chore: clean up temp files

quantize_to_bnb_proper.py

@@ -1,106 +0,0 @@
-#!/usr/bin/env python3
-"""Properly quantize model to BnB 4-bit using BnB API."""
-
-import argparse
-import gc
-import torch
-from pathlib import Path
-from transformers import AutoModelForCausalLM, AutoConfig
-
-
-def quantize_model(model_path, output_path):
-    """Load bf16 model, properly quantize to BnB 4-bit, save."""
-    
-    print(f"Loading model from: {model_path}")
-    model = AutoModelForCausalLM.from_pretrained(
-        model_path,
-        device_map="cpu",
-        torch_dtype=torch.bfloat16,
-        trust_remote_code=True,
-        low_cpu_mem_usage=True,
-    )
-    print(f"✓ Model loaded to CPU (~70GB bf16)")
-    
-    # Count parameters
-    total_params = sum(p.numel() for p in model.parameters())
-    print(f"   Total parameters: {total_params / 1e9:.2f}B")
-    
-    # Apply PEFT prepare for k-bit training
-    print("\nApplying PEFT prepare_model_for_kbit_training...")
-    from peft import prepare_model_for_kbit_training
-    model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=False)
-    print("✓ Model prepared for k-bit training")
-    
-    # Quantize using BnB's actual API
-    print("Quantizing with BnB 4-bit...")
-    from bitsandbytes.nn import Linear4bit
-    from torch import nn
-    
-    quantized_count = 0
-    for name, module in list(model.named_modules()):
-        if isinstance(module, nn.Linear) and 'lm_head' not in name:
-            # Create new Linear4bit with proper quantization
-            new_module = Linear4bit(
-                module.in_features,
-                module.out_features,
-                bias=module.bias is not None,
-                compute_dtype=torch.float16,
-                quant_type='nf4',
-            )
-            
-            # Copy weights (BnB will quantize during forward)
-            with torch.no_grad():
-                new_module.weight.data = module.weight.data.clone()
-                if module.bias is not None:
-                    new_module.bias.data = module.bias.data.clone()
-            
-            # Replace in model
-            layers = name.split('.')
-            parent = model
-            for layer in layers[:-1]:
-                parent = getattr(parent, layer)
-            setattr(parent, layers[-1], new_module)
-            quantized_count += 1
-    
-    print(f"✓ Quantized {quantized_count} linear layers to 4-bit")
-    
-    # Count quantized parameters
-    bnb_params = sum(
-        1 for p in model.parameters() 
-        if hasattr(p, 'quant_state') and p.quant_state is not None
-    )
-    print(f"   Quantized modules: {bnb_params}")
-    
-    # Save model config
-    print(f"\nSaving to: {output_path}")
-    model.config.save_pretrained(output_path)
-    
-    # Save weights
-    model.save_pretrained(output_path)
-    print("✓ Model saved")
-    
-    # Free memory
-    del model
-    gc.collect()
-    torch.cuda.empty_cache()
-    
-    print("\nDone! Model is ready for QLoRA training.")
-    print(f"Save location: {output_path}")
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Quantize model to BnB 4-bit")
-    parser.add_argument("--model-path", type=str, 
-                       default="/data/models/Ornith-1.0-35B",
-                       help="Path to bf16 model")
-    parser.add_argument("--output-path", type=str,
-                       default="/data/models/Ornith-1.0-35B-bnb-4bit",
-                       help="Output path for quantized model")
-    args = parser.parse_args()
-    
-    Path(args.output_path).mkdir(parents=True, exist_ok=True)
-    quantize_model(args.model_path, args.output_path)
-
-
-if __name__ == "__main__":
-    main()