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README.md

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+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- chat
+---
+
+# Rex-v0.1-1.5B

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 1000000.0,
+  "sliding_window": 32768,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.40.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "pad_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "repetition_penalty": 1.1,
+  "temperature": 0.7,
+  "top_p": 0.8,
+  "top_k": 20,
+  "transformers_version": "4.37.0"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:302e327795994403cb1e3cb6a3345c76b246b894d14078c936b570c83a4e9057
+size 3087467144

tokenization_rex_qwen2.py

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+from transformers import Qwen2Tokenizer
+import bm25s
+from bm25s.hf import BM25HF
+# import Stemmer  # optional: for stemming
+
+# define the RexQwen2Tokenizer class that is a subclass of Qwen2Tokenizer
+
+class RexQwen2Tokenizer(Qwen2Tokenizer): 
+    # to simplify the __init__ method 
+    def __init__(
+        self,
+        rex_index_name = "Llama-3-Magpie-Pro-1M-v0.1",
+        rex_size = 3,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.rex_index_name = rex_index_name 
+        hf_repo_name = f"yuchenlin/BM25S_index_{self.rex_index_name}"
+        self.retriever = BM25HF.load_from_hub(
+            hf_repo_name, revision="main", load_corpus=True
+        ) 
+        self.rex_size = rex_size
+        self.user_prefix = "<|im_start|>user"
+
+    def _rex_query(self, query):
+        k = self.rex_size
+        query_tokens = bm25s.tokenize(query, show_progress=False)
+        results, scores = self.retriever.retrieve(query_tokens, k=k)
+        rex_chat_history = []
+        for i in range(results.shape[1]):
+            doc, score = results[0, i], scores[0, i]
+            rex_query = doc["query"]
+            rex_response = doc["response"]
+            rex_chat_history.append({"role": "user", "content": rex_query})
+            rex_chat_history.append({"role": "assistant", "content": rex_response})
+        rex_chat_history_tokens = self.apply_chat_template(rex_chat_history, tokenize=False, add_generation_prompt=False)
+        start_user = rex_chat_history_tokens.index(self.user_prefix)
+        rex_chat_history_tokens = rex_chat_history_tokens[start_user:]
+        return rex_chat_history_tokens
+
+
+
+    # redefine the _tokenize method
+    def tokenize(self, text):   
+        # find the index for first user query 
+        if self.user_prefix not in text:
+            # the query is not wrapped with chat template yet 
+            # raise NotImplementedError
+            return super().tokenize(text)
+        start_index = text.index(self.user_prefix)
+        rex_chat_history_tokens = self._rex_query(text[start_index+len(self.user_prefix):])
+        rex_text = text[:start_index] + rex_chat_history_tokens + text[start_index:]
+        print(rex_text)
+        tokens = super().tokenize(rex_text)
+        return tokens
+
+
+
+if __name__ == "__main__":
+    from transformers import AutoTokenizer
+    model_path = "/net/nfs/mosaic/yuchenl/Qwen2-1.5B-Instruct"
+    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, rex_size=3)
+    # tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-1.5B-Instruct", trust_remote_code=True)
+    messages = [
+        {"role": "user", "content": "Who is Yuchen Lin?"},
+        {"role": "assistant", "content": "Yuchen Lin is a NLP researcher."},
+        {"role": "user", "content": "Can I ask him a question?"}
+    ]
+    query = tokenizer.apply_chat_template(messages, tokenize=False) 
+    print(tokenizer.tokenize(query))
+    

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": ["<|im_start|>", "<|im_end|>"],
+  "bos_token": null,
+  "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "model_max_length": 32768,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "RexQwen2Tokenizer",
+  "unk_token": null,
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_rex_qwen2.RexQwen2Tokenizer",
+      null
+      ]
+  }
+}