Add pipeline tag and library name (#9)

- Add pipeline tag and library name (6d90a4c21ac596c6e3eda398bf9bff6defb8e5d7)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,6 +1,9 @@
 ---
 base_model:
 - deepseek-ai/DeepSeek-V3
+library_name: llama.cpp
+pipeline_tag: text-generation
+license: other
 ---
 
 **UPDATE Jan 4th 2025: Support for DeepSeek-V3 has been merged, you can now pull from the master branch of llama.cpp. The versions uploaded in this repo are already requanted to support the changes in the tensor names**
@@ -114,7 +117,8 @@ Throughout the entire training process, we did not experience any irrecoverable
 
 </div>
 
-**NOTE: The total size of DeepSeek-V3 models on HuggingFace is 685B, which includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights.**
+> [!NOTE]
+> The total size of DeepSeek-V3 models on Hugging Face is 685B, which includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights.
 
 To ensure optimal performance and flexibility, we have partnered with open-source communities and hardware vendors to provide multiple ways to run the model locally. For step-by-step guidance, check out Section 6: [How_to Run_Locally](#6-how-to-run-locally).
 
@@ -167,12 +171,13 @@ For developers looking to dive deeper, we recommend exploring [README_WEIGHTS.md
 
 </div>
 
-Note: Best results are shown in bold. Scores with a gap not exceeding 0.3 are considered to be at the same level. DeepSeek-V3 achieves the best performance on most benchmarks, especially on math and code tasks.
-For more evaluation details, please check our paper. 
+> [!NOTE]
+> Best results are shown in bold. Scores with a gap not exceeding 0.3 are considered to be at the same level. DeepSeek-V3 achieves the best performance on most benchmarks, especially on math and code tasks.
+> For more evaluation details, please check our paper. 
 
 #### Context Window
 <p align="center">
-  <img width="80%" src="https://huggingface.co/deepseek-ai/DeepSeek-V3/resolve/main/figures/niah.png>
+  <img width="80%" src="https://huggingface.co/deepseek-ai/DeepSeek-V3/resolve/main/figures/niah.png">
 </p>
 
 Evaluation results on the ``Needle In A Haystack`` (NIAH) tests.  DeepSeek-V3 performs well across all context window lengths up to **128K**. 
@@ -209,17 +214,16 @@ Evaluation results on the ``Needle In A Haystack`` (NIAH) tests.  DeepSeek-V3 pe
 | | C-Eval (EM) | 78.6 | 79.5 | 86.1 | 61.5 | 76.7 | 76.0 | **86.5** |
 | | C-SimpleQA (Correct) | 48.5 | 54.1 | 48.4 | 50.4 | 51.3 | 59.3 | **64.8** |
 
-Note: All models are evaluated in a configuration that limits the output length to 8K. Benchmarks containing fewer than 1000 samples are tested multiple times using varying temperature settings to derive robust final results. DeepSeek-V3 stands as the best-performing open-source model, and also exhibits competitive performance against frontier closed-source models.
-
 </div>
 
+> [!NOTE]
+> All models are evaluated in a configuration that limits the output length to 8K. Benchmarks containing fewer than 1000 samples are tested multiple times using varying temperature settings to derive robust final results. DeepSeek-V3 stands as the best-performing open-source model, and also exhibits competitive performance against frontier closed-source models.
+
 
 ####  Open Ended Generation Evaluation
 
 <div align="center">
 
-
-
 | Model | Arena-Hard | AlpacaEval 2.0 |
 |-------|------------|----------------|
 | DeepSeek-V2.5-0905 | 76.2 | 50.5 |
@@ -229,9 +233,11 @@ Note: All models are evaluated in a configuration that limits the output length
 | Claude-Sonnet-3.5-1022 | 85.2 | 52.0 |
 | DeepSeek-V3 | **85.5** | **70.0** |
 
-Note: English open-ended conversation evaluations. For AlpacaEval 2.0, we use the length-controlled win rate as the metric.
 </div>
 
+> [!NOTE]
+> English open-ended conversation evaluations. For AlpacaEval 2.0, we use the length-controlled win rate as the metric.
+
 
