updated model.py

model.py

@@ -1,63 +1,23 @@
-# import os
-# from transformers import AutoModelForCausalLM, AutoTokenizer
-# import torch
-
-# # Correct model name
-# MODEL_NAME = "bigcode/starcoder"
-
-# # Ensure the token is provided
-# HF_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
-# if not HF_TOKEN:
-#     raise ValueError("Missing Hugging Face token. Set HUGGINGFACE_TOKEN as an environment variable.")
-
-# # Set device
-# device = "cuda" if torch.cuda.is_available() else "cpu"
-
-# # Load tokenizer with authentication
-# tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, token=HF_TOKEN)
-
-# # Load model with optimizations
-# model = AutoModelForCausalLM.from_pretrained(
-#     MODEL_NAME,
-#     token=HF_TOKEN,
-#     torch_dtype=torch.float16,  # Reduce memory usage
-#     low_cpu_mem_usage=True,     # Optimize loading
-#     device_map="auto",         # Automatic device placement
-#     offload_folder="offload"    # Offload to disk if needed
-# ).to(device)
-
-# def generate_code(prompt: str, max_tokens: int = 256):
-#     """Generates code based on the input prompt."""
-#     if not prompt.strip():
-#         return "Error: Empty prompt provided."
-
-#     inputs = tokenizer(prompt, return_tensors="pt").to(device)
-#     output = model.generate(**inputs, max_new_tokens=max_tokens)
-#     return tokenizer.decode(output[0], skip_special_tokens=True)
-
 import os
-from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
-MODEL_NAME = "bigcode/starcoderbase-3b"  # Lighter version
+MODEL_NAME = "bigcode/starcoderbase-1b"
 HF_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
 
-quant_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_quant_type="nf4",
-    bnb_4bit_compute_dtype=torch.float16
-)
+# Force CPU mode
+device = "cpu"  # Change this from "cuda"
 
 tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, token=HF_TOKEN)
 model = AutoModelForCausalLM.from_pretrained(
     MODEL_NAME,
     token=HF_TOKEN,
-    quantization_config=quant_config,
-    device_map="auto",
+    torch_dtype=torch.float16,  # Keep memory low
+    device_map="auto",  # Still allows auto placement
     trust_remote_code=True
-)
+).to(device)
 
 def generate_code(prompt: str, max_tokens: int = 256):
-    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
     output = model.generate(**inputs, max_new_tokens=max_tokens)
-    return tokenizer.decode(output[0], skip_special_tokens=True)
+    return tokenizer.decode(output[0], skip_special_tokens=True)