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Fine-tuning with Single GPU

This recipe steps you through how to finetune a Meta Llama 3 model on the text summarization task using the samsum dataset on a single GPU.

These are the instructions for using the canonical finetuning script in the llama-recipes package.

Requirements

Ensure that you have installed the llama-recipes package (details).

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

  1. PEFT to use parameter-efficient finetuning.
  2. bitsandbytes for int8 quantization.

How to run it?

python -m finetuning.py  --use_peft --peft_method lora --quantization --use_fp16 --model_name /patht_of_model_folder/8B --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

Note

In case you are using a multi-GPU machine please make sure to only make one of them visible using export CUDA_VISIBLE_DEVICES=GPU:id.

How to run with different datasets?

Currently 3 open source datasets are supported that can be found in Datasets config file. You can also use your custom dataset (more info here).

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

  • alpaca_dataset : to get this open source data please download the aplaca.json to 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 finetuning.py  --use_peft --peft_method lora --quantization  --dataset grammar_dataset --model_name /patht_of_model_folder/8B --output_dir Path/to/save/PEFT/model

# alpaca_dataset

python -m finetuning.py  --use_peft --peft_method lora --quantization  --dataset alpaca_dataset --model_name /patht_of_model_folder/8B --output_dir Path/to/save/PEFT/model


# samsum_dataset

python -m finetuning.py  --use_peft --peft_method lora --quantization  --dataset samsum_dataset --model_name /patht_of_model_folder/8B --output_dir Path/to/save/PEFT/model

FLOPS Counting and Pytorch Profiling

To help with benchmarking effort, we are adding the support for counting the FLOPS during the fine-tuning process. You can achieve this by setting --flop_counter when launching your single/multi GPU fine-tuning. Use --flop_counter_start to choose which step to count the FLOPS. It is recommended to allow a warm-up stage before using the FLOPS counter.

Similarly, you can set --use_profiler flag and pass a profiling output path using --profiler_dir to capture the profile traces of your model using PyTorch profiler. To get accurate profiling result, the pytorch profiler requires a warm-up stage and the current config is wait=1, warmup=2, active=3, thus the profiler will start the profiling after step 3 and will record the next 3 steps. Therefore, in order to use pytorch profiler, the --max-train-step has been greater than 6. The pytorch profiler would be helpful for debugging purposes. However, the --flop_counter and --use_profiler can not be used in the same time to ensure the measurement accuracy.