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---

license: other
license_name: deepseek
license_link: https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-MODEL
---


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  <a href="#2-model-downloads">Model Download</a> |
  <a href="#3-evaluation-results">Evaluation Results</a> |
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  <a href="https://arxiv.org/abs/2405.04434"><b>Paper Link</b>👁️</a>
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# DeepSeek-V2-Chat-0628

## 1. Introduction

DeepSeek-V2-Chat-0628 is an improved version of DeepSeek-V2-Chat. For model details, please visit [DeepSeek-V2 page](https://huggingface.co/deepseek-ai/DeepSeek-V2-Chat) for more information. 

DeepSeek-V2-Chat-0628 has achieved remarkable performance on the LMSYS Chatbot Arena Leaderboard:

Overall Ranking: #11, outperforming all other open-source models.

<p align="center">
  <img width="90%" src="figures/arena1.jpeg" />
</p>

Coding Arena Ranking: #3, showcasing exceptional capabilities in coding tasks.

<p align="center">
  <img width="90%" src="figures/arena2.png" />
</p>

Hard Prompts Arena Ranking: #3, demonstrating strong performance on challenging prompts.

<p align="center">
  <img width="90%" src="figures/arena3.png" />
</p>

## 2. Improvement

Compared to the previous version DeepSeek-V2-Chat, the new version has made the following improvements:

| **Benchmark** | **DeepSeek-V2-Chat** | **DeepSeek-V2-Chat-0628** | **Improvement** |
|:-----------:|:------------:|:---------------:|:-------------------------:|
| **HumanEval** | 81.1 | 84.8 | +3.7 |
| **MATH** | 53.9 | 71.0 | +17.1 |
| **BBH** | 79.7 | 83.4 | +3.7 |
| **IFEval** | 63.8 | 77.6 | +13.8 |
| **Arena-Hard** | 41.6 | 68.3 | +26.7 |
| **JSON Output (Internal)** | 78 | 85 | +7 |

Furthermore, the instruction following capability in the "system" area has been optimized, significantly enhancing the user experience for immersive translation, RAG, and other tasks.

## 3. How to run locally

**To utilize DeepSeek-V2-Chat-0628 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.



```python

import torch

from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig



model_name = "deepseek-ai/DeepSeek-V2-Chat-0628"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

# `max_memory` should be set based on your devices

max_memory = {i: "75GB" for i in range(8)}

# `device_map` cannot be set to `auto`

model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, device_map="sequential", torch_dtype=torch.bfloat16, max_memory=max_memory, attn_implementation="eager")

model.generation_config = GenerationConfig.from_pretrained(model_name)

model.generation_config.pad_token_id = model.generation_config.eos_token_id



messages = [

    {"role": "user", "content": "Write a piece of quicksort code in C++"}

]

input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)



result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)

```



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



**Note: The chat template has been updated compared to the previous DeepSeek-V2-Chat version.**

An example of chat template is as belows:

```bash

<｜begin▁of▁sentence｜><｜User｜>{user_message_1}<｜Assistant｜>{assistant_message_1}<｜end▁of▁sentence｜><｜User｜>{user_message_2}<｜Assistant｜>

```

You can also add an optional system message:

```bash

<｜begin▁of▁sentence｜>{system_message}



<｜User｜>{user_message_1}<｜Assistant｜>{assistant_message_1}<｜end▁of▁sentence｜><｜User｜>{user_message_2}<｜Assistant｜>

```

### Inference with vLLM (recommended)
To utilize [vLLM](https://github.com/vllm-project/vllm) for model inference, please merge this Pull Request into your vLLM codebase: https://github.com/vllm-project/vllm/pull/4650.

```python

from transformers import AutoTokenizer

from vllm import LLM, SamplingParams



max_model_len, tp_size = 8192, 8

model_name = "deepseek-ai/DeepSeek-V2-Chat-0628"

tokenizer = AutoTokenizer.from_pretrained(model_name)

llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True, enforce_eager=True)

sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])



messages_list = [

    [{"role": "user", "content": "Who are you?"}],

    [{"role": "user", "content": "Translate the following content into Chinese directly: DeepSeek-V2 adopts innovative architectures to guarantee economical training and efficient inference."}],

    [{"role": "user", "content": "Write a piece of quicksort code in C++."}],

]



prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]



outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)



generated_text = [output.outputs[0].text for output in outputs]

print(generated_text)

```

## 4. License
This code repository is licensed under [the MIT License](LICENSE-CODE). The use of DeepSeek-V2 Base/Chat models is subject to [the Model License](LICENSE-MODEL). DeepSeek-V2 series (including Base and Chat) supports commercial use.

## 5. Citation
```

@misc{deepseekv2,

      title={DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model}, 

      author={DeepSeek-AI},

      year={2024},

      eprint={2405.04434},

      archivePrefix={arXiv},

      primaryClass={cs.CL}

}

```

## 6. Contact
If you have any questions, please raise an issue or contact us at [service@deepseek.com](service@deepseek.com).