Installation#
For installing Trinity-RFT, you have three options: from source (recommended), via PyPI, or using Docker.
Before you begin, check your system setup:
If you have GPUs and want to use them:#
Make sure your system meets these requirements:
Python: 3.10 – 3.12
CUDA: 12.8 or higher
GPUs: At least 2 available
If you don’t have GPUs (or prefer not to use them):#
You can use the tinker option instead, which only requires:
Python: 3.11 – 3.12
GPUs: Not required
From Source (Recommended)#
This method is best if you plan to customize or contribute to Trinity-RFT.
1. Clone the Repository#
git clone https://github.com/modelscope/Trinity-RFT
cd Trinity-RFT
2. Set Up a Virtual Environment#
Choose one of the following options:
Using Conda#
conda create -n trinity python=3.12
conda activate trinity
pip install -e ".[vllm,flash_attn]"
# If you have no GPU, comment out the line above and uncomment this instead:
# pip install -e ".[tinker]"
# If you encounter issues when installing flash-attn, try:
# pip install flash-attn==2.8.1 --no-build-isolation
pip install -e ".[dev]" # for development like linting and debugging
Using venv#
python3.10 -m venv .venv
source .venv/bin/activate
pip install -e ".[vllm,flash_attn]"
# If you have no GPU, comment out the line above and uncomment this instead:
# pip install -e ".[tinker]"
# If you encounter issues when installing flash-attn, try:
# pip install flash-attn==2.8.1 --no-build-isolation
pip install -e ".[dev]" # for development like linting and debugging
Using uv#
uv is a modern Python package installer.
uv sync --extra vllm --extra dev --extra flash_attn
# If you have no GPU, try to use Tinker instead:
# uv sync --extra tinker --extra dev
Via PyPI#
If you just want to use the package without modifying the code:
pip install trinity-rft
pip install flash-attn==2.8.1
Or with uv:
uv pip install trinity-rft
uv pip install flash-attn==2.8.1
Using Docker#
We provide a Docker setup for hassle-free environment configuration.
git clone https://github.com/modelscope/Trinity-RFT
cd Trinity-RFT
# Build the Docker image
## Tip: You can modify the Dockerfile to add mirrors or set API keys
docker build -f scripts/docker/Dockerfile -t trinity-rft:latest .
# Run the container, replacing <path_to_your_data_and_checkpoints> with your actual path
docker run -it \
--gpus all \
--shm-size="64g" \
--rm \
-v $PWD:/workspace \
-v <path_to_your_data_and_checkpoints>:/data \
trinity-rft:latest
Note
For training with Megatron-LM, please refer to Megatron-LM Backend.
Troubleshooting#
If you encounter installation issues, refer to the FAQ or GitHub Issues.