Training Tools: Picking Your Weapon

You have your data. Now you need the software to push that data into the model weights. Depending on your computer's OS and GPU, you have three primary choices.

1. Unsloth (Best for NVIDIA / Windows / Linux)

Unsloth is currently the "Gold Standard" for local fine-tuning.

• Why: It is 2x faster and uses 70% less VRAM than the official Meta or Hugging Face scripts.
• Ease of Use: It provides Google Colab notebooks so you can train for free in the cloud and then download the file to your local Ollama.
• Output: It can export directly to GGUF, so it’s ready for Ollama instantly.

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2. Axolotl (Best for Power Users)

If you have a server with multiple GPUs and want the absolute most control over every math setting.

• Why: Used by the experts who build the top models on the Hugging Face Leaderboard.
• Ease of Use: Hard. It uses complex YAML configuration files.

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3. MLX (Best for Apple Silicon / Mac)

If you have a Mac Studio or a MacBook Pro with an M2/M3/M4 chip.

• Why: Built by Apple researchers. It is optimized specifically for the "Unified Memory" architecture of the Mac.
• Tool: Use mlx-tuning-fork or mlx-lm projects on GitHub.

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4. The Workflow (Unsloth Example)

1. Format: Convert your data to the Unsloth JSON format.
2. Train: Run the training script (it usually takes 30-60 minutes for a LoRA).
3. Evaluate: Ask the model a few questions to see if it learned the new behavior.
4. Export: Export the result as an adapter_model.bin or a combined GGUF.

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5. Cloud Training (The "Cheat" Code)

If your local computer is too slow, you can use Google Colab or RunPod.

1. Rent an A100 GPU for $1.00/hour.
2. Run an Unsloth notebook.
3. In 30 minutes, you'll have a trained LoRA.
4. Download the LoRA to your laptop and run it in Ollama.

Total cost for a custom-trained model: $0.50.

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Key Takeaways

• Unsloth is the recommended tool for almost all local training tasks.
• Axolotl is for large-scale enterprise server training.
• MLX is the native choice for Apple Silicon users.
• You can Train in the cloud and Run locally to save money and time.