README.md

---
title: DeepSeek-R1
emoji: 🧠
colorFrom: indigo
colorTo: blue
sdk: docker
app_file: app.py
pinned: false
license: mit
---

# DeepSeek-R1


# Open R1

*A fully open reproduction of DeepSeek-R1. This repo is work in progress, let's build it together!*

## Overview

The goal of this repo is to build the missing pieces of the R1 pipeline such that everybody can reproduce and build on top of it. The project is simple by design and mostly consists of:

- `src/open_r1` contains the scripts to train and evaluate models as well generate synthetic data:
    - `grpo.py`: trains a model with GRPO on a given dataset.
    - `sft.py`: simple SFT of a model on a dataset.
    - `evaluate.py`: evaluates a model on the R1 benchmarks.
    - `generate.py`: generate synthetic data from a model using [Distilabel](https://github.com/argilla-io/distilabel).
- `Makefile` contains an easy to run command for each step in the R1 pipeline leveraging the scripts above.

### Plan of attack

We will use the DeepSeek-R1 [tech report](https://github.com/deepseek-ai/DeepSeek-R1) as a guide, which can roughly be broken down into three main steps:

* Step 1: replicate the R1-Distill models by distilling a high-quality corpus from DeepSeek-R1.
* Step 2: replicate the pure RL pipeline that DeepSeek used to create R1-Zero. This will likely involve curating new, large-scale datasets for math, reasoning, and code.
* Step 3: show we can go from base model to RL-tuned via multi-stage training.

<center>
    <img src="assets/plan-of-attack.png" width="500">
</center>


## Installation

To run the code in this project, first, create a Python virtual environment using e.g. Conda:

```shell
conda create -n openr1 python=3.11 && conda activate openr1
```

Next, install vLLM:

```shell
pip install vllm==0.6.6.post1

# For HF (cluster only has CUDA 12.1)
pip install vllm==0.6.6.post1 --extra-index-url https://download.pytorch.org/whl/cu121
```

This will also install PyTorch `v2.5.1` and it is **very important** to use this version since the vLLM binaries are compiled for it. You can then install the remaining dependencies for your specific use case via `pip install -e .[LIST OF MODES]`. For most contributors, we recommend:

```shell
pip install -e ".[dev]"
```

Next, log into your Hugging Face and Weights and Biases accounts as follows:

```shell
huggingface-cli login
wandb login
```

Finally, check your system has Git LFS installed so that you can load and push models/datasets to the Hugging Face Hub:

```shell
git-lfs --version
```

If it isn't installed, run:

```shell
sudo apt-get install git-lfs
```

## Training models

We support training models with either DDP or DeepSpeed ZeRO-2 and ZeRO-3. To switch between methods, simply change the path to the `accelerate` YAML config in `configs`.

> [!NOTE]
> The training commands below are configured for a node of 8 x H100s (80GB). For different hardware and topologies, you may need to tune the batch size and number of gradient accumulation steps.

### SFT

To run SFT on a dataset distilled from DeepSeek-R1 with reasoning traces such as [Bespoke-Stratos-17k](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k), run:

```
accelerate launch --config_file=configs/zero3.yaml src/open_r1/sft.py \
    --model_name_or_path Qwen/Qwen2.5-Math-1.5B-Instruct \
    --dataset_name HuggingFaceH4/Bespoke-Stratos-17k \
    --learning_rate 2.0e-5 \
    --num_train_epochs 1 \
    --packing \
    --max_seq_length 4096 \
    --per_device_train_batch_size 4 \
    --per_device_eval_batch_size 4 \
    --gradient_accumulation_steps 4 \
    --gradient_checkpointing \
    --bf16 \
    --logging_steps 5 \
    --eval_strategy steps \
    --eval_steps 100 \
    --output_dir data/Qwen2.5-1.5B-Open-R1-Distill
```

To launch a Slurm job, run:

```shell
sbatch --output=/path/to/logs/%x-%j.out --err=/path/to/logs/%x-%j.err slurm/sft.slurm {model} {dataset} {accelerator}
```

Here `{model}` and `{dataset}` refer to the model and dataset IDs on the Hugging Face Hub, while `{accelerator}` refers to the choice of 🤗 Accelerate config in `configs`. 

