Upload 21 files

added_tokens.json

@@ -0,0 +1,6 @@
+{
+  "<|assistant|>": 49154,
+  "<|end|>": 49155,
+  "<|system|>": 49152,
+  "<|user|>": 49153
+}

all_results.json

@@ -0,0 +1,14 @@
+{
+    "epoch": 5.9,
+    "eval_loss": 1.4719988107681274,
+    "eval_runtime": 5.2039,
+    "eval_samples": 202,
+    "eval_samples_per_second": 38.817,
+    "eval_steps_per_second": 1.345,
+    "perplexity": 4.35793713301621,
+    "train_loss": 0.9728257921006944,
+    "train_runtime": 2307.7787,
+    "train_samples": 3888,
+    "train_samples_per_second": 10.108,
+    "train_steps_per_second": 0.039
+}

config.json

@@ -0,0 +1,39 @@
+{
+  "_name_or_path": "data/starcoderplus-ift-v4.1",
+  "activation_function": "gelu",
+  "architectures": [
+    "GPTBigCodeForCausalLM"
+  ],
+  "attention_softmax_in_fp32": true,
+  "attn_pdrop": 0.1,
+  "bos_token_id": 0,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 0,
+  "inference_runner": 0,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "max_batch_size": null,
+  "max_sequence_length": null,
+  "model_type": "gpt_bigcode",
+  "multi_query": true,
+  "n_embd": 6144,
+  "n_head": 48,
+  "n_inner": 24576,
+  "n_layer": 40,
+  "n_positions": 8192,
+  "pad_key_length": true,
+  "pre_allocate_kv_cache": false,
+  "resid_pdrop": 0.1,
+  "scale_attention_softmax_in_fp32": true,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.28.1",
+  "use_cache": true,
+  "validate_runner_input": true,
+  "vocab_size": 49156
+}

dialogue_template.json

@@ -0,0 +1,12 @@
+{
+  "system": "",
+  "messages": null,
+  "system_token": "<|system|>",
+  "system_format": "standard",
+  "user_token": "<|user|>",
+  "assistant_token": "<|assistant|>",
+  "end_token": "<|end|>",
+  "mid_str": "\n",
+  "end_str": "\n",
+  "extra_end_text": ""
+}

eval_results.json

@@ -0,0 +1,9 @@
+{
+    "epoch": 5.9,
+    "eval_loss": 1.4719988107681274,
+    "eval_runtime": 5.2039,
+    "eval_samples": 202,
+    "eval_samples_per_second": 38.817,
+    "eval_steps_per_second": 1.345,
+    "perplexity": 4.35793713301621
+}

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "transformers_version": "4.28.1"
+}

handler.py

@@ -0,0 +1,49 @@
+from typing import Any, Dict
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from peft import PeftConfig, PeftModel
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # load model and processor from path
+        self.tokenizer = AutoTokenizer.from_pretrained(path)
+        try:
+            config = PeftConfig.from_pretrained(path)
+            model = AutoModelForCausalLM.from_pretrained(
+                config.base_model_name_or_path,
+                return_dict=True,
+                load_in_8bit=True,
+                device_map="auto",
+                torch_dtype=torch.float16,
+                trust_remote_code=True,
+            )
+            model.resize_token_embeddings(len(self.tokenizer))
+            model = PeftModel.from_pretrained(model, path)
+        except Exception:
+            model = AutoModelForCausalLM.from_pretrained(
+                path, device_map="auto", load_in_8bit=True, torch_dtype=torch.float16, trust_remote_code=True
+            )
+        self.model = model
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
+        # process input
+        inputs = data.pop("inputs", data)
+        parameters = data.pop("parameters", None)
+
+        # preprocess
+        inputs = self.tokenizer(inputs, return_tensors="pt").to(self.device)
+
+        # pass inputs with all kwargs in data
+        if parameters is not None:
+            outputs = self.model.generate(**inputs, **parameters)
+        else:
+            outputs = self.model.generate(**inputs)
+
+        # postprocess the prediction
+        prediction = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+        return [{"generated_text": prediction}]

model-00001-of-00001.safetensors

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+size 9857346344

model-00004-of-00004.safetensors

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+oid sha256:8b8ceee3f55cb5ab715ef4fbe35bf9178fff50f6cf51cad65a65b4dfcb797c41
+size 1362323832

model.safetensors.index.json

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