flwr new @zikaizhang/t-ioi-finance-smollm2

FlowerTune LLM on Finance Dataset

This directory conducts federated instruction tuning with pretrained SmolLM2 Series Models: SmolLM2-135M-Instruct, SmolLM2-360M-Instruct, SmolLM2-135M and SmolLM2-360M on a Finance dataset. We use Flower Datasets to download, partition and preprocess the dataset. Flower's Simulation Engine is used to simulate the LLM fine-tuning process in federated way, which allows users to perform the training on a single GPU.

Methodology

This baseline performs federated LLM fine-tuning with LoRA using the 🤗PEFT library. The clients' models are aggregated with FedAvg strategy. This provides a baseline performance for the leaderboard of Finance challenge.

Environments setup

Project dependencies are defined in pyproject.toml. Install them in an activated Python environment with:

pip install -e .

Experimental setup

The dataset is divided into 50 partitions in an IID fashion, a partition is assigned to each ClientApp. We randomly sample a fraction (0.1) of the total nodes to participate in each round, for a total of 50 rounds. All settings are defined in pyproject.toml.

Running the challenge

Then, follow the instruction here to log in your account. Note you only need to complete this stage once in your development machine:

huggingface-cli login

Run the challenge with default config values. The configs are defined in [tool.flwr.app.config] entry of pyproject.toml, and are loaded automatically.

flwr run

Benchmark

All the experiments are conducted on a NVIDIA GeForce GTX 1080 (8 GB).

Challenges	FPB	FIQA	TFNS	Avg
SmolLM2-135M-Instruct (50Rounds)	28.63	59.21	21.60	36.48
SmolLM2-135M (50Rounds)	29.86	59.53	21.06	36.81
SmolLM2-360M-Instruct (50Rounds)	52.64	66.11	37.93	52.22
SmolLM2-360M (50Rounds)	53.79	21.71	52.42	42.64

VRAM consumption

We use models with 4-bit quantization as default. The estimated VRAM consumption per client for each challenge is shown below:

Models	SmolLM2-135M-Instruct (BS=16)	SmolLM2-360M-Instruct (BS=8)	SmolLM2-135M (BS=16)	SmolLM2-360M (BS=8)
VRAM	6.96 GB	4.01 GB	6.19 GB	4.14 GB
Comm	886.23 MB	1570.31 MB	886.23 MB	1570.31 MB

You can adjust the CPU/GPU resources you assign to each of the clients based on your device, which are specified with options.backend.client-resources.num-cpus and options.backend.client-resources.num-gpus under [tool.flwr.federations.local-simulation] entry in pyproject.toml.

Model saving

The global PEFT model checkpoints are saved every 5 rounds after aggregation on the sever side as default, which can be specified with train.save-every-round under [tool.flwr.app.config] entry in pyproject.toml.