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handler.py

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+from typing import Dict, Any
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from peft import PeftModel
+
+
+class EndpointHandler:
+    def __init__(self, path: str = ""):
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        
+        bnb_config = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_quant_type="nf4",
+            bnb_4bit_compute_dtype=torch.bfloat16,
+            bnb_4bit_use_double_quant=True,
+        )
+        
+        base_model_id = "deepseek-ai/deepseek-coder-6.7b-instruct"
+        
+        self.tokenizer = AutoTokenizer.from_pretrained(path)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            base_model_id,
+            quantization_config=bnb_config,
+            device_map="auto",
+            trust_remote_code=True,
+        )
+        self.model = PeftModel.from_pretrained(self.model, path)
+        self.model.eval()
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        inputs = data.get("inputs", "")
+        parameters = data.get("parameters", {})
+        
+        max_new_tokens = parameters.get("max_new_tokens", 512)
+        temperature = parameters.get("temperature", 0.7)
+        top_p = parameters.get("top_p", 0.95)
+        do_sample = parameters.get("do_sample", True)
+        
+        if not inputs.startswith("### System:"):
+            prompt = f"""### System:
+You are an expert Minecraft Forge mod developer for version 1.21.11. Write clean, efficient, and well-structured Java code.
+
+### User:
+{inputs}
+
+### Assistant:
+"""
+        else:
+            prompt = inputs
+        
+        input_ids = self.tokenizer(prompt, return_tensors="pt").to(self.device)
+        
+        with torch.no_grad():
+            outputs = self.model.generate(
+                **input_ids,
+                max_new_tokens=max_new_tokens,
+                temperature=temperature,
+                top_p=top_p,
+                do_sample=do_sample,
+                pad_token_id=self.tokenizer.eos_token_id,
+            )
+        
+        generated_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        if "### Assistant:" in generated_text:
+            generated_text = generated_text.split("### Assistant:")[-1].strip()
+        
+        return {"generated_text": generated_text}