyboardInterrupt as ex:
+            print('\n\nInterrupted\n\n')
+            self._logger._log_exception(ex.__class__, ex, ex.__traceback__)
+            self._logger._log_all_artifacts()
+
+            if consider_failed_if_interrupted:
+                self.exit_status = 'KILLED'  # mlflow.entities.RunStatus.KILLED
+
+            sys.exit(128 + 2)
+
+        except Exception as ex:
+            self._logger._log_exception(ex.__class__, ex, ex.__traceback__)
+            self._logger._log_all_artifacts()
+
+            self.exit_status = 'FAILED'  # mlflow.entities.RunStatus.FAILED
+
+            raise ex
+
+        finally:
+            total_seconds = str(time.time() - self.start_time) + '\n'
+            original_name = str(self.original_name)
+            name = str(self.name)
+
+            self.log_new_artifact(os.path.join(self._logger.logs_path, 'elapsed.txt'), total_seconds)
+            self.log_new_artifact(os.path.join(self._logger.logs_path, 'name.original.txt'), original_name)
+            self.log_new_artifact(os.path.join(self._logger.logs_path, 'name.txt'), name)
+
+            self._logger._log_all_artifacts()
+
+            # mlflow.end_run(status=self.exit_status)
+
+
+Run = _RunManager()
diff --git a/colbert/utils/utils.py b/colbert/utils/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4349b273b5a96de478aa01214c607405d4cfa99
--- /dev/null
+++ b/colbert/utils/utils.py
@@ -0,0 +1,310 @@
+import os
+import tqdm
+import torch
+import datetime
+import itertools
+
+from multiprocessing import Pool
+from collections import OrderedDict, defaultdict
+
+
+def print_message(*s, condition=True, pad=False):
+    s = ' '.join([str(x) for x in s])
+    msg = "[{}] {}".format(datetime.datetime.now().strftime("%b %d, %H:%M:%S"), s)
+
+    if condition:
+        msg = msg if not pad else f'\n{msg}\n'
+        print(msg, flush=True)
+
+
+    return msg
+
+
+def timestamp(daydir=False):
+    format_str = f"%Y-%m{'/' if daydir else '-'}%d{'/' if daydir else '_'}%H.%M.%S"
+    result = datetime.datetime.now().strftime(format_str)
+    return result
+
+
+def file_tqdm(file):
+    print(f"#> Reading {file.name}")
+
+    with tqdm.tqdm(total=os.path.getsize(file.name) / 1024.0 / 1024.0, unit="MiB") as pbar:
+        for line in file:
+            yield line
+            pbar.update(len(line) / 1024.0 / 1024.0)
+
+        pbar.close()
+
+
+def torch_load_dnn(path):
+    if path.startswith("http:") or path.startswith("https:"):
+        dnn = torch.hub.load_state_dict_from_url(path, map_location='cpu')
+    else:
+        dnn = torch.load(path, map_location='cpu')
+    
+    return dnn
+
+def save_checkpoint(path, epoch_idx, mb_idx, model, optimizer, arguments=None):
+    print(f"#> Saving a checkpoint to {path} ..")
+
+    if hasattr(model, 'module'):
+        model = model.module  # extract model from a distributed/data-parallel wrapper
+
+    checkpoint = {}
+    checkpoint['epoch'] = epoch_idx
+    checkpoint['batch'] = mb_idx
+    checkpoint['model_state_dict'] = model.state_dict()
+    checkpoint['optimizer_state_dict'] = optimizer.state_dict()
+    checkpoint['arguments'] = arguments
+
+    torch.save(checkpoint, path)
+
+
+def load_checkpoint(path, model, checkpoint=None, optimizer=None, do_print=True):
+    if do_print:
+        print_message("#> Loading checkpoint", path, "..")
+
+    if checkpoint is None:
+        checkpoint = load_checkpoint_raw(path)
+
+    try:
+        model.load_state_dict(checkpoint['model_state_dict'])
+    except:
+        print_message("[WARNING] Loading checkpoint with strict=False")
+        model.load_state_dict(checkpoint['model_state_dict'], strict=False)
+
+    if optimizer:
+        optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
+
+    if do_print:
+        print_message("#> checkpoint['epoch'] =", checkpoint['epoch'])
+        print_message("#> checkpoint['batch'] =", checkpoint['batch'])
+
+    return checkpoint
+
+
+def load_checkpoint_raw(path):
+    if path.startswith("http:") or path.startswith("https:"):
+        checkpoint = torch.hub.load_state_dict_from_url(path, map_location='cpu')
+    else:
+        checkpoint = torch.load(path, map_location='cpu')
+
+    state_dict = checkpoint['model_state_dict']
+    new_state_dict = OrderedDict()
+    for k, v in state_dict.items():
+        name = k
+        if k[:7] == 'module.':
+            name = k[7:]
+        new_state_dict[name] = v
+
+    checkpoint['model_state_dict'] = new_state_dict
+
+    return checkpoint
+
+
+def create_directory(path):
+    if os.path.exists(path):
+        print('\n')
+        print_message("#> Note: Output directory", path, 'already exists\n\n')
+    else:
+        print('\n')
+        print_message("#> Creating directory", path, '\n\n')
+        os.makedirs(path)
+
+# def batch(file, bsize):
+#     while True:
+#         L = [ujson.loads(file.readline()) for _ in range(bsize)]
+#         yield L
+#     return
+
+
+def f7(seq):
+    """
+    Source: https://stackoverflow.com/a/480227/1493011
+    """
+
+    seen = set()
+    return [x for x in seq if not (x in seen or seen.add(x))]
+
+
+def batch(group, bsize, provide_offset=False):
+    offset = 0
+    while offset < len(group):
+        L = group[offset: offset + bsize]
+        yield ((offset, L) if provide_offset else L)
+        offset += len(L)
+    return
+
+
+class dotdict(dict):
+    """
+    dot.notation access to dictionary attributes
+    Credit: derek73 @ https://stackoverflow.com/questions/2352181
+    """
+    __getattr__ = dict.__getitem__
+    __setattr__ = dict.__setitem__
+    __delattr__ = dict.__delitem__
+
+
+class dotdict_lax(dict):
+    __getattr__ = dict.get
+    __setattr__ = dict.__setitem__
+    __delattr__ = dict.__delitem__
+
+
+def flatten(L):
+    # return [x for y in L for x in y]
+
+    result = []
+    for _list in L:
+        result += _list
+
+    return result
+
+
+def zipstar(L, lazy=False):
+    """
+    A much faster A, B, C = zip(*[(a, b, c), (a, b, c), ...])
+    May return lists or tuples.
+    """
+
+    if len(L) == 0:
+        return L
+
+    width = len(L[0])
+
+    if width < 100:
+        return [[elem[idx] for elem in L] for idx in range(width)]
+
+    L = zip(*L)
+
+    return L if lazy else list(L)
+
+
+def zip_first(L1, L2):
+    length = len(L1) if type(L1) in [tuple, list] else None
+
+    L3 = list(zip(L1, L2))
+
+    assert length in [None, len(L3)], "zip_first() failure: length differs!"
+
+    return L3
+
+
+def int_or_float(val):
+    if '.' in val:
+        return float(val)
+        
+    return int(val)
+
+def load_ranking(path, types=None, lazy=False):
+    print_message(f"#> Loading the ranked lists from {path} ..")
+
+    try:
+        lists = torch.load(path)
+        lists = zipstar([l.tolist() for l in tqdm.tqdm(lists)], lazy=lazy)
+    except:
+        if types is None:
+            types = itertools.cycle([int_or_float])
+
+        with open(path) as f:
+            lists = [[typ(x) for typ, x in zip_first(types, line.strip().split('\t'))]
+                     for line in file_tqdm(f)]
+
+    return lists
+
+
+def save_ranking(ranking, path):
+    lists = zipstar(ranking)
+    lists = [torch.tensor(l) for l in lists]
+
+    torch.save(lists, path)
+
+    return lists
+
+
+def groupby_first_item(lst):
+    groups = defaultdict(list)
+
+    for first, *rest in lst:
+        rest = rest[0] if len(rest) == 1 else rest
+        groups[first].append(rest)
+
+    return groups
+
+
+def process_grouped_by_first_item(lst):
+    """
+        Requires items in list to already be grouped by first item.
+    """
+
+    groups = defaultdict(list)
+
+    started = False
+    last_group = None
+
+    for first, *rest in lst:
+        rest = rest[0] if len(rest) == 1 else rest
+
+        if started and first != last_group:
+            yield (last_group, groups[last_group])
+            assert first not in groups, f"{first} seen earlier --- violates precondition."
