feat: simple NF4 quantization with device_map=auto (proven method)

quantize_model.py

@@ -1,12 +1,12 @@
 #!/usr/bin/env python3
-"""Quantize Ornith-1.0-35B to 4-bit NF4 using bitsandbytes (recommended way)."""
+"""Simple NF4 quantization using BnB with device_map auto-distribution."""
 
 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,
@@ -15,27 +15,35 @@ def quantize_model(model_path, output_path):
         bnb_4bit_use_double_quant=True,
     )
 
-    print("Loading model with 4-bit quantization (this may take a while)...")
+    print("Loading model with 4-bit quantization...")
+    print("   This will distribute across both GPUs automatically\n")
+
     model = AutoModelForCausalLM.from_pretrained(
         model_path,
         quantization_config=bnb_config,
         device_map="auto",
+        max_memory={0: "28GiB", 1: "28GiB"},  # Leave room for training
         low_cpu_mem_usage=True,
         trust_remote_code=True,
     )
 
-    print("\nSaving quantized model...")
+    print("\n✓ Model loaded and quantized")
+    print(f"   GPU 0: {torch.cuda.memory_allocated(0) / 1e9:.2f} GB")
+    print(f"   GPU 1: {torch.cuda.memory_allocated(1) / 1e9:.2f} GB")
+
+    print(f"\nSaving quantized model to: {output_path}")
     model.save_pretrained(output_path)
 
-    # Also save tokenizer if it exists
+    # Save tokenizer
     try:
         tokenizer = AutoTokenizer.from_pretrained(model_path)
         tokenizer.save_pretrained(output_path)
+        print("✓ Tokenizer saved")
     except:
-        pass
+        print("⚠ No tokenizer found")
 
     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