lora

Fine-Tuning with LoRA or QLoRA

This is an example of using MLX to fine-tune either a Llama 7B¹ or a Mistral 7B² model with low rank adaptation (LoRA)³ for a target task. The example also supports quantized LoRA (QLoRA).⁴

In this example we'll use the WikiSQL⁵ dataset to train the LLM to generate SQL queries from natural language. However, the example is intended to be general should you wish to use a custom dataset.

Contents

• Setup
• Run
  • Fine-tune
  • Evaluate
  • Generate
• Results
• Custom Data
• Memory Issues

Setup

Install the dependencies:

pip install -r requirements.txt

Next, download and convert the model. The Mistral weights can be downloaded with:

curl -O https://files.mistral-7b-v0-1.mistral.ai/mistral-7B-v0.1.tar
tar -xf mistral-7B-v0.1.tar

If you do not have access to the Llama weights you will need to request access from Meta.

Convert the model with:

python convert.py \
    --torch-path <path_to_torch_model> \
    --mlx-path <path_to_mlx_model>

If you wish to use QLoRA, then convert the model with 4-bit quantization using the -q option.

Run

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

python lora.py --help

Fine-tune

To fine-tune a model use:

python lora.py --model <path_to_model> \
               --train \
               --iters 600

If --model points to a quantized model, then the training will use QLoRA, otherwise it will use regular LoRA.

Note, the model path should have the MLX weights, the tokenizer, and the config.json which will all be output by the convert.py script.

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

You can resume fine-tuning with an existing adapter with --resume-adapter-file <path_to_adapters.npz>.

Evaluate

To compute test set perplexity use

python lora.py --model <path_to_model> \
               --adapter-file <path_to_adapters.npz> \
               --test 

Generate

For generation use

python lora.py --model <path_to_model> \
               --adapter-file <path_to_adapters.npz> \
               --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

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

Custom Data

You can make your own dataset for fine-tuning with LoRA. You can specify the dataset with --data=<my_data_directory>. Check the subdirectory data/ to see the expected format.

For fine-tuning (--train), the data loader expects a train.jsonl and a valid.jsonl to be in the data directory. For evaluation (--test), the data loader expects a test.jsonl in the data directory. Each line in the *.jsonl file should look like:

{"text": "This is an example for the model."}

Note other keys will be ignored by the loader.

Memory Issues

Fine-tuning a large model with LoRA requires a machine with a decent amount of memory. Here are some tips to reduce memory use should you need to do so:

1. Try quantization (QLoRA). You can use QLoRA by generating a quantized model with convert.py and the -q flag. See the Setup section for more details.

2. Try using a smaller batch size with --batch-size. The default is 4 so setting this to 2 or 1 will reduce memory consumption. This may slow things down a little, but will also reduce the memory use.

3. Reduce the number of layers to fine-tune with --lora-layers. The default is 16, so you can try 8 or 4. This reduces the amount of memory needed for back propagation. It may also reduce the quality of the fine-tuned model if you are fine-tuning with a lot of data.

4. Longer examples require more memory. If it makes sense for your data, one thing you can do is break your examples into smaller sequences when making the {train, valid, test}.jsonl files.

For example, for a machine with 32 GB the following should run reasonably fast:

python lora.py \
   --model <path_to_model> \
   --train \
   --batch-size 1 \
   --lora-layers 4

The above command on an M1 Max with 32 GB runs at about 250 tokens-per-second.

Footnotes

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

2. Refer to the blog post and github repository for more details. ↩

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

4. Refer to the paper QLoRA: Efficient Finetuning of Quantized LLMs ↩

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