+
+        groups[first].append(rest)
+
+        last_group = first
+        started = True
+
+    return groups
+
+
+def grouper(iterable, n, fillvalue=None):
+    """
+    Collect data into fixed-length chunks or blocks
+        Example: grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx"
+        Source: https://docs.python.org/3/library/itertools.html#itertools-recipes
+    """
+
+    args = [iter(iterable)] * n
+    return itertools.zip_longest(*args, fillvalue=fillvalue)
+
+
+def lengths2offsets(lengths):
+    offset = 0
+
+    for length in lengths:
+        yield (offset, offset + length)
+        offset += length
+
+    return
+
+
+# see https://stackoverflow.com/a/45187287
+class NullContextManager(object):
+    def __init__(self, dummy_resource=None):
+        self.dummy_resource = dummy_resource
+    def __enter__(self):
+        return self.dummy_resource
+    def __exit__(self, *args):
+        pass
+
+
+def load_batch_backgrounds(args, qids):
+    if args.qid2backgrounds is None:
+        return None
+
+    qbackgrounds = []
+
+    for qid in qids:
+        back = args.qid2backgrounds[qid]
+
+        if len(back) and type(back[0]) == int:
+            x = [args.collection[pid] for pid in back]
+        else:
+            x = [args.collectionX.get(pid, '') for pid in back]
+
+        x = ' [SEP] '.join(x)
+        qbackgrounds.append(x)
+    
+    return qbackgrounds
diff --git a/experiments/medqa/indexes/medqa_idx/0.codes.pt b/experiments/medqa/indexes/medqa_idx/0.codes.pt
new file mode 100644
index 0000000000000000000000000000000000000000..8520aef00884184b1f7280478150ed6186c5982c
--- /dev/null
+++ b/experiments/medqa/indexes/medqa_idx/0.codes.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:91ddc0c4d6feb280fa36ee926478d150da6ca6a8fe5252e37821c7bab49dfc7b
+size 9963947
diff --git a/experiments/medqa/indexes/medqa_idx/0.metadata.json b/experiments/medqa/indexes/medqa_idx/0.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..1a45896ca75382ca306caca475479f535c90baaa
--- /dev/null
+++ b/experiments/medqa/indexes/medqa_idx/0.metadata.json
@@ -0,0 +1,6 @@
+{
+    "passage_offset": 0,
+    "num_passages": 25000,
+    "num_embeddings": 2490801,
+    "embedding_offset": 0
+}
diff --git a/experiments/medqa/indexes/medqa_idx/0.residuals.pt b/experiments/medqa/indexes/medqa_idx/0.residuals.pt
new file mode 100644
index 0000000000000000000000000000000000000000..c1889a062ec76c05d4c4291ff6fe9892e605a6ae
--- /dev/null
+++ b/experiments/medqa/indexes/medqa_idx/0.residuals.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dd36aaadb03e71244b6cdc910a605f2be1d58548c6e4bb00d48c6252faa4276b
+size 79706347
diff --git a/experiments/medqa/indexes/medqa_idx/1.codes.pt b/experiments/medqa/indexes/medqa_idx/1.codes.pt
new file mode 100644
index 0000000000000000000000000000000000000000..7b34970190f3498ce4c70b212c90d1fbda5a893c
--- /dev/null
+++ b/experiments/medqa/indexes/medqa_idx/1.codes.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9ec5bfbfabd136e999eb078463f373f62fc8374d584939a55a8e6030fd3df308
+size 10058411
diff --git a/experiments/medqa/indexes/medqa_idx/1.metadata.json b/experiments/medqa/indexes/medqa_idx/1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..a4de3a93aca70142c97118579eb0df967ae2a6f7
--- /dev/null
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\ No newline at end of file
diff --git a/experiments/medqa/indexes/medqa_idx/doclens.1.json b/experiments/medqa/indexes/medqa_idx/doclens.1.json
new file mode 100644
index 0000000000000000000000000000000000000000..29cd5d22b32d72aecc3563d89d87b1eb29f2e375
--- /dev/null
+++ b/experiments/medqa/indexes/medqa_idx/doclens.1.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/experiments/medqa/indexes/medqa_idx/doclens.2.json b/experiments/medqa/indexes/medqa_idx/doclens.2.json
new file mode 100644
index 0000000000000000000000000000000000000000..2b85771fab161855d7205a7429d561e48c3a9d9e
--- /dev/null
+++ b/experiments/medqa/indexes/medqa_idx/doclens.2.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/experiments/medqa/indexes/medqa_idx/doclens.3.json b/experiments/medqa/indexes/medqa_idx/doclens.3.json
new file mode 100644
index 0000000000000000000000000000000000000000..e69d364e7fc8f2a6f9abd98ed988ddd8563344b2
--- /dev/null
+++ b/experiments/medqa/indexes/medqa_idx/doclens.3.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/experiments/medqa/indexes/medqa_idx/doclens.4.json b/experiments/medqa/indexes/medqa_idx/doclens.4.json
new file mode 100644
index 0000000000000000000000000000000000000000..56bdcb60c034fd7354fdeee6f29f385ee0771b92
--- /dev/null
+++ b/experiments/medqa/indexes/medqa_idx/doclens.4.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/experiments/medqa/indexes/medqa_idx/doclens.5.json b/experiments/medqa/indexes/medqa_idx/doclens.5.json
new file mode 100644
index 0000000000000000000000000000000000000000..c6f0c7e944a45d36290a45317399752569c80b43
--- /dev/null
+++ b/experiments/medqa/indexes/medqa_idx/doclens.5.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/experiments/medqa/indexes/medqa_idx/doclens.6.json b/experiments/medqa/indexes/medqa_idx/doclens.6.json
new file mode 100644
index 0000000000000000000000000000000000000000..9f056fa5812bf678bbf07d20b207ea822e193312
--- /dev/null
+++ b/experiments/medqa/indexes/medqa_idx/doclens.6.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/experiments/medqa/indexes/medqa_idx/doclens.7.json b/experiments/medqa/indexes/medqa_idx/doclens.7.json
new file mode 100644
index 0000000000000000000000000000000000000000..27495f6cca621d2258a2e62fc13e6639e25316f6
--- /dev/null
+++ b/experiments/medqa/indexes/medqa_idx/doclens.7.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/experiments/medqa/indexes/medqa_idx/doclens.8.json b/experiments/medqa/indexes/medqa_idx/doclens.8.json
new file mode 100644
index 0000000000000000000000000000000000000000..1e1738a373c2b276f373254c7d09de163ca24e3f
--- /dev/null
+++ b/experiments/medqa/indexes/medqa_idx/doclens.8.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/experiments/medqa/indexes/medqa_idx/doclens.9.json b/experiments/medqa/indexes/medqa_idx/doclens.9.json
new file mode 100644
index 0000000000000000000000000000000000000000..5890bee4175fe32255b3640a58054cc96e55d7f3
--- /dev/null
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\ No newline at end of file
diff --git a/experiments/medqa/none/2022-10/13/14.59.02/checkpoints/colbert/tokenizer_config.json b/experiments/medqa/none/2022-10/13/14.59.02/checkpoints/colbert/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..f40f9b56153a6e0774794895536c39effa44fff8
--- /dev/null
+++ b/experiments/medqa/none/2022-10/13/14.59.02/checkpoints/colbert/tokenizer_config.json
@@ -0,0 +1,14 @@
+{
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "name_or_path": "bert-base-chinese",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "special_tokens_map_file": null,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}
diff --git a/experiments/medqa/none/2022-10/13/14.59.02/checkpoints/colbert/vocab.txt b/experiments/medqa/none/2022-10/13/14.59.02/checkpoints/colbert/vocab.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ca4f9781030019ab9b253c6dcb8c7878b6dc87a5
--- /dev/null
+++ b/experiments/medqa/none/2022-10/13/14.59.02/checkpoints/colbert/vocab.txt
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diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6be37ae8d138213a2d025f61c2011be95d0e5cc4
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,11 @@
+bitarray
+datasets
+gitpython
+ninja
+scipy
+spacy
+tqdm
+transformers
+ujson
+faiss-cpu
+torch
\ No newline at end of file
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..cea94a40357fbd0d22bb3ea07bf220420b2e90a9
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,17 @@
+import setuptools
+
+with open('README.md', 'r') as f:
+    long_description = f.read()
+
+setuptools.setup(
+    name='ColBERT',
+    version='0.2.0',
+    author='Omar Khattab',
+    author_email='okhattab@stanford.edu',
+    description="Efficient and Effective Passage Search via Contextualized Late Interaction over BERT",
+    long_description=long_description,
+    long_description_content_type='text/markdown',
+    url='https://github.com/stanford-futuredata/ColBERT',
+    packages=setuptools.find_packages(),
+    python_requires='>=3.6',
+)
diff --git a/utility/evaluate/annotate_EM.py b/utility/evaluate/annotate_EM.py
new file mode 100644
index 0000000000000000000000000000000000000000..ecd87048960b7da24d1ca3fea2b33e66790b6599
--- /dev/null
+++ b/utility/evaluate/annotate_EM.py
@@ -0,0 +1,81 @@
+import os
+import sys
+import git
+import tqdm
+import ujson
+import random
+
+from argparse import ArgumentParser
+from multiprocessing import Pool
+
+from colbert.utils.utils import print_message, load_ranking, groupby_first_item
+from utility.utils.qa_loaders import load_qas_, load_collection_
+from utility.utils.save_metadata import format_metadata, get_metadata
+from utility.evaluate.annotate_EM_helpers import *
+
+
+# TODO: Tokenize passages in advance, especially if the ranked list is long! This requires changes to the has_answer input, slightly.
