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docs: update README.md #17

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12 changes: 6 additions & 6 deletions README.md
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Expand Up @@ -78,12 +78,12 @@ We pretrained DeepSeek-V2 on a diverse and high-quality corpus comprising 8.1 tr

| **Model** | **Context Length** | **Download** |
| :------------: | :------------: | :------------: |
| DeepSeek-V2 | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V2) |
| DeepSeek-V2-Chat (RL) | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V2-Chat) |
| DeepSeek-V2 | 128k | [🤗 Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-V2) |
| DeepSeek-V2-Chat (RL) | 128k | [🤗 Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-V2-Chat) |

</div>

Due to the constraints of HuggingFace, the open-source code currently experiences slower performance than our internal codebase when running on GPUs with Huggingface. To facilitate the efficient execution of our model, we offer a dedicated vllm solution that optimizes performance for running our model effectively.
Due to the constraints of Hugging Face, the open-source code currently experiences slower performance than our internal codebase when running on GPUs with Hugging Face. To facilitate the efficient execution of our model, we offer a dedicated vllm solution that optimizes performance for running our model effectively.

## 3. Evaluation Results
### Base Model
Expand Down Expand Up @@ -186,8 +186,8 @@ We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.c

## 7. How to run locally
**To utilize DeepSeek-V2 in BF16 format for inference, 80GB*8 GPUs are required.**
### Inference with Huggingface's Transformers
You can directly employ [Huggingface's Transformers](https://github.com/huggingface/transformers) for model inference.
### Inference with Hugging Face's Transformers
You can directly employ [Hugging Face's Transformers](https://github.com/huggingface/transformers) for model inference.

#### Text Completion
```python
Expand Down Expand Up @@ -235,7 +235,7 @@ result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_token
print(result)
```

The complete chat template can be found within `tokenizer_config.json` located in the huggingface model repository.
The complete chat template can be found within `tokenizer_config.json` located in the Hugging Face model repository.

An example of chat template is as belows:

Expand Down