48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
#!/usr/bin/env python3
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"""Quantize Ornith-1.0-35B to 4-bit NF4 using bitsandbytes (recommended way)."""
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import torch
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from transformers import AutoModelForCausalLM, BitsAndBytesConfig, AutoTokenizer
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def quantize_model(model_path, output_path):
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print(f"Quantizing model from: {model_path}")
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print("This will use bitsandbytes NF4 quantization with double quantization.\n")
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.bfloat16,
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bnb_4bit_use_double_quant=True,
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)
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print("Loading model with 4-bit quantization (this may take a while)...")
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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quantization_config=bnb_config,
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device_map="auto",
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low_cpu_mem_usage=True,
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trust_remote_code=True,
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)
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print("\nSaving quantized model...")
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model.save_pretrained(output_path)
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# Also save tokenizer if it exists
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try:
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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tokenizer.save_pretrained(output_path)
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except:
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pass
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print(f"\n✅ Quantized model saved to: {output_path}")
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print("You can now load it with: AutoModelForCausalLM.from_pretrained(..., load_in_4bit=True)")
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument("--model-path", type=str, default="/data/models/Ornith-1.0-35B")
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parser.add_argument("--output-path", type=str, default="/data/models/Ornith-1.0-35B-nf4")
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args = parser.parse_args()
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quantize_model(args.model_path, args.output_path)
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