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GitHub – Jiayi-Pan/TinyZero

adminJanuary 24, 2025

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TinyZero is a reproduction of DeepSeek R1 Zero. We built upon veRL.

Through RL, the 3B base LM develops self-verification and search abilities all on its own

You can experience the Ahah moment yourself for < $30

Twitter thread: https://x.com/jiayi_pirate/status/1882839370505621655

Full experiment log: https://wandb.ai/jiayipan/TinyZero

conda create -n zero python=3.9
# install torch [or you can skip this step and let vllm to install the correct version for you]
pip install torch==2.4.0 --index-url https://download.pytorch.org/whl/cu121
# install vllm
pip3 install vllm==0.6.3 # or you can install 0.5.4, 0.4.2 and 0.3.1
pip3 install ray

# verl
pip install -e .

# flash attention 2
pip3 install flash-attn --no-build-isolation
# quality of life
pip install wandb IPython matplotlib

Data Preparation

python ./examples/data_preprocess/countdown.py --local_dir {path_to_your_dataset}

Single GPU
Works for model <= 1.5B. For Qwen2.5-0.5B base, we know it fails to learn reasoning.

export N_GPUS=1
export BASE_MODEL={path_to_your_model}
export DATA_DIR={path_to_your_dataset}
export ROLLOUT_TP_SIZE=1
export EXPERIMENT_NAME=countdown-qwen2.5-0.5b
export VLLM_ATTENTION_BACKEND=XFORMERS

bash ./scripts/train_tiny_zero.sh

3B+ model
In this case, the base model is able to develop sophisticated reasoning skills.

export N_GPUS=2
export BASE_MODEL={path_to_your_model}
export DATA_DIR={path_to_your_dataset}
export ROLLOUT_TP_SIZE=2
export EXPERIMENT_NAME=countdown-qwen2.5-3b
export VLLM_ATTENTION_BACKEND=XFORMERS

bash ./scripts/train_tiny_zero.sh

We experiment with QWen-2.5-3B Instruct too.
Data Preparation
To follow chat template, we need to reprocess the data:

conda activate zero
python examples/data_preprocess/countdown.py --template_type=qwen-instruct --local_dir={path_to_your_dataset}

Training

export N_GPUS=2
export BASE_MODEL={path_to_your_model}
export DATA_DIR={path_to_your_dataset}
export ROLLOUT_TP_SIZE=2
export EXPERIMENT_NAME=countdown-qwen2.5-3b-instruct
export VLLM_ATTENTION_BACKEND=XFORMERS

bash ./scripts/train_tiny_zero.sh

We run our experiments based on veRL.
We use Qwen2.5 series base model Qwen2.5.

@misc{tinyzero,
author       = {Jiayi Pan and Junjie Zhang and Xingyao Wang and Lifan Yuan},
title        = {TinyZero},
howpublished = {[https://github.com/Jiayi-Pan/TinyZero](https://github.com/Jiayi-Pan/TinyZero)},
note         = {Accessed: 2025-01-24},
year         = {2025}
}