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import gradio as gr
import torch
import os
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
from peft import PeftModel
from huggingface_hub import HfApi
import shutil
import gc
# Configuration - UPDATE THESE
LORA_REPO = "your-username/qwen25vl-lora-adapter" # Your LoRA repo
OUTPUT_REPO = "your-username/qwen25vl-invoice-merged" # Output repo
BASE_MODEL = "unsloth/Qwen2.5-VL-7B-Instruct"
HF_TOKEN = os.environ.get("HF_TOKEN") # Set in Space secrets
def merge_model():
"""Merge LoRA with base model and upload to Hub"""
try:
# Use Space's disk efficiently
work_dir = "/tmp/merge"
if os.path.exists(work_dir):
shutil.rmtree(work_dir)
os.makedirs(work_dir)
# Update status
yield "Loading base model..."
# Load model with CPU offload to save GPU memory
model = Qwen2VLForConditionalGeneration.from_pretrained(
BASE_MODEL,
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True,
cache_dir=work_dir,
low_cpu_mem_usage=True
)
yield "Loading processor..."
processor = AutoProcessor.from_pretrained(
BASE_MODEL,
trust_remote_code=True,
cache_dir=work_dir
)
yield "Loading LoRA adapter from Hub..."
model = PeftModel.from_pretrained(model, LORA_REPO)
yield "Merging weights... This may take a few minutes..."
model = model.merge_and_unload()
# Clear GPU cache
torch.cuda.empty_cache()
gc.collect()
yield "Saving merged model..."
output_dir = os.path.join(work_dir, "merged")
os.makedirs(output_dir, exist_ok=True)
# Save with smaller shards
model.save_pretrained(
output_dir,
max_shard_size="2GB",
safe_serialization=True
)
processor.save_pretrained(output_dir)
yield "Uploading to HuggingFace Hub..."
api = HfApi(token=HF_TOKEN)
# Create output repo
api.create_repo(OUTPUT_REPO, exist_ok=True, private=True)
# Upload the merged model
api.upload_folder(
folder_path=output_dir,
repo_id=OUTPUT_REPO,
repo_type="model",
commit_message="Merged LoRA adapter with base model"
)
# Cleanup
shutil.rmtree(work_dir)
yield f"✅ Success! Model merged and uploaded to: {OUTPUT_REPO}"
except Exception as e:
yield f"❌ Error: {str(e)}"
# Cleanup on error
if os.path.exists("/tmp/merge"):
shutil.rmtree("/tmp/merge")
# Create Gradio interface
def create_interface():
with gr.Blocks(title="Qwen2.5-VL LoRA Merger") as demo:
gr.Markdown(
"""
# Qwen2.5-VL LoRA Merger
This Space will merge your LoRA adapter with the base model and upload to HuggingFace.
**Configuration:**
- Base Model: `{}`
- LoRA Adapter: `{}`
- Output Repo: `{}`
""".format(BASE_MODEL, LORA_REPO, OUTPUT_REPO)
)
status = gr.Textbox(label="Status", lines=10)
merge_btn = gr.Button("Start Merge", variant="primary")
merge_btn.click(
fn=merge_model,
inputs=[],
outputs=[status]
)
return demo
if __name__ == "__main__":
demo = create_interface()
demo.launch() |