| """ | |
| Stack 2.9 - Simple Local Chat | |
| Run the fine-tuned model locally on your machine | |
| """ | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| MODEL_PATH = "/Users/walidsobhi/stack-2-9-final-model" | |
| print("Loading model...") | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| MODEL_PATH, | |
| torch_dtype=torch.float16, | |
| device_map="auto", | |
| trust_remote_code=True | |
| ) | |
| print("Model loaded!\n") | |
| def chat(prompt): | |
| messages = [ | |
| {"role": "system", "content": "You are a helpful coding assistant."}, | |
| {"role": "user", "content": prompt} | |
| ] | |
| text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) | |
| inputs = tokenizer([text], return_tensors="pt").to(model.device) | |
| outputs = model.generate( | |
| **inputs, | |
| max_new_tokens=512, | |
| temperature=0.7, | |
| do_sample=True, | |
| pad_token_id=tokenizer.pad_token_id | |
| ) | |
| response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return response[len(text):].strip() | |
| # Interactive loop | |
| while True: | |
| prompt = input("You: ") | |
| if prompt.lower() in ['quit', 'exit']: | |
| break | |
| print("Thinking...") | |
| print(f"Bot: {chat(prompt)}\n") | |