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import gradio as gr
import subprocess
import os
from pathlib import Path
def run_training():
"""Run the fine-tuning process and stream output."""
output_text = "Starting training...\n\n"
yield output_text
# Run the training script
process = subprocess.Popen(
["python", "finetune.py"],
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
text=True,
bufsize=1
)
# Stream output
for line in process.stdout:
output_text += line
yield output_text
process.wait()
if process.returncode == 0:
output_text += "\n\n✅ Training completed successfully!"
output_text += f"\n\nModel saved to: {os.path.abspath('./qwen-codeforces-cots')}"
else:
output_text += f"\n\n❌ Training failed with exit code {process.returncode}"
yield output_text
def check_gpu():
"""Check GPU availability."""
import torch
if torch.cuda.is_available():
gpu_name = torch.cuda.get_device_name(0)
gpu_memory = torch.cuda.get_device_properties(0).total_memory / 1e9
return f"✅ GPU Available: {gpu_name} ({gpu_memory:.1f} GB)"
else:
return "❌ No GPU available - training will be slow!"
# Create Gradio interface
with gr.Blocks(title="Qwen3 Fine-tuning on Codeforces") as demo:
gr.Markdown("""
# 🚀 Qwen3-0.5B Fine-tuning on Codeforces CoTs
Fine-tuning Qwen3-0.5B-Instruct on competitive programming problems with chain-of-thought reasoning.
**Dataset**: open-r1/codeforces-cots (~48K examples)
**Method**: QLoRA (LoRA + 4-bit quantization)
**Training**: 1000 steps with checkpoints every 200 steps
""")
gpu_status = gr.Textbox(label="GPU Status", value=check_gpu(), interactive=False)
gr.Markdown("### Training Configuration")
gr.Markdown("""
- **Model**: Qwen/Qwen2.5-0.5B-Instruct
- **Batch Size**: 1 (with gradient accumulation of 16)
- **Learning Rate**: 2e-4
- **Max Steps**: 1000
- **LoRA Rank**: 16
- **Trainable Parameters**: ~8.8M (1.75% of total)
""")
start_btn = gr.Button("🎯 Start Training", variant="primary", size="lg")
output = gr.Textbox(
label="Training Output",
lines=25,
max_lines=50,
show_copy_button=True
)
start_btn.click(
fn=run_training,
inputs=[],
outputs=[output]
)
gr.Markdown("""
### 📝 Notes
- Training will take several hours depending on GPU speed
- Checkpoints are saved every 200 steps to `./qwen-codeforces-cots/`
- You can download the final model after training completes
- The model will be compatible with the base Qwen2.5-0.5B-Instruct architecture
""")
if __name__ == "__main__":
demo.launch()
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