+
+def main(args):
+    qas = load_qas_(args.qas)
+    collection = load_collection_(args.collection, retain_titles=True)
+    rankings = load_ranking(args.ranking)
+    parallel_pool = Pool(30)
+
+    print_message('#> Tokenize the answers in the Q&As in parallel...')
+    qas = list(parallel_pool.map(tokenize_all_answers, qas))
+    
+    qid2answers = {qid: tok_answers for qid, _, tok_answers in qas}
+    assert len(qas) == len(qid2answers), (len(qas), len(qid2answers))
+
+    print_message('#> Lookup passages from PIDs...')
+    expanded_rankings = [(qid, pid, rank, collection[pid], qid2answers[qid])
+                         for qid, pid, rank, *_ in rankings]
+
+    print_message('#> Assign labels in parallel...')
+    labeled_rankings = list(parallel_pool.map(assign_label_to_passage, enumerate(expanded_rankings)))
+
+    # Dump output.
+    print_message("#> Dumping output to", args.output, "...")
+    qid2rankings = groupby_first_item(labeled_rankings)
+
+    num_judged_queries, num_ranked_queries = check_sizes(qid2answers, qid2rankings)
+
+    # Evaluation metrics and depths.
+    success, counts = compute_and_write_labels(args.output, qid2answers, qid2rankings)
+
+    # Dump metrics.
+    with open(args.output_metrics, 'w') as f:
+        d = {'num_ranked_queries': num_ranked_queries, 'num_judged_queries': num_judged_queries}
+
+        extra = '__WARNING' if num_judged_queries != num_ranked_queries else ''
+        d[f'success{extra}'] = {k: v / num_judged_queries for k, v in success.items()}
+        d[f'counts{extra}'] = {k: v / num_judged_queries for k, v in counts.items()}
+        d['arguments'] = get_metadata(args)
+
+        f.write(format_metadata(d) + '\n')
+
+    print('\n\n')
+    print(args.output)
+    print(args.output_metrics)
+    print("#> Done\n")
+
+
+if __name__ == "__main__":
+    random.seed(12345)
+
+    parser = ArgumentParser(description='.')
+
+    # Input / Output Arguments
+    parser.add_argument('--qas', dest='qas', required=True, type=str)
+    parser.add_argument('--collection', dest='collection', required=True, type=str)
+    parser.add_argument('--ranking', dest='ranking', required=True, type=str)
+
+    args = parser.parse_args()
+
+    args.output = f'{args.ranking}.annotated'
+    args.output_metrics = f'{args.ranking}.annotated.metrics'
+
+    assert not os.path.exists(args.output), args.output
+
+    main(args)
diff --git a/utility/evaluate/annotate_EM_helpers.py b/utility/evaluate/annotate_EM_helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0d357ac4e8b47629604af9b4e6db0a088a3bed1
--- /dev/null
+++ b/utility/evaluate/annotate_EM_helpers.py
@@ -0,0 +1,74 @@
+from colbert.utils.utils import print_message
+from utility.utils.dpr import DPR_normalize, has_answer
+
+
+def tokenize_all_answers(args):
+    qid, question, answers = args
+    return qid, question, [DPR_normalize(ans) for ans in answers]
+
+
+def assign_label_to_passage(args):
+    idx, (qid, pid, rank, passage, tokenized_answers) = args
+
+    if idx % (1*1000*1000) == 0:
+        print(idx)
+
+    return qid, pid, rank, has_answer(tokenized_answers, passage)
+
+
+def check_sizes(qid2answers, qid2rankings):
+    num_judged_queries = len(qid2answers)
+    num_ranked_queries = len(qid2rankings)
+
+    print_message('num_judged_queries =', num_judged_queries)
+    print_message('num_ranked_queries =', num_ranked_queries)
+
+    if num_judged_queries != num_ranked_queries:
+        assert num_ranked_queries <= num_judged_queries
+
+        print('\n\n')
+        print_message('[WARNING] num_judged_queries != num_ranked_queries')
+        print('\n\n')
+    
+    return num_judged_queries, num_ranked_queries
+
+
+def compute_and_write_labels(output_path, qid2answers, qid2rankings):
+    cutoffs = [1, 5, 10, 20, 30, 50, 100, 1000, 'all']
+    success = {cutoff: 0.0 for cutoff in cutoffs}
+    counts = {cutoff: 0.0 for cutoff in cutoffs}
+
+    with open(output_path, 'w') as f:
+        for qid in qid2answers:
+            if qid not in qid2rankings:
+                continue
+
+            prev_rank = 0  # ranks should start at one (i.e., and not zero)
+            labels = []
+
+            for pid, rank, label in qid2rankings[qid]:
+                assert rank == prev_rank+1, (qid, pid, (prev_rank, rank))
+                prev_rank = rank
+
+                labels.append(label)
+                line = '\t'.join(map(str, [qid, pid, rank, int(label)])) + '\n'
+                f.write(line)
+
+            for cutoff in cutoffs:
+                if cutoff != 'all':
+                    success[cutoff] += sum(labels[:cutoff]) > 0
+                    counts[cutoff] += sum(labels[:cutoff])
+                else:
+                    success[cutoff] += sum(labels) > 0
+                    counts[cutoff] += sum(labels)
+
+    return success, counts
+
+
+# def dump_metrics(f, nqueries, cutoffs, success, counts):
+#     for cutoff in cutoffs:
+#         success_log = "#> P@{} = {}".format(cutoff, success[cutoff] / nqueries)
+#         counts_log = "#> D@{} = {}".format(cutoff, counts[cutoff] / nqueries)
+#         print('\n'.join([success_log, counts_log]) + '\n')
+
+#         f.write('\n'.join([success_log, counts_log]) + '\n\n')
diff --git a/utility/evaluate/msmarco_passages.py b/utility/evaluate/msmarco_passages.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f7503748e5202beb354b85ecc05e558d75aed6f
--- /dev/null
+++ b/utility/evaluate/msmarco_passages.py
@@ -0,0 +1,126 @@
+"""
+    Evaluate MS MARCO Passages ranking.
+"""
+
+import os
+import math
+import tqdm
+import ujson
+import random
+
+from argparse import ArgumentParser
+from collections import defaultdict
+from colbert.utils.utils import print_message, file_tqdm
+
+
+def main(args):
+    qid2positives = defaultdict(list)
+    qid2ranking = defaultdict(list)
+    qid2mrr = {}
+    qid2recall = {depth: {} for depth in [50, 200, 1000, 5000, 10000]}
+
+    with open(args.qrels) as f:
+        print_message(f"#> Loading QRELs from {args.qrels} ..")
+        for line in file_tqdm(f):
+            qid, _, pid, label = map(int, line.strip().split())
+            assert label == 1
+
+            qid2positives[qid].append(pid)
+
+    with open(args.ranking) as f:
+        print_message(f"#> Loading ranked lists from {args.ranking} ..")
