Fine-Tuning Language Models on Apple Hardware: A Comprehensive Guide

Introduction to Fine-Tuning Language Models on Apple Hardware

Fine-tuning language models has become an essential skill for AI practitioners looking to customize models for specific tasks. With Apple's MLX framework, it's now possible to perform this process on Apple hardware. This comprehensive guide will walk you through the intricacies of fine-tuning, using a practical example of teaching a model to perform calculations.

Setting Up the Environment

Before diving into fine-tuning, you need to set up your environment:

1. Install Apple's MLX LM framework:

   pip install mlx_lm
   

2. This installation provides command-line utilities for interacting with and fine-tuning models.

Exploring Base Models

Let's start by examining some base models:

Llama 2 3B Instruct

The default model used by MLX is a 4-bit quantized version of Llama 2 3B Instruct. It's a compact model that offers decent performance for its size.

Qwen Models

For our fine-tuning experiments, we'll use Qwen models. They come in various sizes:

• 500 million parameters
• 1.5 billion parameters
• 3 billion parameters
• 7 billion parameters

This range allows us to demonstrate the impact of model size on fine-tuning results.

Basic Model Interaction

Before fine-tuning, let's interact with the base models:

mlx_lm.generate --model qwen/qwen1_5-0.5b-chat "Tell me a limerick about cheese"

You'll notice that smaller models struggle with complex tasks like generating limericks, while larger models perform better.

Fine-Tuning Process

Preparing the Dataset

The first step in fine-tuning is preparing your dataset. For our example, we'll create a dataset teaching the model to use a calculator for arithmetic operations.

Create a JSONL file with prompt-completion pairs:

{"prompt": "Could you sum 4 and 9 for me?", "completion": "The result is calculator(4 + 9)"}

Ensure you have separate files for training, testing, and validation.

Fine-Tuning Command

Use the following command to start fine-tuning:

mlx_lm.lora --model qwen/qwen1_5-7b-chat --train_data path/to/train.jsonl --learning_rate 1e-5 --num_epochs 100 --fine_tune_type full

This command uses:

• The 7B Qwen model
• A learning rate of 1e-5
• 100 training epochs
• Full fine-tuning (as opposed to LoRA)

Monitoring Training Progress

During training, you'll see output indicating:

• Number of trainable parameters
• Training loss
• Validation loss
• Tokens processed per second

For the 7B model, expect around 95 tokens per second on Apple hardware.

Evaluating Fine-Tuned Models

After fine-tuning, evaluate your model:

mlx_lm.generate --model qwen/qwen1_5-7b-chat --adapter_path path/to/adapters "Could you add 2665 to 1447?"

You should see the model now using the calculator format for arithmetic operations.

Challenges in Fine-Tuning

Data Diversity

One major challenge is maintaining data diversity. If your training data lacks variety, the model may:

• Give short responses consistently
• Forget how to perform other tasks
• Overgeneralize the calculator pattern

Model Size Considerations

Smaller models (like the 500M parameter version) are more susceptible to these issues, while larger models (7B+) are more robust.

Advanced Fine-Tuning Techniques

LoRA (Low-Rank Adaptation)

LoRA is a technique that updates only a small subset of model parameters:

mlx_lm.lora --model qwen/qwen1_5-7b-chat --train_data path/to/train.jsonl --learning_rate 1e-5 --num_epochs 100 --fine_tune_type lora --num_lora_layers 4

Benefits of LoRA:

• Faster training (328 tokens/second vs. 95 for full fine-tuning)
• Lower memory usage (17GB vs. 45GB)
• Reduced risk of catastrophic forgetting

Mixers

Mixers help prevent the model from forgetting previously learned tasks. Create a "general" dataset with various tasks and mix it with your specific fine-tuning data.

Creating a Chat Model

To fine-tune a chat model:

1. Format your data in a chat style:

   {"role": "user", "content": "What is 25 + 35?"}
   {"role": "assistant", "content": "The result is calculator(25 + 35) = 60"}
   

2. Use the mlx_lm.chat command for interaction:

   mlx_lm.chat --model qwen/qwen1_5-3b-chat --adapter_path path/to/adapters
   

Fusing Adapters

After fine-tuning, you can fuse the adapters with the base model:

mlx_lm.fuse --model qwen/qwen1_5-3b-chat --adapter_path path/to/adapters --save_path path/to/fused_model

This creates a standalone model incorporating your fine-tuned changes.

Conclusion

Fine-tuning language models on Apple hardware using MLX offers exciting possibilities for customizing AI for specific tasks. Key takeaways:

1. Choose an appropriate model size for your task and hardware constraints.
2. Maintain data diversity to prevent overfitting and task forgetting.
3. Consider advanced techniques like LoRA and mixers for more efficient and effective fine-tuning.
4. Be aware of the challenges, such as catastrophic forgetting and overgeneralization.
5. Experiment with different approaches to find the best balance for your specific use case.

By mastering these techniques, you can create powerful, customized language models tailored to your specific needs, all on Apple hardware.

Article created from: https://youtu.be/yOcUCnLgvt8?si=30sjZOrgnnUGpZTQ