初始化项目，由ModelHub XC社区提供模型

Model: karmakorma/sasbuddylm-v3-merged
Source: Original Platform

handler.py

@@ -0,0 +1,73 @@
+"""Custom endpoint handler for HF Inference Endpoints.
+
+Bypasses the default huggingface_inference_toolkit auto-pipeline (which was
+crashing at model-load on this Qwen2.5-Coder merge). Loads explicitly in bf16,
+handles both plain-string and chat-message inputs, returns the generated text
+in the shape the MCP client expects: [{"generated_text": "..."}].
+"""
+from __future__ import annotations
+
+from typing import Any
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+class EndpointHandler:
+    def __init__(self, path: str = ""):
+        print(f"[handler] Loading tokenizer from {path}")
+        self.tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
+        if self.tokenizer.pad_token is None:
+            self.tokenizer.pad_token = self.tokenizer.eos_token
+
+        print(f"[handler] Loading model from {path} in bfloat16")
+        self.model = AutoModelForCausalLM.from_pretrained(
+            path,
+            torch_dtype=torch.bfloat16,
+            device_map="auto",
+            trust_remote_code=True,
+        )
+        self.model.eval()
+        print("[handler] Ready")
+
+    def __call__(self, data: dict[str, Any]) -> list[dict[str, str]]:
+        inputs = data.get("inputs", "")
+        params = data.get("parameters", {}) or {}
+
+        max_new_tokens = int(params.get("max_new_tokens", 1500))
+        temperature = float(params.get("temperature", 0.1))
+        top_p = float(params.get("top_p", 0.95))
+        do_sample = bool(params.get("do_sample", temperature > 0.01))
+        return_full_text = bool(params.get("return_full_text", False))
+
+        # Accept either a plain string or a list of chat messages.
+        if isinstance(inputs, list) and inputs and isinstance(inputs[0], dict):
+            prompt_text = self.tokenizer.apply_chat_template(
+                inputs, tokenize=False, add_generation_prompt=True
+            )
+        else:
+            prompt_text = inputs
+
+        tokenized = self.tokenizer(
+            prompt_text,
+            return_tensors="pt",
+            truncation=True,
+            max_length=8192,
+        ).to(self.model.device)
+
+        with torch.no_grad():
+            outputs = self.model.generate(
+                **tokenized,
+                max_new_tokens=max_new_tokens,
+                temperature=temperature if do_sample else 1.0,
+                top_p=top_p,
+                do_sample=do_sample,
+                pad_token_id=self.tokenizer.eos_token_id,
+                eos_token_id=self.tokenizer.eos_token_id,
+            )
+
+        prompt_len = tokenized["input_ids"].shape[-1]
+        new_tokens = outputs[0] if return_full_text else outputs[0][prompt_len:]
+        text = self.tokenizer.decode(new_tokens, skip_special_tokens=True)
+
+        return [{"generated_text": text}]