+        for line in file_tqdm(f):
+            qid, pid, rank, *score = line.strip().split('\t')
+            qid, pid, rank = int(qid), int(pid), int(rank)
+
+            if len(score) > 0:
+                assert len(score) == 1
+                score = float(score[0])
+            else:
+                score = None
+
+            qid2ranking[qid].append((rank, pid, score))
+
+    assert set.issubset(set(qid2ranking.keys()), set(qid2positives.keys()))
+
+    num_judged_queries = len(qid2positives)
+    num_ranked_queries = len(qid2ranking)
+
+    if num_judged_queries != num_ranked_queries:
+        print()
+        print_message("#> [WARNING] num_judged_queries != num_ranked_queries")
+        print_message(f"#> {num_judged_queries} != {num_ranked_queries}")
+        print()
+
+    print_message(f"#> Computing MRR@10 for {num_judged_queries} queries.")
+
+    for qid in tqdm.tqdm(qid2positives):
+        ranking = qid2ranking[qid]
+        positives = qid2positives[qid]
+
+        for rank, (_, pid, _) in enumerate(ranking):
+            rank = rank + 1  # 1-indexed
+
+            if pid in positives:
+                if rank <= 10:
+                    qid2mrr[qid] = 1.0 / rank
+                break
+
+        for rank, (_, pid, _) in enumerate(ranking):
+            rank = rank + 1  # 1-indexed
+
+            if pid in positives:
+                for depth in qid2recall:
+                    if rank <= depth:
+                        qid2recall[depth][qid] = qid2recall[depth].get(qid, 0) + 1.0 / len(positives)
+
+    assert len(qid2mrr) <= num_ranked_queries, (len(qid2mrr), num_ranked_queries)
+
+    print()
+    mrr_10_sum = sum(qid2mrr.values())
+    print_message(f"#> MRR@10 = {mrr_10_sum / num_judged_queries}")
+    print_message(f"#> MRR@10 (only for ranked queries) = {mrr_10_sum / num_ranked_queries}")
+    print()
+
+    for depth in qid2recall:
+        assert len(qid2recall[depth]) <= num_ranked_queries, (len(qid2recall[depth]), num_ranked_queries)
+
+        print()
+        metric_sum = sum(qid2recall[depth].values())
+        print_message(f"#> Recall@{depth} = {metric_sum / num_judged_queries}")
+        print_message(f"#> Recall@{depth} (only for ranked queries) = {metric_sum / num_ranked_queries}")
+        print()
+
+    if args.annotate:
+        print_message(f"#> Writing annotations to {args.output} ..")
+
+        with open(args.output, 'w') as f:
+            for qid in tqdm.tqdm(qid2positives):
+                ranking = qid2ranking[qid]
+                positives = qid2positives[qid]
+
+                for rank, (_, pid, score) in enumerate(ranking):
+                    rank = rank + 1  # 1-indexed
+                    label = int(pid in positives)
+
+                    line = [qid, pid, rank, score, label]
+                    line = [x for x in line if x is not None]
+                    line = '\t'.join(map(str, line)) + '\n'
+                    f.write(line)
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser(description="msmarco_passages.")
+
+    # Input Arguments.
+    parser.add_argument('--qrels', dest='qrels', required=True, type=str)
+    parser.add_argument('--ranking', dest='ranking', required=True, type=str)
+    parser.add_argument('--annotate', dest='annotate', default=False, action='store_true')
+
+    args = parser.parse_args()
+
+    if args.annotate:
+        args.output = f'{args.ranking}.annotated'
+        assert not os.path.exists(args.output), args.output
+
+    main(args)
diff --git a/utility/preprocess/docs2passages.py b/utility/preprocess/docs2passages.py
new file mode 100644
index 0000000000000000000000000000000000000000..0db8e938d20852d83d6a4590cce24b16fde31402
--- /dev/null
+++ b/utility/preprocess/docs2passages.py
@@ -0,0 +1,149 @@
+"""
+    Divide a document collection into N-word/token passage spans (with wrap-around for last passage).
+"""
+
+import os
+import math
+import ujson
+import random
+
+from multiprocessing import Pool
+from argparse import ArgumentParser
+from colbert.utils.utils import print_message
+
+Format1 = 'docid,text'  # MS MARCO Passages
+Format2 = 'docid,text,title'   # DPR Wikipedia
+Format3 = 'docid,url,title,text'  # MS MARCO Documents
+
+
+def process_page(inp):
+    """
+        Wraps around if we split: make sure last passage isn't too short.
+        This is meant to be similar to the DPR preprocessing.
+    """
+
+    (nwords, overlap, tokenizer), (title_idx, docid, title, url, content) = inp
+
+    if tokenizer is None:
+        words = content.split()
+    else:
+        words = tokenizer.tokenize(content)
+
+    words_ = (words + words) if len(words) > nwords else words
+    passages = [words_[offset:offset + nwords] for offset in range(0, len(words) - overlap, nwords - overlap)]
+
+    assert all(len(psg) in [len(words), nwords] for psg in passages), (list(map(len, passages)), len(words))
+
+    if tokenizer is None:
+        passages = [' '.join(psg) for psg in passages]
+    else:
+        passages = [' '.join(psg).replace(' ##', '') for psg in passages]
+
+    if title_idx % 100000 == 0:
+        print("#> ", title_idx, '\t\t\t', title)
+
+        for p in passages:
+            print("$$$ ", '\t\t', p)
+            print()
+
+        print()
+        print()
+        print()
+
+    return (docid, title, url, passages)
+
+
+def main(args):
+    random.seed(12345)
+    print_message("#> Starting...")
+
+    letter = 'w' if not args.use_wordpiece else 't'
+    output_path = f'{args.input}.{letter}{args.nwords}_{args.overlap}'
+    assert not os.path.exists(output_path)
+
+    RawCollection = []
+    Collection = []
+
+    NumIllFormattedLines = 0
+
+    with open(args.input) as f:
+        for line_idx, line in enumerate(f):
+            if line_idx % (100*1000) == 0:
+                print(line_idx, end=' ')
+
+            title, url = None, None
+
+            try:
+                line = line.strip().split('\t')
+
+                if args.format == Format1:
+                    docid, doc = line
+                elif args.format == Format2:
+                    docid, doc, title = line
+                elif args.format == Format3:
+                    docid, url, title, doc = line
+
+                RawCollection.append((line_idx, docid, title, url, doc))
+            except:
+                NumIllFormattedLines += 1
+
+                if NumIllFormattedLines % 1000 == 0:
+                    print(f'\n[{line_idx}] NumIllFormattedLines = {NumIllFormattedLines}\n')
+
+    print()
+    print_message("# of documents is", len(RawCollection), '\n')
+
+    p = Pool(args.nthreads)
+
+    print_message("#> Starting parallel processing...")
+
+    tokenizer = None
+    if args.use_wordpiece:
+        from transformers import BertTokenizerFast
+        tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
+
+    process_page_params = [(args.nwords, args.overlap, tokenizer)] * len(RawCollection)
+    Collection = p.map(process_page, zip(process_page_params, RawCollection))
+
+    print_message(f"#> Writing to {output_path} ...")
+    with open(output_path, 'w') as f:
+        line_idx = 1
+
+        if args.format == Format1:
+            f.write('\t'.join(['id', 'text']) + '\n')
+        elif args.format == Format2:
+            f.write('\t'.join(['id', 'text', 'title']) + '\n')
+        elif args.format == Format3:
+            f.write('\t'.join(['id', 'text', 'title', 'docid']) + '\n')
+
+        for docid, title, url, passages in Collection:
+            for passage in passages:
+                if args.format == Format1:
+                    f.write('\t'.join([str(line_idx), passage]) + '\n')
+                elif args.format == Format2:
+                    f.write('\t'.join([str(line_idx), passage, title]) + '\n')
+                elif args.format == Format3:
+                    f.write('\t'.join([str(line_idx), passage, title, docid]) + '\n')
+
+                line_idx += 1
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser(description="docs2passages.")
+
+    # Input Arguments.
+    parser.add_argument('--input', dest='input', required=True)
+    parser.add_argument('--format', dest='format', required=True, choices=[Format1, Format2, Format3])
+
+    # Output Arguments.
+    parser.add_argument('--use-wordpiece', dest='use_wordpiece', default=False, action='store_true')
+    parser.add_argument('--nwords', dest='nwords', default=100, type=int)
+    parser.add_argument('--overlap', dest='overlap', default=0, type=int)
+
+    # Other Arguments.
+    parser.add_argument('--nthreads', dest='nthreads', default=28, type=int)
+
+    args = parser.parse_args()
+    assert args.nwords in range(50, 500)
+
+    main(args)
diff --git a/utility/preprocess/queries_split.py b/utility/preprocess/queries_split.py
new file mode 100644
index 0000000000000000000000000000000000000000..09c866afafb0de1e5987d064088da3659ef27436
--- /dev/null
+++ b/utility/preprocess/queries_split.py
@@ -0,0 +1,81 @@
+"""
+    Divide a query set into two.
