---
title: DeepSeek-V2-Chat-0628
canonical_url: "https://www.modelscope.cn/models/deepseek-ai/DeepSeek-V2-Chat-0628"
md_url: "https://www.modelscope.cn/models/deepseek-ai/DeepSeek-V2-Chat-0628.md"
repository: deepseek-ai/DeepSeek-V2-Chat-0628
last_updated: 2024-07-26
pipeline_tag: text-generation
tasks:
- text-generation
model_type:
- deepseek_v2
architectures:
- DeepseekV2ForCausalLM
parameters: 235.7B
tensor_type:
- BF16
library_name:
- safetensors
- pytorch
frameworks:
- pytorch
inference_backends:
- "deploy_task text/emb"
- "lmdeploy 0.9.1"
- "lmdeploy_turbomind 0.9.1"
- "sglang 0.5.2"
- "vllm 0.9.2"
downloads: 4159
stars: 3
---
# DeepSeek-V2-Chat-0628
> DeepSeek-V2-Chat-0628 - deepseek-ai 在 ModelScope 开源的模型。Model Download | Evaluation Results | Model Architecture | API Platform | License | Citation Paper Link 👁️ DeepSeek-V2-Chat-0628 1. Introduction DeepSeek-V2-Chat-0628 is an improved version of…
deepseek-ai/DeepSeek-V2-Chat-0628 是 ModelScope 魔搭社区上的 235.7B 参数text-generation模型，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。
- **Repository**: deepseek-ai/DeepSeek-V2-Chat-0628
- **Tasks**: text-generation
- **Parameters**: 235.7B
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Downloads**: 4159
- **Stars**: 3
- **Last updated**: 2024-07-26
Source: https://www.modelscope.cn/models/deepseek-ai/DeepSeek-V2-Chat-0628
---
Model Download |
Evaluation Results |
Model Architecture |
API Platform |
License |
Citation
Paper Link👁️
# 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.
Coding Arena Ranking: #3, showcasing exceptional capabilities in coding tasks.
Hard Prompts Arena Ranking: #3, demonstrating strong performance on challenging prompts.
## 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).
