初始化项目，由ModelHub XC社区提供模型

Model: dizza01/medalpaca-13b
Source: Original Platform

handler.py

@@ -0,0 +1,77 @@
+import os
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+class EndpointHandler:
+    def __init__(self, path: str = ""):
+        model_dir = path or "/repository"
+
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            model_dir,
+            trust_remote_code=True,
+        )
+        if self.tokenizer.pad_token_id is None:
+            self.tokenizer.pad_token = self.tokenizer.eos_token
+
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_dir,
+            trust_remote_code=True,
+            torch_dtype=torch.float16,
+            low_cpu_mem_usage=True,
+            device_map="auto",
+        )
+        self.model.eval()
+
+    def _messages_to_prompt(self, inputs):
+        # Use chat template only if both the method and a non-empty template exist
+        if hasattr(self.tokenizer, "apply_chat_template") and getattr(
+            self.tokenizer, "chat_template", None
+        ):
+            return self.tokenizer.apply_chat_template(
+                inputs,
+                tokenize=False,
+                add_generation_prompt=True,
+            )
+        # Fallback for plain causal LMs with no chat_template (e.g. MedAlpaca)
+        parts = []
+        for msg in inputs:
+            role = (msg.get("role") or "user").upper()
+            content = msg.get("content", "")
+            parts.append(f"[{role}]\n{content}")
+        parts.append("[ASSISTANT]\n")
+        return "\n\n".join(parts)
+
+    def __call__(self, data):
+        inputs = data.get("inputs", "")
+        params = data.get("parameters", {}) or {}
+
+        max_new_tokens = int(params.get("max_new_tokens", 128))
+        temperature = float(params.get("temperature", 0.0))
+        top_p = float(params.get("top_p", 1.0))
+        do_sample = bool(params.get("do_sample", temperature > 0))
+        repetition_penalty = float(params.get("repetition_penalty", 1.0))
+        no_repeat_ngram_size = int(params.get("no_repeat_ngram_size", 0))
+
+        if isinstance(inputs, list):
+            prompt = self._messages_to_prompt(inputs)
+        else:
+            prompt = str(inputs)
+
+        enc = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
+
+        with torch.no_grad():
+            out = self.model.generate(
+                **enc,
+                max_new_tokens=max_new_tokens,
+                temperature=temperature,
+                top_p=top_p,
+                do_sample=do_sample,
+                repetition_penalty=repetition_penalty,
+                no_repeat_ngram_size=no_repeat_ngram_size,
+                pad_token_id=self.tokenizer.pad_token_id,
+                eos_token_id=self.tokenizer.eos_token_id,
+            )
+
+        generated_ids = out[0][enc["input_ids"].shape[-1]:]
+        text = self.tokenizer.decode(generated_ids, skip_special_tokens=True)
+        return {"generated_text": text}