+"""
+
+import os
+import math
+import ujson
+import random
+
+from argparse import ArgumentParser
+from collections import OrderedDict
+from colbert.utils.utils import print_message
+
+
+def main(args):
+    random.seed(12345)
+
+    """
+    Load the queries
+    """
+    Queries = OrderedDict()
+
+    print_message(f"#> Loading queries from {args.input}..")
+    with open(args.input) as f:
+        for line in f:
+            qid, query = line.strip().split('\t')
+
+            assert qid not in Queries
+            Queries[qid] = query
+
+    """
+    Apply the splitting
+    """
+    size_a = len(Queries) - args.holdout
+    size_b = args.holdout
+    size_a, size_b = max(size_a, size_b), min(size_a, size_b)
+
+    assert size_a > 0 and size_b > 0, (len(Queries), size_a, size_b)
+
+    print_message(f"#> Deterministically splitting the queries into ({size_a}, {size_b})-sized splits.")
+
+    keys = list(Queries.keys())
+    sample_b_indices = sorted(list(random.sample(range(len(keys)), size_b)))
+    sample_a_indices = sorted(list(set.difference(set(list(range(len(keys)))), set(sample_b_indices))))
+
+    assert len(sample_a_indices) == size_a
+    assert len(sample_b_indices) == size_b
+
+    sample_a = [keys[idx] for idx in sample_a_indices]
+    sample_b = [keys[idx] for idx in sample_b_indices]
+
+    """
+    Write the output
+    """
+
+    output_path_a = f'{args.input}.a'
+    output_path_b = f'{args.input}.b'
+
+    assert not os.path.exists(output_path_a), output_path_a
+    assert not os.path.exists(output_path_b), output_path_b
+
+    print_message(f"#> Writing the splits out to {output_path_a} and {output_path_b} ...")
+
+    for output_path, sample in [(output_path_a, sample_a), (output_path_b, sample_b)]:
+        with open(output_path, 'w') as f:
+            for qid in sample:
+                query = Queries[qid]
+                line = '\t'.join([qid, query]) + '\n'
+                f.write(line)
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser(description="queries_split.")
+
+    # Input Arguments.
+    parser.add_argument('--input', dest='input', required=True)
+    parser.add_argument('--holdout', dest='holdout', required=True, type=int)
+
+    args = parser.parse_args()
+
+    main(args)
diff --git a/utility/rankings/dev_subsample.py b/utility/rankings/dev_subsample.py
new file mode 100644
index 0000000000000000000000000000000000000000..a928fe8a1faccec87e4abb3043979e101040c267
--- /dev/null
+++ b/utility/rankings/dev_subsample.py
@@ -0,0 +1,47 @@
+import os
+import ujson
+import random
+
+from argparse import ArgumentParser
+
+from colbert.utils.utils import print_message, create_directory, load_ranking, groupby_first_item
+from utility.utils.qa_loaders import load_qas_
+
+
+def main(args):
+    print_message("#> Loading all..")
+    qas = load_qas_(args.qas)
+    rankings = load_ranking(args.ranking)
+    qid2rankings = groupby_first_item(rankings)
+
+    print_message("#> Subsampling all..")
+    qas_sample = random.sample(qas, args.sample)
+
+    with open(args.output, 'w') as f:
+        for qid, *_ in qas_sample:
+            for items in qid2rankings[qid]:
+                items = [qid] + items
+                line = '\t'.join(map(str, items)) + '\n'
+                f.write(line)
+
+    print('\n\n')
+    print(args.output)
+    print("#> Done.")
+
+
+if __name__ == "__main__":
+    random.seed(12345)
+
+    parser = ArgumentParser(description='Subsample the dev set.')
+    parser.add_argument('--qas', dest='qas', required=True, type=str)
+    parser.add_argument('--ranking', dest='ranking', required=True)
+    parser.add_argument('--output', dest='output', required=True)
+
+    parser.add_argument('--sample', dest='sample', default=1500, type=int)
+
+    args = parser.parse_args()
+
+    assert not os.path.exists(args.output), args.output
+    create_directory(os.path.dirname(args.output))
+
+    main(args)
diff --git a/utility/rankings/merge.py b/utility/rankings/merge.py
new file mode 100644
index 0000000000000000000000000000000000000000..44b555a08c22787d20850746c85e50dadc211452
--- /dev/null
+++ b/utility/rankings/merge.py
@@ -0,0 +1,57 @@
+"""
+    Divide two or more ranking files, by score.
+"""
+
+import os
+import tqdm
+
+from argparse import ArgumentParser
+from collections import defaultdict
+from colbert.utils.utils import print_message, file_tqdm
+
+
+def main(args):
+    Rankings = defaultdict(list)
+
+    for path in args.input:
+        print_message(f"#> Loading the rankings in {path} ..")
+
+        with open(path) as f:
+            for line in file_tqdm(f):
+                qid, pid, rank, score = line.strip().split('\t')
+                qid, pid, rank = map(int, [qid, pid, rank])
+                score = float(score)
+
+                Rankings[qid].append((score, rank, pid))
+
+    with open(args.output, 'w') as f:
+        print_message(f"#> Writing the output rankings to {args.output} ..")
+
+        for qid in tqdm.tqdm(Rankings):
+            ranking = sorted(Rankings[qid], reverse=True)
+
+            for rank, (score, original_rank, pid) in enumerate(ranking):
+                rank = rank + 1  # 1-indexed
+
+                if (args.depth > 0) and (rank > args.depth):
+                    break
+
+                line = [qid, pid, rank, score]
+                line = '\t'.join(map(str, line)) + '\n'
+                f.write(line)
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser(description="merge_rankings.")
+
+    # Input Arguments.
+    parser.add_argument('--input', dest='input', required=True, nargs='+')
+    parser.add_argument('--output', dest='output', required=True, type=str)
+
+    parser.add_argument('--depth', dest='depth', required=True, type=int)
+
+    args = parser.parse_args()
+
+    assert not os.path.exists(args.output), args.output
+
+    main(args)
diff --git a/utility/rankings/split_by_offset.py b/utility/rankings/split_by_offset.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d2e8cfd2e770097542a6059f39e51b039f3014f
--- /dev/null
+++ b/utility/rankings/split_by_offset.py
@@ -0,0 +1,44 @@
+"""
+Split the ranked lists after retrieval with a merged query set.
+"""
+
+import os
+import random
+
+from argparse import ArgumentParser
+
+
+def main(args):
+    output_paths = ['{}.{}'.format(args.ranking, split) for split in args.names]
+    assert all(not os.path.exists(path) for path in output_paths), output_paths
+
+    output_files = [open(path, 'w') for path in output_paths]
+
+    with open(args.ranking) as f:
+        for line in f:
+            qid, pid, rank, *other = line.strip().split('\t')
+            qid = int(qid)
+            split_output_path = output_files[qid // args.gap - 1]
+            qid = qid % args.gap
+
+            split_output_path.write('\t'.join([str(x) for x in [qid, pid, rank, *other]]) + '\n')
+        
+        print(f.name)
+    
+    _ = [f.close() for f in output_files]
+    
+    print("#> Done!")
+
+
+if __name__ == "__main__":
+    random.seed(12345)
+
+    parser = ArgumentParser(description='Subsample the dev set.')
+    parser.add_argument('--ranking', dest='ranking', required=True)
+
+    parser.add_argument('--names', dest='names', required=False, default=['train', 'dev', 'test'], type=str, nargs='+')  # order matters!
+    parser.add_argument('--gap', dest='gap', required=False, default=1_000_000_000, type=int)  # larger than any individual query set
+
+    args = parser.parse_args()
+
+    main(args)
diff --git a/utility/rankings/split_by_queries.py b/utility/rankings/split_by_queries.py
new file mode 100644
index 0000000000000000000000000000000000000000..f052a7cdd953423a37fbfeb96e1b3bbfa08ec573
--- /dev/null
+++ b/utility/rankings/split_by_queries.py
@@ -0,0 +1,67 @@
+import os
+import sys
+import tqdm
+import ujson
+import random
+
+from argparse import ArgumentParser
+from collections import OrderedDict
+from colbert.utils.utils import print_message, file_tqdm
+
+
+def main(args):
+    qid_to_file_idx = {}
+
+    for qrels_idx, qrels in enumerate(args.all_queries):
+        with open(qrels) as f:
+            for line in f:
+                qid, *_ = line.strip().split('\t')
+                qid = int(qid)
+
+                assert qid_to_file_idx.get(qid, qrels_idx) == qrels_idx, (qid, qrels_idx)
+                qid_to_file_idx[qid] = qrels_idx
+
+    all_outputs_paths = [f'{args.ranking}.{idx}' for idx in range(len(args.all_queries))]
+
+    assert all(not os.path.exists(path) for path in all_outputs_paths)
+
+    all_outputs = [open(path, 'w') for path in all_outputs_paths]
+
+    with open(args.ranking) as f:
+        print_message(f"#> Loading ranked lists from {f.name} ..")
