🧠 LLM Fine-Tuner

Your own custom AI — no coding, no PhD, no drama.
Upload your data → click Train → get a ready-to-use model in minutes.

🤔 What is this?

Imagine you could take a smart AI assistant and teach it to be an expert in your specific topic — your business, your writing style, your data. That's exactly what fine-tuning is.

LLM Fine-Tuner lets you do it through a simple visual interface. No programming knowledge needed. If you can use a spreadsheet and a web browser, you can fine-tune an AI model.

What you can build:

• 💼 A customer support bot that knows your products inside and out
• ✍️ A writing assistant that matches your exact tone and style
• 🏥 A domain expert trained on your specialised knowledge
• 🎮 A character AI with a specific personality
• 📚 A Q&A tool trained on your own documents

✨ Why people love it

• No coding — Everything happens through a point-and-click interface
• Works on regular hardware — Even a gaming laptop with 8 GB of GPU memory is enough
• Fast — Train a model in minutes, not days
• Your data stays yours — Everything runs on your own machine
• Export anywhere — Use your model in Ollama, LM Studio, or share it online

🖼️ Gallery

⚡ What fine-tuning actually looks like

Think of fine-tuning like a master craftsman shaping raw material into a precision tool. The AI already knows a lot — you're just focusing that knowledge toward exactly what you need.

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🚀 Supercharged with Unsloth

LLM Fine-Tuner uses Unsloth under the hood — a turbo engine that makes training 2–5× faster and uses dramatically less memory. Even a regular gaming GPU becomes a fine-tuning powerhouse.

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🔓 Heretic Mode

Some AI models have built-in restrictions that get in the way. Heretic Mode lets you remove those restrictions with one click. Use it responsibly — with great power comes great responsibility.

🚀 Get Started in 2 Minutes

The Easy Way (recommended)

# Download the project
git clone https://github.com/Yog-Sotho/LLM-fine-tuner.git
cd LLM-fine-tuner

# Run the installer — it handles everything for you
chmod +x install.sh && ./install.sh

The installer will ask you a few yes/no questions. When it's done, launch with:

source llm_finetuner_env/bin/activate
llm-finetune

Open http://localhost:7860 in your browser. You're ready to train.

Want to skip all the questions?

./install.sh --yes

Just want to try it quickly?

pip install -r requirements.txt
python main.py

Using Google Colab? You can run this entirely in your browser with a free GPU. See the full installation guide.

📋 How it works — step by step

Step 1 — Prepare your data

Create a simple spreadsheet (CSV file) with two columns:

That's it. No special formatting, no coding. Just questions and the answers you want the model to give.

Don't have a CSV? The tool also accepts PDFs, Excel files, JSON, plain text files, and more — or it can write the questions and answers for you from your documents (PDF, Word, text) with an AI model.

Step 2 — Upload and train

1. Open the app (it runs in your browser)
2. Click 📂 Data and upload your file
3. Click 🚀 Training, pick a preset, and hit ▶ Start Training
4. Watch the progress bar — training takes minutes to an hour depending on your hardware

Step 3 — Test and export

1. Go to 💬 Inference and type a question
2. Your model responds using everything it learned from your data
3. When happy, go to 📤 Share to download or publish your model

📂 What data formats does it accept?

🎛️ Training Options (Plain English)

You don't need to understand all of these to get started — the defaults work great. But here's what they mean if you're curious:

📊 Supported Training Modes

Large models: multi-GPU presets for DDP, FSDP2, FSDP + QLoRA and DeepSpeed ZeRO-2/3 (configs/accelerate/), mixture-of-experts support (router kept stable, load balancing on) and long-sequence options (activation offloading, padding-free batches) — see docs/10_advanced.md.

📤 Export Options

Once trained, your model can be:

• Downloaded as a ZIP — keep a backup on your computer
• Published to HuggingFace Hub — share it with the world (or keep it private)
• Exported as GGUF — run it offline with Ollama or LM Studio on any computer, even without internet
• Served with vLLM — high-speed serving for multiple users at once

🗺️ What's been built, what's coming

Already available:

• ✅ Visual interface — no coding needed
• ✅ All major training modes (SFT, DPO, RLHF, ORPO)
• ✅ GGUF export for Ollama & LM Studio
• ✅ Heretic Mode
• ✅ Command-line mode for power users
• ✅ Batch evaluation tools
• ✅ Data augmentation
• ✅ Training data from your documents — an AI model writes and rates the questions and answers
• ✅ Docker image — one-command setup with no dependencies
• ✅ Multi-GPU training
• ✅ Vision + language models
• ✅ Embedding-model fine-tuning for search / RAG — with before/after retrieval scores

📖 Documentation

New to fine-tuning? The docs are written for non-technical users. Read them as a website at yog-sotho.github.io/LLM-fine-tuner, or right here:

🤝 Contributing

Contributions welcome! Fork the repo, make your changes, and open a pull request with a clear description of what you did and why.

Found a bug? Open an issue with your OS, GPU model, and the full error message.

📜 License

GPL-3.0 — free to use, modify, and share. Attribution appreciated ❤️