 ## 5. Chat Website & API Platform
 You can chat with DeepSeek-V3 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com/sign_in)
@@ -246,7 +252,7 @@ DeepSeek-V3 can be deployed locally using the following hardware and open-source
 2. **SGLang**: Fully support the DeepSeek-V3 model in both BF16 and FP8 inference modes.
 3. **LMDeploy**: Enables efficient FP8 and BF16 inference for local and cloud deployment.
 4. **TensorRT-LLM**: Currently supports BF16 inference and INT4/8 quantization, with FP8 support coming soon.
-5. **vLLM**: Support DeekSeek-V3 model with FP8 and BF16 modes for tensor parallelism and pipeline parallelism.
+5. **vLLM**: Support DeepSeek-V3 model with FP8 and BF16 modes for tensor parallelism and pipeline parallelism.
 6. **AMD GPU**: Enables running the DeepSeek-V3 model on AMD GPUs via SGLang in both BF16 and FP8 modes.
 7. **Huawei Ascend NPU**: Supports running DeepSeek-V3 on Huawei Ascend devices.
 
@@ -259,10 +265,23 @@ cd inference
 python fp8_cast_bf16.py --input-fp8-hf-path /path/to/fp8_weights --output-bf16-hf-path /path/to/bf16_weights
 ```
 
-**NOTE: Huggingface's Transformers has not been directly supported yet.**
+> [!NOTE]
+> Hugging Face's Transformers has not been directly supported yet.
 
 ### 6.1 Inference with DeepSeek-Infer Demo (example only)
 
+#### System Requirements
+
+> [!NOTE] 
+> Linux with Python 3.10 only. Mac and Windows are not supported.
+
+Dependencies:
+```pip-requirements
+torch==2.4.1
+triton==3.0.0
+transformers==4.46.3
+safetensors==0.4.5
+```
 #### Model Weights & Demo Code Preparation
 
 First, clone our DeepSeek-V3 GitHub repository:
@@ -271,18 +290,18 @@ First, clone our DeepSeek-V3 GitHub repository:
 git clone https://github.com/deepseek-ai/DeepSeek-V3.git
 ```
 
-Navigate to the `inference` folder and install dependencies listed in `requirements.txt`.
+Navigate to the `inference` folder and install dependencies listed in `requirements.txt`. Easiest way is to use a package manager like `conda` or `uv` to create a new virtual environment and install the dependencies.
 
 ```shell
 cd DeepSeek-V3/inference
 pip install -r requirements.txt
 ```
 
-Download the model weights from HuggingFace, and put them into `/path/to/DeepSeek-V3` folder.
+Download the model weights from Hugging Face, and put them into `/path/to/DeepSeek-V3` folder.
 
 #### Model Weights Conversion
 
-Convert HuggingFace model weights to a specific format:
+Convert Hugging Face model weights to a specific format:
 
 ```shell
 python convert.py --hf-ckpt-path /path/to/DeepSeek-V3 --save-path /path/to/DeepSeek-V3-Demo --n-experts 256 --model-parallel 16
@@ -293,13 +312,13 @@ python convert.py --hf-ckpt-path /path/to/DeepSeek-V3 --save-path /path/to/DeepS
 Then you can chat with DeepSeek-V3:
 
 ```shell
-torchrun --nnodes 2 --nproc-per-node 8 generate.py --node-rank $RANK --master-addr $ADDR --ckpt-path /path/to/DeepSeek-V3-Demo --config configs/config_671B.json --interactive --temperature 0.7 --max-new-tokens 200
+torchrun --nnodes 2 --nproc-per-node 8 --node-rank $RANK --master-addr $ADDR generate.py --ckpt-path /path/to/DeepSeek-V3-Demo --config configs/config_671B.json --interactive --temperature 0.7 --max-new-tokens 200
 ```
 
 Or batch inference on a given file:
 
