Add pipeline tag

README.md

@@ -1,6 +1,9 @@
 ---
 library_name: transformers
+pipeline_tag: text-generation
 ---
+
+```markdown
 <!-- markdownlint-disable first-line-h1 -->
 <!-- markdownlint-disable html -->
 <!-- markdownlint-disable no-duplicate-header -->
@@ -96,12 +99,13 @@ Throughout the entire training process, we did not experience any irrecoverable
 
 | **Model** | **#Total Params** | **#Activated Params** | **Context Length** | **Download** |
 | :------------: | :------------: | :------------: | :------------: | :------------: |
-| DeepSeek-V3-Base | 671B | 37B | 128K   | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V3-Base)   |
-| DeepSeek-V3   | 671B | 37B |  128K   | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V3)   |
+| DeepSeek-V3-Base | 671B | 37B | 128K   | [🤗 Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-V3-Base)   |
+| DeepSeek-V3   | 671B | 37B |  128K   | [🤗 Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-V3)   |
 
 </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).
 
@@ -132,7 +136,7 @@ For developers looking to dive deeper, we recommend exploring [README_WEIGHTS.md
 | | WinoGrande (Acc.) | 5-shot | **86.3** | 82.3 | 85.2 | 84.9 |
 | | RACE-Middle (Acc.) | 5-shot | 73.1 | 68.1 | **74.2** | 67.1 |
 | | RACE-High (Acc.) | 5-shot | 52.6 | 50.3 | **56.8** | 51.3 |
-| | TriviaQA (EM) | 5-shot | 80.0 | 71.9 | **82.7** | **82.9** |
+| | TriviaQA (EM) | 5-shot | 80.0 | 71.9 | 82.7 | **82.9** |
 | | NaturalQuestions (EM) | 5-shot | 38.6 | 33.2 | **41.5** | 40.0 |
 | | AGIEval (Acc.) | 0-shot | 57.5 | 75.8 | 60.6 | **79.6** |
 | Code | HumanEval (Pass@1) | 0-shot | 43.3 | 53.0 | 54.9 | **65.2** |
@@ -154,8 +158,9 @@ 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">
@@ -198,15 +203,10 @@ Evaluation results on the ``Needle In A Haystack`` (NIAH) tests.  DeepSeek-V3 pe
 
 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>
-
-
 ####  Open Ended Generation Evaluation
 
 <div align="center">
 
-
-
 | Model | Arena-Hard | AlpacaEval 2.0 |
 |-------|------------|----------------|
 | DeepSeek-V2.5-0905 | 76.2 | 50.5 |
@@ -219,7 +219,6 @@ Note: All models are evaluated in a configuration that limits the output length
 Note: English open-ended conversation evaluations. For AlpacaEval 2.0, we use the length-controlled win rate as the metric.
 </div>
 
-
 ## 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)
 
@@ -233,7 +232,7 @@ 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.
 
@@ -246,7 +245,8 @@ 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)
 
@@ -269,7 +269,7 @@ Download the model weights from HuggingFace, and put them into `/path/to/DeepSee
 
 #### 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
@@ -302,7 +302,6 @@ Here are the launch instructions from the SGLang team: https://github.com/sgl-pr
 
 For comprehensive step-by-step instructions on running DeepSeek-V3 with LMDeploy, please refer to here: https://github.com/InternLM/lmdeploy/issues/2960
 
-
 ### 6.4 Inference with TRT-LLM (recommended)
 
 [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. 
@@ -318,7 +317,6 @@ In collaboration with the AMD team, we have achieved Day-One support for AMD GPU
 ### 6.7 Recommended Inference Functionality with Huawei Ascend NPUs
 The [MindIE](https://www.hiascend.com/en/software/mindie) framework from the Huawei Ascend community has successfully adapted the BF16 version of DeepSeek-V3. For step-by-step guidance on Ascend NPUs, please follow the [instructions here](https://modelers.cn/models/MindIE/deepseekv3).
 
-
 ## 7. License
 This code repository is licensed under [the MIT License](LICENSE-CODE). The use of DeepSeek-V3 Base/Chat models is subject to [the Model License](LICENSE-MODEL). DeepSeek-V3 series (including Base and Chat) supports commercial use.
 
@@ -336,4 +334,5 @@ This code repository is licensed under [the MIT License](LICENSE-CODE). The use
 ```
 
 ## 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]).
+```