Delete handler.py

handler.py

@@ -1,46 +0,0 @@
-# handler.py
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from peft import PeftModel
-
-# Base model that your LoRA was trained on (must match training)
-BASE_MODEL = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"   # change if you trained on a different DeepSeek variant
-ADAPTER_PATH = "GilbertAkham/deepseek-R1-multitask-lora"
-
-class EndpointHandler:
-    def __init__(self, path=""):
-        print("🚀 Loading base model...")
-        self.tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
-
-        # Load base model
-        base_model = AutoModelForCausalLM.from_pretrained(
-            BASE_MODEL,
-            torch_dtype=torch.float16,
-            device_map="auto",
-            trust_remote_code=True
-        )
-
-        print(f"🔗 Attaching LoRA adapter from {ADAPTER_PATH}...")
-        # Load the LoRA adapter properly
-        self.model = PeftModel.from_pretrained(base_model, ADAPTER_PATH)
-        self.model.eval()
-
-        print("✅ Model + LoRA adapter loaded successfully.")
-
-    def __call__(self, data):
-        prompt = data.get("inputs", "")
-        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
-
-        with torch.no_grad():
-            outputs = self.model.generate(
-                **inputs,
-                max_new_tokens=512,
-                temperature=0.7,
-                top_p=0.9,
-                do_sample=True,
-                pad_token_id=self.tokenizer.eos_token_id,
-                eos_token_id=self.tokenizer.eos_token_id,
-            )
-
-        text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
-        return {"generated_text": text}