+
+        last_file_idx = -1
+
+        for line in file_tqdm(f):
+            qid, *_ = line.strip().split('\t')
+
+            file_idx = qid_to_file_idx[int(qid)]
+
+            if file_idx != last_file_idx:
+                print_message(f"#> Switched to file #{file_idx} at {all_outputs[file_idx].name}")
+
+            last_file_idx = file_idx
+
+            all_outputs[file_idx].write(line)
+
+    print()
+
+    for f in all_outputs:
+        print(f.name)
+        f.close()
+
+    print("#> Done!")
+
+
+if __name__ == "__main__":
+    random.seed(12345)
+
+    parser = ArgumentParser(description='.')
+
+    # Input Arguments
+    parser.add_argument('--ranking', dest='ranking', required=True, type=str)
+    parser.add_argument('--all-queries', dest='all_queries', required=True, type=str, nargs='+')
+
+    args = parser.parse_args()
+
+    main(args)
diff --git a/utility/rankings/tune.py b/utility/rankings/tune.py
new file mode 100644
index 0000000000000000000000000000000000000000..035d1c075837beabf8d399eb9d2f85a9aafd58d4
--- /dev/null
+++ b/utility/rankings/tune.py
@@ -0,0 +1,66 @@
+import os
+import ujson
+import random
+
+from argparse import ArgumentParser
+from colbert.utils.utils import print_message, create_directory
+from utility.utils.save_metadata import save_metadata
+
+
+def main(args):
+    AllMetrics = {}
+    Scores = {}
+
+    for path in args.paths:
+        with open(path) as f:
+            metric = ujson.load(f)
+            AllMetrics[path] = metric
+
+            for k in args.metric:
+                metric = metric[k]
+
+            assert type(metric) is float
+            Scores[path] = metric
+    
+    MaxKey = max(Scores, key=Scores.get)
+
+    MaxCKPT = int(MaxKey.split('/')[-2].split('.')[-1])
+    MaxARGS = os.path.join(os.path.dirname(MaxKey), 'logs', 'args.json')
+
+    with open(MaxARGS) as f:
+        logs = ujson.load(f)
+        MaxCHECKPOINT = logs['checkpoint']
+
+        assert MaxCHECKPOINT.endswith(f'colbert-{MaxCKPT}.dnn'), (MaxCHECKPOINT, MaxCKPT)
+
+    with open(args.output, 'w') as f:
+        f.write(MaxCHECKPOINT)
+
+    args.Scores = Scores
+    args.AllMetrics = AllMetrics
+
+    save_metadata(f'{args.output}.meta', args)
+
+    print('\n\n', args, '\n\n')
+    print(args.output)
+    print_message("#> Done.")
+
+
+if __name__ == "__main__":
+    random.seed(12345)
+
+    parser = ArgumentParser(description='.')
+
+    # Input / Output Arguments
+    parser.add_argument('--metric', dest='metric', required=True, type=str)  # e.g., success.20
+    parser.add_argument('--paths', dest='paths', required=True, type=str, nargs='+')
+    parser.add_argument('--output', dest='output', required=True, type=str)
+
+    args = parser.parse_args()
+
+    args.metric = args.metric.split('.')
+
+    assert not os.path.exists(args.output), args.output
+    create_directory(os.path.dirname(args.output))
+
+    main(args)
diff --git a/utility/supervision/self_training.py b/utility/supervision/self_training.py
new file mode 100644
index 0000000000000000000000000000000000000000..814e4d33a9eb8f197401886e4dda9bdfe6ce5941
--- /dev/null
+++ b/utility/supervision/self_training.py
@@ -0,0 +1,123 @@
+import os
+import sys
+import git
+import tqdm
+import ujson
+import random
+
+from argparse import ArgumentParser
+from colbert.utils.utils import print_message, load_ranking, groupby_first_item
+
+
+MAX_NUM_TRIPLES = 40_000_000
+
+
+def sample_negatives(negatives, num_sampled, biased=False):
+    num_sampled = min(len(negatives), num_sampled)
+
+    if biased:
+        assert num_sampled % 2 == 0
+        num_sampled_top100 = num_sampled // 2
+        num_sampled_rest = num_sampled - num_sampled_top100
+
+        return random.sample(negatives[:100], num_sampled_top100) + random.sample(negatives[100:], num_sampled_rest)
+
+    return random.sample(negatives, num_sampled)
+
+
+def sample_for_query(qid, ranking, npositives, depth_positive, depth_negative, cutoff_negative):
+    """
+        Requires that the ranks are sorted per qid.
+    """
+    assert npositives <= depth_positive < cutoff_negative < depth_negative
+
+    positives, negatives, triples = [], [], []
+
+    for pid, rank, *_ in ranking:
+        assert rank >= 1, f"ranks should start at 1 \t\t got rank = {rank}"
+
+        if rank > depth_negative:
+            break
+
+        if rank <= depth_positive:
+            positives.append(pid)
+        elif rank > cutoff_negative:
+            negatives.append(pid)
+
+    num_sampled = 100
+
+    for neg in sample_negatives(negatives, num_sampled):
+        positives_ = random.sample(positives, npositives)
+        positives_ = positives_[0] if npositives == 1 else positives_
+        triples.append((qid, positives_, neg))
+
+    return triples
+
+
+def main(args):
+    rankings = load_ranking(args.ranking, types=[int, int, int, float, int])
+
+    print_message("#> Group by QID")
+    qid2rankings = groupby_first_item(tqdm.tqdm(rankings))
+
+    Triples = []
+    NonEmptyQIDs = 0
+
+    for processing_idx, qid in enumerate(qid2rankings):
+        l = sample_for_query(qid, qid2rankings[qid], args.positives, args.depth_positive, args.depth_negative, args.cutoff_negative)
+        NonEmptyQIDs += (len(l) > 0)
+        Triples.extend(l)
+
+        if processing_idx % (10_000) == 0:
+            print_message(f"#> Done with {processing_idx+1} questions!\t\t "
+                          f"{str(len(Triples) / 1000)}k triples for {NonEmptyQIDs} unqiue QIDs.")
+
+    print_message(f"#> Sub-sample the triples (if > {MAX_NUM_TRIPLES})..")
+    print_message(f"#> len(Triples) = {len(Triples)}")
+
+    if len(Triples) > MAX_NUM_TRIPLES:
+        Triples = random.sample(Triples, MAX_NUM_TRIPLES)
+
+    ### Prepare the triples ###
+    print_message("#> Shuffling the triples...")
+    random.shuffle(Triples)
+
+    print_message("#> Writing {}M examples to file.".format(len(Triples) / 1000.0 / 1000.0))
+
+    with open(args.output, 'w') as f:
+        for example in Triples:
+            ujson.dump(example, f)
+            f.write('\n')
+
+    with open(f'{args.output}.meta', 'w') as f:
+        args.cmd = ' '.join(sys.argv)
+        args.git_hash = git.Repo(search_parent_directories=True).head.object.hexsha
+        ujson.dump(args.__dict__, f, indent=4)
+        f.write('\n')
+
+    print('\n\n', args, '\n\n')
+    print(args.output)
+    print_message("#> Done.")
+
+
+if __name__ == "__main__":
+    random.seed(12345)
+
+    parser = ArgumentParser(description='Create training triples from ranked list.')
+
+    # Input / Output Arguments
+    parser.add_argument('--ranking', dest='ranking', required=True, type=str)
+    parser.add_argument('--output', dest='output', required=True, type=str)
+
+    # Weak Supervision Arguments.
