Fine Tune a model with MLX for Ollama

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AI Summary

Video Summary: Fine-Tuning AI Models

Introduction to Fine-Tuning

• AI models are impressive in their knowledge and response capabilities.
• The goal is to improve AI models to be more personalized and efficient.
• Fine-tuning is the process of adjusting a model’s output style, rather than teaching it new information.

Fine-Tuning Process Overview

• Fine-tuning involves modifying model weights to change response style and format.
• It is simpler than it appears and does not require expert knowledge.
• The video uses the mlx tool for fine-tuning, which supports Apple silicon.

Step-by-Step Fine-Tuning Guide

Step 1: Prepare a Dataset

• Create a dataset of questions and answers for the model to learn from.
• Visit ama.com to find the mistol model and understand the expected question-answer format.
• Save the formatted questions and answers in a .jsonl file, where each line is a new object with a key “text” and the formatted string as the value.
• Generate at least 50-100 examples, focusing on the style of response rather than new facts.
• The example used involves summarizing video scripts to train the model to mimic the author’s speaking and writing style.
• Divide the dataset into training (60%), validation (20%), and test (20%) files.

Step 2: Run the Fine-Tuning Process

• Install mlx with pip install mlx LM.
• Access the mistol model on Hugging Face and log in using the Hugging Face CLI.
• Run the fine-tuning command using mlx lm with appropriate flags for training, model, data, and batch size.
• The process creates a new directory with the fine-tuned model’s adapter files.

Step 3: Use the New Adapter

• Define a new model using the adapter from the fine-tuning process.
• Use olama create command to create the model file.
• Start using the fine-tuned model with AMA run.

Conclusion

• The process is not as intimidating as it may seem.
• The author acknowledges that the fine-tuning is not perfect and requires further refinement.
• The video ends with an invitation for viewers to share their fine-tuning experiences and ideas.

(Note: The summary does not include any URLs or CLI commands as none were provided in the text.)