Update app.py

app.py

@@ -1,3 +1,99 @@
+# from fastapi import FastAPI, HTTPException
+# from pydantic import BaseModel
+# from transformers import AutoModelForCausalLM, AutoTokenizer
+# import torch
+# from huggingface_hub import snapshot_download
+# from safetensors.torch import load_file
+
+# class ModelInput(BaseModel):
+#     prompt: str
+#     max_new_tokens: int = 50
+
+# app = FastAPI()
+
+# # Define model paths
+# base_model_path = "HuggingFaceTB/SmolLM2-135M-Instruct"
+# adapter_path = "khurrameycon/SmolLM-135M-Instruct-qa_pairs_converted.json-25epochs"
+
+# try:
+#     # First load the base model
+#     print("Loading base model...")
+#     model = AutoModelForCausalLM.from_pretrained(
+#         base_model_path,
+#         torch_dtype=torch.float16,
+#         trust_remote_code=True,
+#         device_map="auto"
+#     )
+    
+#     # Load tokenizer from base model
+#     print("Loading tokenizer...")
+#     tokenizer = AutoTokenizer.from_pretrained(base_model_path)
+    
+#     # Download adapter weights
+#     print("Downloading adapter weights...")
+#     adapter_path_local = snapshot_download(adapter_path)
+    
+#     # Load the safetensors file
+#     print("Loading adapter weights...")
+#     state_dict = load_file(f"{adapter_path_local}/adapter_model.safetensors")
+    
+#     # Load state dict into model
+#     model.load_state_dict(state_dict, strict=False)
+    
+#     print("Model and adapter loaded successfully!")
+
+# except Exception as e:
+#     print(f"Error during model loading: {e}")
+#     raise
+
+# def generate_response(model, tokenizer, instruction, max_new_tokens=128):
+#     """Generate a response from the model based on an instruction."""
+#     try:
+#         messages = [{"role": "user", "content": instruction}]
+#         input_text = tokenizer.apply_chat_template(
+#             messages, tokenize=False, add_generation_prompt=True
+#         )
+        
+#         inputs = tokenizer.encode(input_text, return_tensors="pt").to(model.device)
+#         outputs = model.generate(
+#             inputs,
+#             max_new_tokens=max_new_tokens,
+#             temperature=0.2,
+#             top_p=0.9,
+#             do_sample=True,
+#         )
+        
+#         response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+#         return response
+    
+#     except Exception as e:
+#         raise ValueError(f"Error generating response: {e}")
+
+# @app.post("/generate")
+# async def generate_text(input: ModelInput):
+#     try:
+#         response = generate_response(
+#             model=model,
+#             tokenizer=tokenizer,
+#             instruction=input.prompt,
+#             max_new_tokens=input.max_new_tokens
+#         )
+#         return {"generated_text": response}
+    
+#     except Exception as e:
+#         raise HTTPException(status_code=500, detail=str(e))
+
+# @app.get("/")
+# async def root():
+#     return {"message": "Welcome to the Model API!"}
+
+
+
+
+
+
+# //////////////////////////////////////////
+
 from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
 from transformers import AutoModelForCausalLM, AutoTokenizer
@@ -40,6 +136,10 @@ try:
     # Load state dict into model
     model.load_state_dict(state_dict, strict=False)
     
+    # Optional: Set the model to use the adapter
+    # In case you are using adapters, you need to activate them
+    model.set_active_adapters(adapter_path)  # Activating the adapter
+    
     print("Model and adapter loaded successfully!")
 
 except Exception as e:
@@ -85,4 +185,4 @@ async def generate_text(input: ModelInput):
 
 @app.get("/")
 async def root():
-    return {"message": "Welcome to the Model API!"}
+    return {"message": "Welcome to the Model API!"}