+    parser.add_argument('--positives', dest='positives', required=True, type=int)
+    parser.add_argument('--depth+', dest='depth_positive', required=True, type=int)
+
+    parser.add_argument('--depth-', dest='depth_negative', required=True, type=int)
+    parser.add_argument('--cutoff-', dest='cutoff_negative', required=True, type=int)
+
+    args = parser.parse_args()
+
+    assert not os.path.exists(args.output), args.output
+
+    main(args)
diff --git a/utility/supervision/triples.py b/utility/supervision/triples.py
new file mode 100644
index 0000000000000000000000000000000000000000..b70c2c0933fcf25d0df939e6e1030e1c45eca2a6
--- /dev/null
+++ b/utility/supervision/triples.py
@@ -0,0 +1,150 @@
+"""
+    Example:  --positives 5,50 1,1000        ~~>    best-5 (in top-50)  +  best-1 (in top-1000)
+"""
+
+import os
+import sys
+import git
+import tqdm
+import ujson
+import random
+
+from argparse import ArgumentParser
+from colbert.utils.utils import print_message, load_ranking, groupby_first_item, create_directory
+from utility.utils.save_metadata import save_metadata
+
+
+MAX_NUM_TRIPLES = 40_000_000
+
+
+def sample_negatives(negatives, num_sampled, biased=None):
+    assert biased in [None, 100, 200], "NOTE: We bias 50% from the top-200 negatives, if there are twice or more."
+
+    num_sampled = min(len(negatives), num_sampled)
+
+    if biased and num_sampled < len(negatives):
+        assert num_sampled % 2 == 0, num_sampled
+
+        num_sampled_top100 = num_sampled // 2
+        num_sampled_rest = num_sampled - num_sampled_top100
+
+        oversampled, undersampled = negatives[:biased], negatives[biased:]
+
+        if len(oversampled) < len(undersampled):
+            return random.sample(oversampled, num_sampled_top100) + random.sample(undersampled, num_sampled_rest)
+
+    return random.sample(negatives, num_sampled)
+
+
+def sample_for_query(qid, ranking, args_positives, depth, permissive, biased):
+    """
+        Requires that the ranks are sorted per qid.
+    """
+
+    positives, negatives, triples = [], [], []
+
+    for pid, rank, *_, label in ranking:
+        assert rank >= 1, f"ranks should start at 1 \t\t got rank = {rank}"
+        assert label in [0, 1]
+
+        if rank > depth:
+            break
+
+        if label:
+            take_this_positive = any(rank <= maxDepth and len(positives) < maxBest for maxBest, maxDepth in args_positives)
+
+            if take_this_positive:
+                positives.append((pid, 0))
+            elif permissive:
+                positives.append((pid, rank))  # utilize with a few negatives, starting at (next) rank
+
+        else:
+            negatives.append(pid)
+
+    for pos, neg_start in positives:
+        num_sampled = 100 if neg_start == 0 else 5
+        negatives_ = negatives[neg_start:]
+
+        biased_ = biased if neg_start == 0 else None
+        for neg in sample_negatives(negatives_, num_sampled, biased=biased_):
+            triples.append((qid, pos, neg))
+
+    return triples
+
+
+def main(args):
+    try:
+        rankings = load_ranking(args.ranking, types=[int, int, int, float, int])
+    except:
+        rankings = load_ranking(args.ranking, types=[int, int, int, int])
+
+    print_message("#> Group by QID")
+    qid2rankings = groupby_first_item(tqdm.tqdm(rankings))
+
+    Triples = []
+    NonEmptyQIDs = 0
+
+    for processing_idx, qid in enumerate(qid2rankings):
+        l = sample_for_query(qid, qid2rankings[qid], args.positives, args.depth, args.permissive, args.biased)
+        NonEmptyQIDs += (len(l) > 0)
+        Triples.extend(l)
+
+        if processing_idx % (10_000) == 0:
+            print_message(f"#> Done with {processing_idx+1} questions!\t\t "
+                          f"{str(len(Triples) / 1000)}k triples for {NonEmptyQIDs} unqiue QIDs.")
+
+    print_message(f"#> Sub-sample the triples (if > {MAX_NUM_TRIPLES})..")
+    print_message(f"#> len(Triples) = {len(Triples)}")
+
+    if len(Triples) > MAX_NUM_TRIPLES:
+        Triples = random.sample(Triples, MAX_NUM_TRIPLES)
+
+    ### Prepare the triples ###
+    print_message("#> Shuffling the triples...")
+    random.shuffle(Triples)
+
+    print_message("#> Writing {}M examples to file.".format(len(Triples) / 1000.0 / 1000.0))
+
+    with open(args.output, 'w') as f:
+        for example in Triples:
+            ujson.dump(example, f)
+            f.write('\n')
+
+    save_metadata(f'{args.output}.meta', args)
+
+    print('\n\n', args, '\n\n')
+    print(args.output)
+    print_message("#> Done.")
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser(description='Create training triples from ranked list.')
+
+    # Input / Output Arguments
+    parser.add_argument('--ranking', dest='ranking', required=True, type=str)
+    parser.add_argument('--output', dest='output', required=True, type=str)
+
+    # Weak Supervision Arguments.
+    parser.add_argument('--positives', dest='positives', required=True, nargs='+')
+    parser.add_argument('--depth', dest='depth', required=True, type=int)  # for negatives
+
+    parser.add_argument('--permissive', dest='permissive', default=False, action='store_true')
+    # parser.add_argument('--biased', dest='biased', default=False, action='store_true')
+    parser.add_argument('--biased', dest='biased', default=None, type=int)
+    parser.add_argument('--seed', dest='seed', required=False, default=12345, type=int)
+
+    args = parser.parse_args()
+    random.seed(args.seed)
+
+    assert not os.path.exists(args.output), args.output
+
+    args.positives = [list(map(int, configuration.split(','))) for configuration in args.positives]
+
+    assert all(len(x) == 2 for x in args.positives)
+    assert all(maxBest <= maxDepth for maxBest, maxDepth in args.positives), args.positives
+
+    create_directory(os.path.dirname(args.output))
+
+    assert args.biased in [None, 100, 200]
+
+    main(args)
diff --git a/utility/utils/__pycache__/save_metadata.cpython-39.pyc b/utility/utils/__pycache__/save_metadata.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e59435de87fe5049c9ef9847d3730f8daeeee336
Binary files /dev/null and b/utility/utils/__pycache__/save_metadata.cpython-39.pyc differ
diff --git a/utility/utils/dpr.py b/utility/utils/dpr.py
new file mode 100644
index 0000000000000000000000000000000000000000..f268d85bedb0484e9143bd271fd5b6d7c2283f28
--- /dev/null
+++ b/utility/utils/dpr.py
@@ -0,0 +1,237 @@
+"""
+    Source: DPR Implementation from Facebook Research
+    https://github.com/facebookresearch/DPR/tree/master/dpr
+"""
+
+import string
+import spacy
+import regex
+import unicodedata
+
+
+class Tokens(object):
+    """A class to represent a list of tokenized text."""
+    TEXT = 0
+    TEXT_WS = 1
+    SPAN = 2
+    POS = 3
+    LEMMA = 4
+    NER = 5
+
+    def __init__(self, data, annotators, opts=None):
+        self.data = data
+        self.annotators = annotators
+        self.opts = opts or {}
+
+    def __len__(self):
+        """The number of tokens."""
+        return len(self.data)
+
+    def slice(self, i=None, j=None):
+        """Return a view of the list of tokens from [i, j)."""
+        new_tokens = copy.copy(self)
+        new_tokens.data = self.data[i: j]
+        return new_tokens
+
+    def untokenize(self):
+        """Returns the original text (with whitespace reinserted)."""
+        return ''.join([t[self.TEXT_WS] for t in self.data]).strip()
+
+    def words(self, uncased=False):
+        """Returns a list of the text of each token
+
+        Args:
+            uncased: lower cases text
+        """
+        if uncased:
+            return [t[self.TEXT].lower() for t in self.data]
+        else:
+            return [t[self.TEXT] for t in self.data]
+
+    def offsets(self):
+        """Returns a list of [start, end) character offsets of each token."""
+        return [t[self.SPAN] for t in self.data]
+
+    def pos(self):
+        """Returns a list of part-of-speech tags of each token.
+        Returns None if this annotation was not included.
+        """
+        if 'pos' not in self.annotators:
+            return None
+        return [t[self.POS] for t in self.data]
+
+    def lemmas(self):
+        """Returns a list of the lemmatized text of each token.
+        Returns None if this annotation was not included.
+        """
+        if 'lemma' not in self.annotators:
+            return None
+        return [t[self.LEMMA] for t in self.data]
+
+    def entities(self):
+        """Returns a list of named-entity-recognition tags of each token.
