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Finetuning the whole model

If you are interested in parameter-efficient finetuning, check out finetune_adapter.md. In contrast to parameter-efficient finetuning, this "full" approach finetunes all model parameters, which is substantially more expensive. It may only be recommended as a baseline for comparison studies.

Preparation

The steps here only need to be done once:

  1. Follow the instructions in the README to install the dependencies.
  2. Download and convert the weights following our guide.

LitGPT provides common datasets for finetuning, such as Alpaca, LIMA, Dolly, and more. You can optionally prepare your own dataset. For more information about dataset preparation, also see the prepare_dataset.md tutorial.

Running the finetuning

litgpt finetune_full tiiuae/falcon-7b \
  --data Alpaca \

Finetuning the falcon-7b model requires at least 8 GPUs with ~40 GB memory each.

You can speed up training by passing the devices argument to the script to utilize more GPUs if available. Depending on the available GPU memory, you can also tune the micro_batch_size parameter to utilize the GPU efficiently.

This script will save checkpoints periodically to the out_dir directory. If you are finetuning different models or on your own dataset, you can specify an output directory with your preferred name:

litgpt finetune_full tiiuae/falcon-7b \
  --data Alpaca \
  --out_dir out/full/my-model-finetuned

If your GPU does not support bfloat16, you can pass the --precision 32-true argument. For instance, to fine-tune on MPS (the GPU on modern Macs), you can run

litgpt finetune_full tiiuae/falcon-7b \
  --data Alpaca \
  --out_dir out/full/my-model-finetuned \
  --precision 32-true

Note that mps as the accelerator will be picked up automatically by Fabric when running on a modern Mac.

Test the model

You can test the finetuned model with your own instructions by running:

litgpt generate tiiuae/falcon-7b \
    --prompt "Recommend a movie to watch on the weekend." \
    --finetuned_path out/full/my-model-finetuned/lit_model_finetuned.pth

Output:

A good movie to watch on the weekend would be The Lion King, since it's a classic family film that everyone can enjoy...

If your GPU supports bfloat16, the script will automatically use it.

Tune on your dataset

You can easily train on your own instruction dataset saved in JSON format.

  1. Create a JSON file in which each row holds one instruction-response pair. A row has an entry for 'instruction', 'input', and 'output', where 'input' is optional and can be the empty string if the instruction doesn't require a context. Below is an example json file:

    [
    {
        "instruction": "Arrange the given numbers in ascending order.",
        "input": "2, 4, 0, 8, 3",
        "output": "0, 2, 3, 4, 8"
    },
    ...
]

  2. Run litgpt finetune by passing in the location of your data (and optionally other parameters):

    litgpt finetune tiiuae/falcon-7b \
    --data JSON \
    --data.json_path data/mydata.json \
    --out_dir data/mydata-finetuned