@mrs83/flowertune_qwen2.5-7b-instruct-medical

FlowerTune LLM on Medical Dataset

This directory conducts federated instruction tuning with a pretrained Qwen2.5-7B-Instruct model on a Medical 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 app performs federated LLM fine-tuning with LoRA using the 🤗PEFT library. The clients' models are aggregated with FedProx strategy.

Qwen2.5-7B-Instruct

Precision: bf16 for model weights.
Quantization: 4-bit quantization for reduced memory usage.
Optimizer: Paged AdamW 8-bit for effective optimization under constrained resources.
LoRA Configuration:
- Rank (r): 8
- Alpha: 32
- Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Training Configuration:
- Batch size: 16
- Maximum number of steps: 6
- Warmup steps: 2
- Total number of rounds: 1
- Fraction fit per round: 0.15
Learning Rate Scheduler: Constant learning rate scheduler with warmup steps, where:
- Maximum LR: 5e-5
- Minimum LR: 1e-6
Strategy: FedProx

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