llama-recipes/docs/single_gpu.md

Fine-tuning with Single GPU

To run fine-tuning on a single GPU, we will make use of two packages

1- PEFT methods and in specific using HuggingFace PEFTlibrary.

2- bitsandbytes int8 quantization.

Given combination of PEFT and Int8 quantization, we would be able to fine_tune a Llama 2 7B model on one consumer grade GPU such as A10.

Requirements

To run the examples, make sure to install the llama-recipes package (See README.md for details).

Please note that the llama-recipes package will install PyTorch 2.0.1 version, in case you want to run FSDP + PEFT, please make sure to install PyTorch nightlies.

How to run it?

Get access to a machine with one GPU or if using a multi-GPU machine please make sure to only make one of them visible using export CUDA_VISIBLE_DEVICES=GPU:id and run the following. It runs by default with samsum_dataset for summarization application.

python -m llama_recipes.finetuning  --use_peft --peft_method lora --quantization --use_fp16 --model_name /patht_of_model_folder/7B --output_dir Path/to/save/PEFT/model

The args used in the command above are:

• --use_peft boolean flag to enable PEFT methods in the script

• --peft_method to specify the PEFT method, here we use lora other options are llama_adapter, prefix.

• --quantization boolean flag to enable int8 quantization

How to run with different datasets?

Currently 4 datasets are supported that can be found in Datasets config file.

• grammar_dataset : use this notebook to pull and process theJfleg and C4 200M datasets for grammar checking.

• alpaca_dataset : to get this open source data please download the aplaca.json to ft_dataset folder.

wget -P src/llama_recipes/datasets https://raw.githubusercontent.com/tatsu-lab/stanford_alpaca/main/alpaca_data.json

• samsum_dataset

to run with each of the datasets set the dataset flag in the command as shown below:

# grammer_dataset

python -m llama_recipes.finetuning  --use_peft --peft_method lora --quantization  --dataset grammar_dataset --model_name /patht_of_model_folder/7B --output_dir Path/to/save/PEFT/model

# alpaca_dataset

python -m llama_recipes.finetuning  --use_peft --peft_method lora --quantization  --dataset alpaca_dataset --model_name /patht_of_model_folder/7B --output_dir Path/to/save/PEFT/model


# samsum_dataset

python -m llama_recipes.finetuning  --use_peft --peft_method lora --quantization  --dataset samsum_dataset --model_name /patht_of_model_folder/7B --output_dir Path/to/save/PEFT/model

Where to configure settings?

• Training config file is the main config file that help to specify the settings for our run can be found in

It let us specify the training settings, everything from model_name to dataset_name, batch_size etc. can be set here. Below is the list of supported settings:

model_name: str="PATH/to/LLAMA 2/7B"
enable_fsdp: bool= False
run_validation: bool=True
batch_size_training: int=4
gradient_accumulation_steps: int=1
num_epochs: int=3
num_workers_dataloader: int=2
lr: float=2e-4
weight_decay: float=0.0
gamma: float= 0.85
use_fp16: bool=False
mixed_precision: bool=True
val_batch_size: int=4
dataset = "samsum_dataset" # alpaca_dataset,grammar_dataset
peft_method: str = "lora" # None , llama_adapter, prefix
use_peft: bool=False
output_dir: str = "./ft-output"
freeze_layers: bool = False
num_freeze_layers: int = 1
quantization: bool = False
one_gpu: bool = False
save_model: bool = False
dist_checkpoint_root_folder: str="model_checkpoints"
dist_checkpoint_folder: str="fine-tuned"
save_optimizer: bool=False

• Datasets config file provides the available options for datasets.

• peft config file provides the supported PEFT methods and respective settings that can be modified.