File size: 3,878 Bytes
4942b80 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | #!/usr/bin/env python3
"""
Export fine-tuned Gemma 4 model to various formats.
Usage:
# Export LoRA adapter to merged model + GGUF
python scripts/export_model.py --model checkpoints/finetuned/lora_adapter
# Push to HuggingFace Hub
python scripts/export_model.py --model checkpoints/finetuned/lora_adapter \
--push-to-hub username/gemma4-finetuned
# Export specific GGUF quantization
python scripts/export_model.py --model checkpoints/finetuned/lora_adapter \
--gguf-quant q8_0
"""
import argparse
import os
from unsloth import FastModel
def parse_args():
parser = argparse.ArgumentParser(description="Export fine-tuned Gemma 4 model")
parser.add_argument("--model", type=str, required=True,
help="Path to fine-tuned LoRA adapter")
parser.add_argument("--max-seq-length", type=int, default=2048)
parser.add_argument("--output-dir", type=str, default="checkpoints/finetuned",
help="Base output directory")
# Export options
parser.add_argument("--no-merged", action="store_true",
help="Skip merged 16-bit export")
parser.add_argument("--no-gguf", action="store_true",
help="Skip GGUF export")
parser.add_argument("--gguf-quant", type=str, default="q4_k_m",
choices=["q4_k_m", "q8_0", "f16"],
help="GGUF quantization method")
parser.add_argument("--push-to-hub", type=str, default=None,
help="HuggingFace Hub repo to push to (e.g. username/model-name)")
return parser.parse_args()
def main():
args = parse_args()
print("=" * 60)
print("Gemma 4 Model Export")
print("=" * 60)
print(f"Model: {args.model}")
print(f"Output dir: {args.output_dir}")
print("=" * 60)
# Load model
print("\nLoading model...")
model, tokenizer = FastModel.from_pretrained(
model_name=args.model,
max_seq_length=args.max_seq_length,
load_in_4bit=True,
)
# Export merged model
if not args.no_merged:
merged_path = os.path.join(args.output_dir, "merged")
print(f"\nExporting merged 16-bit model to {merged_path}...")
model.save_pretrained_merged(
merged_path,
tokenizer,
save_method="merged_16bit",
)
print(f" Done! Size: {get_dir_size(merged_path)}")
# Export GGUF
if not args.no_gguf:
gguf_path = os.path.join(args.output_dir, f"gguf_{args.gguf_quant}")
print(f"\nExporting GGUF ({args.gguf_quant}) to {gguf_path}...")
model.save_pretrained_gguf(
gguf_path,
tokenizer,
quantization_method=args.gguf_quant,
)
print(f" Done! Size: {get_dir_size(gguf_path)}")
# Push to Hub
if args.push_to_hub:
print(f"\nPushing to HuggingFace Hub: {args.push_to_hub}...")
# Push LoRA adapter
model.push_to_hub(args.push_to_hub, tokenizer)
print(" Pushed LoRA adapter")
# Push GGUF
model.push_to_hub_gguf(
args.push_to_hub,
tokenizer,
quantization_method=args.gguf_quant,
)
print(f" Pushed GGUF ({args.gguf_quant})")
print("\nExport complete!")
def get_dir_size(path):
"""Get human-readable directory size."""
total = 0
if os.path.isdir(path):
for dirpath, _, filenames in os.walk(path):
for f in filenames:
fp = os.path.join(dirpath, f)
total += os.path.getsize(fp)
elif os.path.isfile(path):
total = os.path.getsize(path)
for unit in ["B", "KB", "MB", "GB"]:
if total < 1024:
return f"{total:.1f} {unit}"
total /= 1024
return f"{total:.1f} TB"
if __name__ == "__main__":
main()
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