---
license: mit
library_name: transformers
base_model:
- deepseek-ai/DeepSeek-V3.2-Exp-Base
base_model_relation: finetune
---
# DeepSeek-V3.2-Exp
## Introduction
We are excited to announce the official release of DeepSeek-V3.2-Exp, an experimental version of our model. As an intermediate step toward our next-generation architecture, V3.2-Exp builds upon V3.1-Terminus by introducing DeepSeek Sparse Attention—a sparse attention mechanism designed to explore and validate optimizations for training and inference efficiency in long-context scenarios.
This experimental release represents our ongoing research into more efficient transformer architectures, particularly focusing on improving computational efficiency when processing extended text sequences.
- DeepSeek Sparse Attention (DSA) achieves fine-grained sparse attention for the first time, delivering substantial improvements in long-context training and inference efficiency while maintaining virtually identical model output quality.
- To rigorously evaluate the impact of introducing sparse attention, we deliberately aligned the training configurations of DeepSeek-V3.2-Exp with V3.1-Terminus. Across public benchmarks in various domains, DeepSeek-V3.2-Exp demonstrates performance on par with V3.1-Terminus.
| Benchmark | DeepSeek-V3.1-Terminus | DeepSeek-V3.2-Exp |
| :--- | :---: | :---: |
| **Reasoning Mode w/o Tool Use** | | |
| MMLU-Pro | 85.0 | 85.0 |
| GPQA-Diamond | 80.7 | 79.9 |
| Humanity's Last Exam | 21.7 | 19.8 |
| LiveCodeBench | 74.9 | 74.1 |
| AIME 2025 | 88.4 | 89.3 |
| HMMT 2025 | 86.1 | 83.6 |
| Codeforces | 2046 | 2121 |
| Aider-Polyglot | 76.1 | 74.5 |
| **Agentic Tool Use** | | |
| BrowseComp | 38.5 | 40.1 |
| BrowseComp-zh | 45.0 | 47.9 |
| SimpleQA | 96.8 | 97.1 |
| SWE Verified | 68.4 | 67.8 |
| SWE-bench Multilingual | 57.8 | 57.9 |
| Terminal-bench | 36.7 | 37.7 |
## How to Run Locally
### HuggingFace
We provide an updated inference demo code in the [inference](https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Exp/tree/main/inference) folder to help the community quickly get started with our model and understand its architectural details.
First convert huggingface model weights to the the format required by our inference demo. Set `MP` to match your available GPU count:
```bash
cd inference
export EXPERTS=256
python convert.py --hf-ckpt-path ${HF_CKPT_PATH} --save-path ${SAVE_PATH} --n-experts ${EXPERTS} --model-parallel ${MP}
```
Launch the interactive chat interface and start exploring DeepSeek's capabilities:
```bash
export CONFIG=config_671B_v3.2.json
torchrun --nproc-per-node ${MP} generate.py --ckpt-path ${SAVE_PATH} --config ${CONFIG} --interactive
```
### SGLang
#### Installation with Docker
```
# H200
docker pull lmsysorg/sglang:dsv32
# MI350
docker pull lmsysorg/sglang:dsv32-rocm
# NPUs
docker pull lmsysorg/sglang:dsv32-a2
docker pull lmsysorg/sglang:dsv32-a3
```
#### Launch Command
```bash
python -m sglang.launch_server --model deepseek-ai/DeepSeek-V3.2-Exp --tp 8 --dp 8 --page-size 64
```
### vLLM
vLLM provides day-0 support of DeepSeek-V3.2-Exp. See the [recipes](https://docs.vllm.ai/projects/recipes/en/latest/DeepSeek/DeepSeek-V3_2-Exp.html) for up-to-date details.
## Open-Source Kernels
For TileLang kernels with **better readability and research-purpose design**, please refer to [TileLang](https://github.com/tile-ai/tilelang/tree/main/examples/deepseek_v32).
For **high-performance CUDA kernels**, indexer logit kernels (including paged versions) are available in [DeepGEMM](https://github.com/deepseek-ai/DeepGEMM/pull/200). Sparse attention kernels are released in [FlashMLA](https://github.com/deepseek-ai/FlashMLA/pull/98).
## License
This repository and the model weights are licensed under the [MIT License](LICENSE).
## Citation
```
@misc{deepseekai2024deepseekv32,
title={DeepSeek-V3.2-Exp: Boosting Long-Context Efficiency with DeepSeek Sparse Attention},
author={DeepSeek-AI},
year={2025},
}
```
## Contact
If you have any questions, please raise an issue or contact us at [service@deepseek.com](service@deepseek.com).
