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| import sys | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| from peft import PeftModel | |
| import torch | |
| # ------------------------- | |
| # CONFIGURATION | |
| # ------------------------- | |
| BASE_MODEL = "microsoft/phi-3-medium-4k-instruct" | |
| LORA_MODEL = "KayHamla06/Milo_WBA" # Hugging Face repo ID | |
| # ------------------------- | |
| # LOAD TOKENIZER + MODEL | |
| # ------------------------- | |
| print("Loading tokenizer...") | |
| tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) | |
| print("Loading base model...") | |
| model = AutoModelForCausalLM.from_pretrained( | |
| BASE_MODEL, | |
| torch_dtype=torch.float16, | |
| device_map="auto" | |
| ) | |
| print("Applying LoRA fine-tune...") | |
| model = PeftModel.from_pretrained(model, LORA_MODEL) | |
| model.eval() | |
| # ------------------------- | |
| # INFERENCE FUNCTION | |
| # ------------------------- | |
| def generate_response(prompt: str) -> str: | |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) | |
| output_tokens = model.generate( | |
| **inputs, | |
| max_new_tokens=300, | |
| temperature=0.7, | |
| top_p=0.9, | |
| do_sample=True, | |
| pad_token_id=tokenizer.eos_token_id | |
| ) | |
| return tokenizer.decode(output_tokens[0], skip_special_tokens=True) | |
| # ------------------------- | |
| # RUN PROMPT FROM TERMINAL | |
| # ------------------------- | |
| if __name__ == "__main__": | |
| if len(sys.argv) < 2: | |
| print("Usage: python model.py \"your prompt here\"") | |
| sys.exit(1) | |
| prompt = sys.argv[1] | |
| print("\n--- MILO RESPONSE ---\n") | |
| print(generate_response(prompt)) | |