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Finetuning with Adapter

Adapter, first introduced for the LLaMA model as LLaMA-Adapter, is a form of prefix-tuning that prepends a learnable adaption-prompt to the inputs of the attention blocks in an LLM. In total, there are only ~500k parameters to update during finetuning in StableLM 3B, which significantly reduces the memory footprint and speeds up training.

We are able to demonstrate instruction-finetuning Lit-GPT StableLM 3B on the Alpaca dataset on a single RTX 3060 GPU. If using 8 GPUs, finetuning can be completed in under 1 hour.

If you are new to Adapter and are interested to learn more about how it works before proceeding with the finetuning guide below, you might find our article Understanding Parameter-Efficient Finetuning of Large Language Models: From Prefix Tuning to LLaMA-Adapters helpful.

LLaMA-Adapter v2 extends the original LLaMA-Adapter idea by adding trainable bias and scale parameters to each linear layer in the transformer. Furthermore, LLaMA-Adapter v2 makes the normalization layers trainable. Where the StableLM 3B model has 500k trainable parameters with GPT v1, GPT-Adapter v2 adds an additional 1.5 M trainable parameter for the bias and scale parameters and ~300k trainable parameters for the normalization layers. So, adapter v2 has ~2.3 M trainable parameters in total.

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.
  3. Download the data and generate the Alpaca instruction tuning dataset:
python scripts/prepare_alpaca.py --checkpoint_dir checkpoints/stabilityai/stablelm-base-alpha-3b

or prepare your own dataset.

For more information about dataset preparation, also see the prepare_dataset.md tutorial.

Running the finetuning

python finetune/adapter.py --io.checkpoint_dir checkpoints/stabilityai/stablelm-base-alpha-3b

or for Adapter V2

python finetune/adapter_v2.py --io.checkpoint_dir checkpoints/stabilityai/stablelm-base-alpha-3b

The finetuning requires at least one GPU with ~12 GB memory. 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. To fit Adapter V2 to 12GB memory set --micro_batch_size 2.

For example, the following settings will let you finetune the model in under 1 hour:

--devices 4 --train.micro_batch_size 4

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:

python finetune/adapter.py --io.out_dir out/adapter/my-model-finetuned

or for Adapter V2

python finetune/adapter_v2.py --io.out_dir out/adapter_v2/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

python finetune/adapter.py --io.out_dir out/adapter/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.

Quantization

Optionally, finetuning using quantization can be enabled via the --quantize flag, for example using the 4-bit NormalFloat data type:

python finetune/adapter.py --quantize "bnb.nf4"

or using adapter_v2 with double-quantization:

python finetune/adapter_v2.py --quantize "bnb.nf4-dq"

For additional benchmarks and resource requirements, please see the Resource Tables.

Test the model

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

python generate/adapter.py \
    --prompt "Recommend a movie to watch on the weekend." \
    --checkpoint_dir checkpoints/stabilityai/stablelm-base-alpha-3b

or for Adapter V2

python generate/adapter_v2.py \
    --prompt "Recommend a movie to watch on the weekend." \
    --checkpoint_dir checkpoints/stabilityai/stablelm-base-alpha-3b

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

With only a few modifications, you can prepare and train on your own instruction dataset.

  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 an 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. Make a copy of scripts/prepare_alpaca.py and name it what you want:

    cp scripts/prepare_alpaca.py scripts/prepare_mydata.py

  3. Modify scripts/prepare_mydata.py to read the json data file.

  4. Run the script to generate the preprocessed, tokenized train-val split:

    python scripts/prepare_mydata.py --destination_path data/mydata/

  5. Run finetune/adapter.py by passing in the location of your data (and optionally other parameters):

    python finetune/adapter.py \
    --io.train_data_dir data/mydata --io.val_data_dir data/mydata/ \
    --io.checkpoint_dir checkpoints/stabilityai/stablelm-base-alpha-3b \
    --io.out_dir data/mydata-finetuned