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Model: dizza01/qwen2.5-7b-finetunerag-merged
Source: Original Platform

handler.py

@@ -0,0 +1,61 @@
+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,
+        )
+
+        # Ensure pad token exists for generation
+        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 __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))
+
+        # Accept either plain string input or chat-style messages
+        if isinstance(inputs, list):
+            prompt = self.tokenizer.apply_chat_template(
+                inputs,
+                tokenize=False,
+                add_generation_prompt=True,
+            )
+        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,
+                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}