### GRPO

```
accelerate launch --config_file configs/zero3.yaml src/open_r1/grpo.py \
    --output_dir DeepSeek-R1-Distill-Qwen-7B-GRPO \
    --model_name_or_path deepseek-ai/DeepSeek-R1-Distill-Qwen-7B \
    --dataset_name AI-MO/NuminaMath-TIR \
    --max_prompt_length 256 \
    --per_device_train_batch_size 1 \
    --gradient_accumulation_steps 16 \
    --logging_steps 10 \
    --bf16
```

## Evaluating models

We use `lighteval` to evaluate models, with custom tasks defined in `src/open_r1/evaluate.py`. For models which fit on a single GPU, run:

```shell
MODEL=deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
MODEL_ARGS="pretrained=$MODEL,dtype=float16,max_model_length=32768,gpu_memory_utilisation=0.8"
TASK=aime24
OUTPUT_DIR=data/evals/$MODEL

lighteval vllm $MODEL_ARGS "custom|$TASK|0|0" \
    --custom-tasks src/open_r1/evaluate.py \
    --use-chat-template \
    --system-prompt="Please reason step by step, and put your final answer within \boxed{}." \
    --output-dir $OUTPUT_DIR 
```

To increase throughput across multiple GPUs, use _data parallel_ as follows:

```shell
NUM_GPUS=8
MODEL=deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
MODEL_ARGS="pretrained=$MODEL,dtype=float16,data_parallel_size=$NUM_GPUS,max_model_length=32768,gpu_memory_utilisation=0.8"
TASK=aime24
OUTPUT_DIR=data/evals/$MODEL

lighteval vllm $MODEL_ARGS "custom|$TASK|0|0" \
    --custom-tasks src/open_r1/evaluate.py \
    --use-chat-template \
    --system-prompt="Please reason step by step, and put your final answer within \boxed{}." \
    --output-dir $OUTPUT_DIR 
```

For large models which require sharding across GPUs, use _tensor parallel_ and run:

```shell
NUM_GPUS=8
MODEL=deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
MODEL_ARGS="pretrained=$MODEL,dtype=float16,tensor_parallel_size=$NUM_GPUS,max_model_length=32768,gpu_memory_utilisation=0.8"
TASK=aime24
OUTPUT_DIR=data/evals/$MODEL

export VLLM_WORKER_MULTIPROC_METHOD=spawn
lighteval vllm $MODEL_ARGS "custom|$TASK|0|0" \
    --custom-tasks src/open_r1/evaluate.py \
    --use-chat-template \
    --system-prompt="Please reason step by step, and put your final answer within \boxed{}." \
    --output-dir $OUTPUT_DIR 
```

## Data generation

### Generate data from a smol distilled R1 model

The following example can be run in 1xH100. 
First install the following dependencies:

```shell
pip install "distilabel[vllm]>=1.5.2"
```

Now save the following snippet into a file named `pipeline.py` and run with `python pipeline.py`. It will generate for each of the 10 examples 4 generations (change the username for the repository to your org/user name):

```python
from datasets import load_dataset
from distilabel.models import vLLM
from distilabel.pipeline import Pipeline
from distilabel.steps.tasks import TextGeneration


prompt_template = """\
You will be given a problem. Please reason step by step, and put your final answer within \boxed{}:
{{ instruction }}"""

dataset = load_dataset("AI-MO/NuminaMath-TIR", split="train").select(range(10))

model_id = "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B"  # Exchange with another smol distilled r1

with Pipeline(
    name="distill-qwen-7b-r1",
    description="A pipeline to generate data from a distilled r1 model",
) as pipeline:

    llm = vLLM(
        model=model_id,
        tokenizer=model_id,
        extra_kwargs={
            "tensor_parallel_size": 1,
            "max_model_len": 8192,
        },
        generation_kwargs={
            "temperature": 0.6,
            "max_new_tokens": 8192,
        },
    )
    prompt_column = "problem"
    text_generation = TextGeneration(
        llm=llm, 
        template=prompt_template,
        num_generations=4,
        input_mappings={"instruction": prompt_column} if prompt_column is not None else {}
    )


if __name__ == "__main__":
    distiset = pipeline.run(dataset=dataset)
    distiset.push_to_hub(repo_id="username/numina-deepseek-r1-qwen-7b")
```

Take a look at the sample dataset at [HuggingFaceH4/numina-deepseek-r1-qwen-7b](https://huggingface.co/datasets/HuggingFaceH4/numina-deepseek-r1-qwen-7b).


### Generate data from DeepSeek-R1

To run the bigger DeepSeek-R1, we used 2 nodes of 8xH100 each one, using the slurm file present in this repo at `slurm/generate.slurm`. First, install the dependencies:

(for now we need to install the vllm dev wheel that [fixes the R1 cuda graph capture](https://github.com/vllm-project/vllm/commits/221d388cc5a836fa189305785ed7e887cea8b510/csrc/moe/moe_align_sum_kernels.cu))
```shell
pip install https://wheels.vllm.ai/221d388cc5a836fa189305785ed7e887cea8b510/vllm-1.0.0.dev-cp38-abi3-manylinux1_x86_64.whl --extra-index-url https://download.pytorch.org/whl/cu121

pip install "distilabel[vllm,ray,openai]>=1.5.2"
```

And then, place the `generate.slurm` file at the same level as `src/open_r1/generate.py` (it will try to run the file in the relative path), and run the following command:

```shell
sbatch generate.slurm \
    --hf-dataset AI-MO/NuminaMath-TIR \
    --temperature 0.6 \
    --prompt-column problem \
    --model deepseek-ai/DeepSeek-R1 \
    --hf-output-dataset username/r1-dataset
```