 ```shell
-torchrun --nnodes 2 --nproc-per-node 8 generate.py --node-rank $RANK --master-addr $ADDR --ckpt-path /path/to/DeepSeek-V3-Demo --config configs/config_671B.json --input-file $FILE
+torchrun --nnodes 2 --nproc-per-node 8 --node-rank $RANK --master-addr $ADDR generate.py --ckpt-path /path/to/DeepSeek-V3-Demo --config configs/config_671B.json --input-file $FILE
 ```
 
 ### 6.2 Inference with SGLang (recommended)
@@ -308,6 +327,10 @@ torchrun --nnodes 2 --nproc-per-node 8 generate.py --node-rank $RANK --master-ad
 
 Notably, [SGLang v0.4.1](https://github.com/sgl-project/sglang/releases/tag/v0.4.1) fully supports running DeepSeek-V3 on both **NVIDIA and AMD GPUs**, making it a highly versatile and robust solution.
 
+SGLang also supports [multi-node tensor parallelism](https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3#example-serving-with-2-h208), enabling you to run this model on multiple network-connected machines.
+
+Multi-Token Prediction (MTP) is in development, and progress can be tracked in the [optimization plan](https://github.com/sgl-project/sglang/issues/2591).
+
 Here are the launch instructions from the SGLang team: https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3
 
 ### 6.3 Inference with LMDeploy (recommended)
@@ -320,6 +343,7 @@ For comprehensive step-by-step instructions on running DeepSeek-V3 with LMDeploy
 
 [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) now supports the DeepSeek-V3 model, offering precision options such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in progress and will be released soon. You can access the custom branch of TRTLLM specifically for DeepSeek-V3 support through the following link to experience the new features directly: https://github.com/NVIDIA/TensorRT-LLM/tree/deepseek/examples/deepseek_v3. 
 
+
 ### 6.5 Inference with vLLM (recommended)
 
 [vLLM](https://github.com/vllm-project/vllm) v0.6.6 supports DeepSeek-V3 inference for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from standard techniques, vLLM offers _pipeline parallelism_ allowing you to run this model on multiple machines connected by networks. For detailed guidance, please refer to the [vLLM instructions](https://docs.vllm.ai/en/latest/serving/distributed_serving.html). Please feel free to follow [the enhancement plan](https://github.com/vllm-project/vllm/issues/11539) as well.
@@ -339,15 +363,8 @@ This code repository is licensed under [the MIT License](LICENSE-CODE). The use
 ```
 @misc{deepseekai2024deepseekv3technicalreport,
       title={DeepSeek-V3 Technical Report}, 
-      author={DeepSeek-AI and Aixin Liu and Bei Feng and Bing Xue and Bingxuan Wang and Bochao Wu and Chengda Lu and Chenggang Zhao and Chengqi Deng and Chenyu Zhang and Chong Ruan and Damai Dai and Daya Guo and Dejian Yang and Deli Chen and Dongjie Ji and Erhang Li and Fangyun Lin and Fucong Dai and Fuli Luo and Guangbo Hao and Guanting Chen and Guowei Li and H. Zhang and Han Bao and Hanwei Xu and Haocheng Wang and Haowei Zhang and Honghui Ding and Huajian Xin and Huazuo Gao and Hui Li and Hui Qu and J. L. Cai and Jian Liang and Jianzhong Guo and Jiaqi Ni and Jiashi Li and Jiawei Wang and Jin Chen and Jingchang Chen and Jingyang Yuan and Junjie Qiu and Junlong Li and Junxiao Song and Kai Dong and Kai Hu and Kaige Gao and Kang Guan and Kexin Huang and Kuai Yu and Lean Wang and Lecong Zhang and Lei Xu and Leyi Xia and Liang Zhao and Litong Wang and Liyue Zhang and Meng Li and Miaojun Wang and Mingchuan Zhang and Minghua Zhang and Minghui Tang and Mingming Li and Ning Tian and Panpan