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P.S. TuneKit trending at #19 on Product Hunt (Day of Launch) with 88 followers & 50+ Upvotes - check it out!
demo.mp4
See it in action
Fine-tuning LLMs is powerful but painful. You need to:
- Set up CUDA, PyTorch, and a dozen dependencies
- Rent expensive GPUs or wait hours on slow hardware
- Write training scripts, manage hyperparameters, handle OOM errors
- Figure out how to export and deploy your model
Most developers give up before they start.
TuneKit eliminates all of that. Upload your data, answer two questions, and get a ready-to-run Colab notebook. Click "Run All" and your fine-tuned model is ready in ~15 minutes.
Your Data → TuneKit → Colab Notebook → Fine-tuned Model
No local GPU. No dependencies. No cost (uses Google's free T4).
| Step | What You Do | What TuneKit Does |
|---|---|---|
| 1 | Upload JSONL file | Validates format, analyzes patterns |
| 2 | Answer 2 questions | Recommends optimal model + hyperparameters |
| 3 | Click "Get Notebook" | Generates pre-configured Colab notebook |
| 4 | Hit "Run All" in Colab | Trains on free T4 GPU (~15 min) |
| 5 | Download your model | Export as LoRA, GGUF, or merged weights |
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Powered by Unsloth - 2x faster training, 70% less VRAM.
| Model | Parameters | Best For |
|---|---|---|
| Phi-4 Mini | 3.8B | Classification, extraction, structured output |
| Llama 3.2 | 1B, 3B | Q&A, conversational AI, context tracking |
| Mistral 7B | 7B | Long-form generation, complex reasoning |
| Qwen 2.5 | 1.5B, 3B | Multilingual, JSON output, structured data |
| Gemma 2 | 2B | Edge deployment, mobile, fast inference |
TuneKit uses the standard conversation format:
{"messages": [{"role": "user", "content": "What's the capital of France?"}, {"role": "assistant", "content": "Paris is the capital of France."}]}
{"messages": [{"role": "system", "content": "You are a helpful coding assistant."}, {"role": "user", "content": "Write hello world in Python"}, {"role": "assistant", "content": "print('Hello, World!')"}]}Requirements:
- JSONL format (one JSON object per line)
- Each line has a
messagesarray - Messages have
role(user/assistant/system) andcontent - Minimum 50 examples (100-1000 recommended)
# Clone the repo
git clone https://github.com/riyanshibohra/TuneKit.git
cd TuneKit
# Install dependencies
pip install -r requirements.txt
# Start the server
uvicorn api.main:app --reloadOpen http://localhost:8000 in your browser.
- Frontend: Vanilla JS, CSS
- Backend: FastAPI, Python
- Training: Unsloth, Transformers, PEFT
- Infrastructure: Google Colab (free T4 GPU)
Contributions are welcome! Feel free to:
- Fork the repo
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
MIT License - use it for anything.
Built with lots of caffeine + curiosity.