#!/usr/bin/env python3
"""Quantize Ornith-1.0-35B to 4-bit NF4 using bitsandbytes (recommended way)."""

import torch
from transformers import AutoModelForCausalLM, BitsAndBytesConfig, AutoTokenizer

def quantize_model(model_path, output_path):
    print(f"Quantizing model from: {model_path}")
    print("This will use bitsandbytes NF4 quantization with double quantization.\n")

    bnb_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_quant_type="nf4",
        bnb_4bit_compute_dtype=torch.bfloat16,
        bnb_4bit_use_double_quant=True,
    )

    print("Loading model with 4-bit quantization (this may take a while)...")
    model = AutoModelForCausalLM.from_pretrained(
        model_path,
        quantization_config=bnb_config,
        device_map="auto",
        low_cpu_mem_usage=True,
        trust_remote_code=True,
    )

    print("\nSaving quantized model...")
    model.save_pretrained(output_path)
    
    # Also save tokenizer if it exists
    try:
        tokenizer = AutoTokenizer.from_pretrained(model_path)
        tokenizer.save_pretrained(output_path)
    except:
        pass

    print(f"\n✅ Quantized model saved to: {output_path}")
    print("You can now load it with: AutoModelForCausalLM.from_pretrained(..., load_in_4bit=True)")

if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument("--model-path", type=str, default="/data/models/Ornith-1.0-35B")
    parser.add_argument("--output-path", type=str, default="/data/models/Ornith-1.0-35B-4bit-nf4")
    args = parser.parse_args()
    
    quantize_model(args.model_path, args.output_path)