Huang and Peiyi Wang and Peng Zhang and Qiancheng Wang and Qihao Zhu and Qinyu Chen and Qiushi Du and R. J. Chen and R. L. Jin and Ruiqi Ge and Ruisong Zhang and Ruizhe Pan and Runji Wang and Runxin Xu and Ruoyu Zhang and Ruyi Chen and S. S. Li and Shanghao Lu and Shangyan Zhou and Shanhuang Chen and Shaoqing Wu and Shengfeng Ye and Shengfeng Ye and Shirong Ma and Shiyu Wang and Shuang Zhou and Shuiping Yu and Shunfeng Zhou and Shuting Pan and T. Wang and Tao Yun and Tian Pei and Tianyu Sun and W. L. Xiao and Wangding Zeng and Wanjia Zhao and Wei An and Wen Liu and Wenfeng Liang and Wenjun Gao and Wenqin Yu and Wentao Zhang and X. Q. Li and Xiangyue Jin and Xianzu Wang and Xiao Bi and Xiaodong Liu and Xiaohan Wang and Xiaojin Shen and Xiaokang Chen and Xiaokang Zhang and Xiaosha Chen and Xiaotao Nie and Xiaowen Sun and Xiaoxiang Wang and Xin Cheng and Xin Liu and Xin Xie and Xingchao Liu and Xingkai Yu and Xinnan Song and Xinxia Shan and Xinyi Zhou and Xinyu Yang and Xinyuan Li and Xuecheng Su and Xuheng Lin and Y. K. Li and Y. Q. Wang and Y. X. Wei and Y. X. Zhu and Yang Zhang and Yanhong Xu and Yanhong Xu and Yanping Huang and Yao Li and Yao Zhao and Yaofeng Sun and Yaohui Li and Yaohui Wang and Yi Yu and Yi Zheng and Yichao Zhang and Yifan Shi and Yiliang Xiong and Ying He and Ying Tang and Yishi Piao and Yisong Wang and Yixuan Tan and Yiyang Ma and Yiyuan Liu and Yongqiang Guo and Yu Wu and Yuan Ou and Yuchen Zhu and Yuduan Wang and Yue Gong and Yuheng Zou and Yujia He and Yukun Zha and Yunfan Xiong and Yunxian Ma and Yuting Yan and Yuxiang Luo and Yuxiang You and Yuxuan Liu and Yuyang Zhou and Z. F. Wu and Z. Z. Ren and Zehui Ren and Zhangli Sha and Zhe Fu and Zhean Xu and Zhen Huang and Zhen Zhang and Zhenda Xie and Zhengyan Zhang and Zhewen Hao and Zhibin Gou and Zhicheng Ma and Zhigang Yan and Zhihong Shao and Zhipeng Xu and Zhiyu Wu and Zhongyu Zhang and Zhuoshu Li and Zihui Gu and Zijia Zhu and Zijun Liu and Zilin Li and Ziwei Xie and Ziyang Song and Ziyi Gao and Zizheng Pan},
-      year={2024},
-      eprint={2412.19437},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2412.19437}, 
-}
+      author={DeepSeek-AI and Aixin Liu and Bei Feng and Bing Xue and Bingxuan Wang and Bochao Wu and Chengda Lu and Chenggang Zhao and Chengqi Deng and Chenyu Zhang and Chong Ruan and Damai Dai and Daya Guo and Dejian Yang and Deli Chen and Dongjie Ji and Erhang Li and Fangyun Lin and Fucong Dai and Fuli Luo and Guangbo Hao and Guanting Chen and Guowei Li and H. Zhang and Han Bao and Hanwei Xu and Haocheng Wang and Haowei Zhang and Honghui Ding and Huajian Xin and Huazuo Gao and Hui Li and Hui Qu and J. L. Cai and Jian Liang and Jianzhong Guo and Jiaqi Ni and Jiashi Li and Jiawei Wang and Jin Chen and Jingchang Chen and Jingyang Yuan and Junjie Qiu and Junlong Li and Junxiao Song and Kai Dong and Kai Hu and Kaige Gao and Kang Guan and Kexin Huang and Kuai Yu and Lean Wang and Lecong Zhang and Lei Xu and Leyi Xia and Liang Zhao and Litong Wang and Liyue Zhang and Meng Li and Miaojun Wang and Mingchuan Zhang and Minghua Zhang and Minghui Tang and Mingming Li and Ning Tian and Panpan Huang and Peiyi Wang and Peng Zhang and Qiancheng Wang and Qihao Zhu and Qinyu Chen and Qiushi Du and R. J. Chen and R. L. Jin and Ruiqi Ge and Ruisong Zhang and Ruizhe Pan and Runji Wang and Runxin Xu and Ruoyu Zhang and Ruyi Chen and S. S. Li and Shanghao Lu and Shangyan Zhou and Shanhuang Chen
 ```
 
 ## 9. Contact
-If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]).
-
+If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]).