@mrs83/flowertune_smollm2-360m-instruct-finance

FlowerTune LLM on Finance Dataset

This directory conducts federated instruction tuning with a pretrained SmolLM2-360M-Instruct model 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 experiment performs federated LLM fine-tuning with DoRA using the 🤗PEFT library.

The clients' models are aggregated with FedAvg strategy.

SmolLM2-360M-Instruct

Precision: bf16 for model weights.
Quantization: 4-bit quantization for reduced memory usage.
Optimizer: paged_adamw_8bit
DoRA Configuration:
- Rank (r): 16
- Alpha: 64
- Target Modules:
  - down_proj
  - up_proj
  - gate_proj
Training Configuration:
- Batch size: 16
- Maximum number of steps: 8
- Total number of rounds: 1
- Fraction fit per round: 0.1
Learning Rate Scheduler:
- Cosine Annealing over rounds, where:
  - Maximum LR: 2e-4
  - Minimum LR: 6e-6
- Constant learning rate scheduler over steps
Strategy: FedAvg

Environments setup

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

pip install -e .

To run this on AMD ROCm, install with:

pip install -e ".[rocm]" --extra-index-url https://download.pytorch.org/whl/rocm7.1/

Running the experiment

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

Model saving

The global PEFT model checkpoints are saved every 1 round 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.

Flower App by ethicalabs.ai - AI/ML research and development - HuggingFace