mlx-examples/lora/README.md

LoRA

This is an example of using MLX to fine-tune a Llama 7B¹ model with low rank adaptation (LoRA)² for a target task.

In this example we'll use the WikiSQL³ dataset to train the LLM to generate SQL queries from natural language. However, the example is intended to be general should you wish to modify the task.

Setup

Install the dependencies:

pip install -r requirements.txt

Next, download and convert the model. If you do not have access to the model weights you will need to request access from Meta.

Convert the weights with:

python convert.py <path_to_torch_weights> mlx_llama_7B.npz

Run

The main script is lora.py. To see a full list of options run

python lora.py --help

To fine-tune a model use:

python lora.py --model mlx_llama_7B.npz \
               --tokenizer tokenizer.model \
               --train \
               --iters 600 \

By default, the adapter weights are saved in adapters.npz. You can specify the output location with --adapter_file.

To compute test set perplexity use

python lora.py --model mlx_llama_7B.npz \
               --tokenizer tokenizer.model \
               --data data \
               --test 

For generation use

python lora.py --model mlx_llama_7B.npz \
               --tokenizer tokenizer.model \
               --num-tokens 50 \
               --prompt "table: 1-10015132-16
columns: Player, No., Nationality, Position, Years in Toronto, School/Club Team
Q: What is terrence ross' nationality
A: "

Results

The initial validation loss for Llama 7B on the WikiSQL is 2.66 and the final validation loss after 1000 iterations is 1.23. The table below shows the training and validation loss at a few points over the course of training.

Iteration	Train Loss	Validation Loss
1	N/A	2.659
200	1.264	1.405
400	1.201	1.303
600	1.123	1.274
800	1.017	1.255
1000	1.070	1.230

After training for 1000 iterations, the validation perplexity reduces to XX.

The model trains at around 475 tokens per second on an M2 Ultra.

---

1. Refer to the arXiv paper and blog post for more details. ↩︎

2. Refer to the arXiv paper for more details on LoRA. ↩︎

3. Refer to the GitHub repo for more information about WikiSQL. ↩︎