docs: Update README with HuggingFace and SGLang instructions

README.md

@@ -1,126 +1,136 @@
----
-license: mit
-library_name: transformers
-base_model:
-  - deepseek-ai/DeepSeek-V3.2-Exp-Base
----
-# DeepSeek-V3.2-Exp
-
-<!-- markdownlint-disable first-line-h1 -->
-<!-- markdownlint-disable html -->
-<!-- markdownlint-disable no-duplicate-header -->
-
-<div align="center">
-  <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" />
-</div>
-<hr>
-<div align="center" style="line-height: 1;">
-  <a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;">
-    <img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-  <a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;">
-    <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20V3-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-  <a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;">
-    <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-</div>
-<div align="center" style="line-height: 1;">
-  <a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;">
-    <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-  <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;">
-    <img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-  <a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;">
-    <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-</div>
-<div align="center" style="line-height: 1;">
-  <a href="LICENSE" style="margin: 2px;">
-    <img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-</div>
-
-## 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.
-
-<div align="center">
- <img src="assets/cost.png" >
-</div>
-
-- 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
-
-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
-```
-
-
-
-## 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 [[email protected]]([email protected]).
+# DeepSeek-V3.2-Exp
+
+<!-- markdownlint-disable first-line-h1 -->
+<!-- markdownlint-disable html -->
+<!-- markdownlint-disable no-duplicate-header -->
+
+<div align="center">
+  <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" />
+</div>
+<hr>
+<div align="center" style="line-height: 1;">
+  <a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;">
+    <img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;">
+    <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20V3-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;">
+    <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+<div align="center" style="line-height: 1;">
+  <a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;">
+    <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;">
+    <img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;">
+    <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+<div align="center" style="line-height: 1;">
+  <a href="LICENSE" style="margin: 2px;">
+    <img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+
+## 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.
+
+<div align="center">
+ <img src="cost.jpg" >
+</div>
+
+- 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-V3.2 | DeepSeek-V3.1-Terminus |
+| :--- | :---: | :---: |
+| **Reasoning Mode w/o Tool Use** | | |
+| MMLU-Pro | 85.0 | 85.0 |
+| GPQA-Diamond | 79.9 | 80.7 |
+| Humanity's Last Exam | 19.8 | 21.7 |
+| LiveCodeBench | 74.1 | 74.9 |
+| AIME 2025 | 89.3 | 88.4 |
+| HMMT 2025 | 83.6 | 86.1 |
+| Codeforces | 2121 | 2046 |
+| Aider-Polyglot | 74.5 | 76.1 |
+| **Agentic Tool Use** | | |
+| BrowseComp | 40.1 | 38.5 |
+| BrowseComp-zh | 47.9 | 45.0 |
+| SimpleQA | 97.1 | 96.8 |
+| SWE Verified | 67.8 | 68.4 |
+| SWE-bench Multilingual | 57.9 | 57.8 |
+| Terminal-bench | 37.7 | 36.7 |
+
+
+
+
+
+## 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
+```
+
+
+
+## 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 [[email protected]]([email protected]).