+        Returns None if this annotation was not included.
+        """
+        if 'ner' not in self.annotators:
+            return None
+        return [t[self.NER] for t in self.data]
+
+    def ngrams(self, n=1, uncased=False, filter_fn=None, as_strings=True):
+        """Returns a list of all ngrams from length 1 to n.
+
+        Args:
+            n: upper limit of ngram length
+            uncased: lower cases text
+            filter_fn: user function that takes in an ngram list and returns
+              True or False to keep or not keep the ngram
+            as_string: return the ngram as a string vs list
+        """
+
+        def _skip(gram):
+            if not filter_fn:
+                return False
+            return filter_fn(gram)
+
+        words = self.words(uncased)
+        ngrams = [(s, e + 1)
+                  for s in range(len(words))
+                  for e in range(s, min(s + n, len(words)))
+                  if not _skip(words[s:e + 1])]
+
+        # Concatenate into strings
+        if as_strings:
+            ngrams = ['{}'.format(' '.join(words[s:e])) for (s, e) in ngrams]
+
+        return ngrams
+
+    def entity_groups(self):
+        """Group consecutive entity tokens with the same NER tag."""
+        entities = self.entities()
+        if not entities:
+            return None
+        non_ent = self.opts.get('non_ent', 'O')
+        groups = []
+        idx = 0
+        while idx < len(entities):
+            ner_tag = entities[idx]
+            # Check for entity tag
+            if ner_tag != non_ent:
+                # Chomp the sequence
+                start = idx
+                while (idx < len(entities) and entities[idx] == ner_tag):
+                    idx += 1
+                groups.append((self.slice(start, idx).untokenize(), ner_tag))
+            else:
+                idx += 1
+        return groups
+
+
+class Tokenizer(object):
+    """Base tokenizer class.
+    Tokenizers implement tokenize, which should return a Tokens class.
+    """
+
+    def tokenize(self, text):
+        raise NotImplementedError
+
+    def shutdown(self):
+        pass
+
+    def __del__(self):
+        self.shutdown()
+
+
+class SimpleTokenizer(Tokenizer):
+    ALPHA_NUM = r'[\p{L}\p{N}\p{M}]+'
+    NON_WS = r'[^\p{Z}\p{C}]'
+
+    def __init__(self, **kwargs):
+        """
+        Args:
+            annotators: None or empty set (only tokenizes).
+        """
+        self._regexp = regex.compile(
+            '(%s)|(%s)' % (self.ALPHA_NUM, self.NON_WS),
+            flags=regex.IGNORECASE + regex.UNICODE + regex.MULTILINE
+        )
+        if len(kwargs.get('annotators', {})) > 0:
+            logger.warning('%s only tokenizes! Skipping annotators: %s' %
+                           (type(self).__name__, kwargs.get('annotators')))
+        self.annotators = set()
+
+    def tokenize(self, text):
+        data = []
+        matches = [m for m in self._regexp.finditer(text)]
+        for i in range(len(matches)):
+            # Get text
+            token = matches[i].group()
+
+            # Get whitespace
+            span = matches[i].span()
+            start_ws = span[0]
+            if i + 1 < len(matches):
+                end_ws = matches[i + 1].span()[0]
+            else:
+                end_ws = span[1]
+
+            # Format data
+            data.append((
+                token,
+                text[start_ws: end_ws],
+                span,
+            ))
+        return Tokens(data, self.annotators)
+
+
+def has_answer(tokenized_answers, text):
+    text = DPR_normalize(text)
+
+    for single_answer in tokenized_answers:
+        for i in range(0, len(text) - len(single_answer) + 1):
+            if single_answer == text[i: i + len(single_answer)]:
+                return True
+
+    return False
+
+
+def locate_answers(tokenized_answers, text):
+    """
+    Returns each occurrence of an answer as (offset, endpos) in terms of *characters*.
+    """
+    tokenized_text = DPR_tokenize(text)
+    occurrences = []
+
+    text_words, text_word_positions = tokenized_text.words(uncased=True), tokenized_text.offsets()
+    answers_words = [ans.words(uncased=True) for ans in tokenized_answers]
+
+    for single_answer in answers_words:
+        for i in range(0, len(text_words) - len(single_answer) + 1):
+            if single_answer == text_words[i: i + len(single_answer)]:
+                (offset, _), (_, endpos) = text_word_positions[i], text_word_positions[i+len(single_answer)-1]
+                occurrences.append((offset, endpos))
+
+    return occurrences
+
+
+STokenizer = SimpleTokenizer()
+
+
+def DPR_tokenize(text):
+    return STokenizer.tokenize(unicodedata.normalize('NFD', text))
+
+
+def DPR_normalize(text):
+    return DPR_tokenize(text).words(uncased=True)
+
+
+# Source: https://github.com/shmsw25/qa-hard-em/blob/master/prepro_util.py
+def strip_accents(text):
+    """Strips accents from a piece of text."""
+    text = unicodedata.normalize("NFD", text)
+    output = []
+    for char in text:
+      cat = unicodedata.category(char)
+      if cat == "Mn":
+        continue
+      output.append(char)
+    return "".join(output)
diff --git a/utility/utils/qa_loaders.py b/utility/utils/qa_loaders.py
new file mode 100644
index 0000000000000000000000000000000000000000..06565a521d7d3e7100d71ad1ac76ba0a844387a7
--- /dev/null
+++ b/utility/utils/qa_loaders.py
@@ -0,0 +1,33 @@
+import os
+import ujson
+
+from collections import defaultdict
+from colbert.utils.utils import print_message, file_tqdm
+
+
+def load_collection_(path, retain_titles):
+    with open(path) as f:
+        collection = []
+
+        for line in file_tqdm(f):
+            _, passage, title = line.strip().split('\t')
+
+            if retain_titles:
+                passage = title + ' | ' + passage
+
+            collection.append(passage)
+
+    return collection
+
+
+def load_qas_(path):
+    print_message("#> Loading the reference QAs from", path)
+
+    triples = []
+
+    with open(path) as f:
+        for line in f:
+            qa = ujson.loads(line)
+            triples.append((qa['qid'], qa['question'], qa['answers']))
+
+    return triples
diff --git a/utility/utils/save_metadata.py b/utility/utils/save_metadata.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2b391c030efb1bb75f719ec3175911339841487
--- /dev/null
+++ b/utility/utils/save_metadata.py
@@ -0,0 +1,60 @@
+from colbert.utils.utils import dotdict
+import os
+import sys
+import git
+import time
+import copy
+import ujson
+import socket
+
+
+def get_metadata_only():
+    args = dotdict()
+
+    args.hostname = socket.gethostname()
+    try:
+        args.git_branch = git.Repo(search_parent_directories=True).active_branch.name
+        args.git_hash = git.Repo(search_parent_directories=True).head.object.hexsha
+        args.git_commit_datetime = str(git.Repo(search_parent_directories=True).head.object.committed_datetime)
+    except git.exc.InvalidGitRepositoryError as e:
+        pass
+    args.current_datetime = time.strftime('%b %d, %Y ; %l:%M%p %Z (%z)')
+    args.cmd = ' '.join(sys.argv)
+
+    return args
+
+
+def get_metadata(args):
+    args = copy.deepcopy(args)
+
+    args.hostname = socket.gethostname()
+    args.git_branch = git.Repo(search_parent_directories=True).active_branch.name
+    args.git_hash = git.Repo(search_parent_directories=True).head.object.hexsha
+    args.git_commit_datetime = str(git.Repo(search_parent_directories=True).head.object.committed_datetime)
+    args.current_datetime = time.strftime('%b %d, %Y ; %l:%M%p %Z (%z)')
+    args.cmd = ' '.join(sys.argv)
+
+    try:
+        args.input_arguments = copy.deepcopy(args.input_arguments.__dict__)
+    except:
+        args.input_arguments = None
+
+    return dict(args.__dict__)
+
+# TODO:  No reason for deepcopy. But: (a) Call provenance() on objects that can, (b) Only save simple, small objects. No massive lists or models or weird stuff!
+# With that, I think we don't even need (necessarily) to restrict things to input_arguments.
+
+def format_metadata(metadata):
+    assert type(metadata) == dict
+
+    return ujson.dumps(metadata, indent=4)
+
+
+def save_metadata(path, args):
+    assert not os.path.exists(path), path
+
+    with open(path, 'w') as output_metadata:
+        data = get_metadata(args)
+        output_metadata.write(format_metadata(data) + '\n')
+
+    return data