# DeepSeek-V2-Chat
---


## README([From Huggingface](https://huggingface.co/deepseek-ai/DeepSeek-V2-Chat))

<!-- markdownlint-disable first-line-h1 -->
<!-- markdownlint-disable html -->
<!-- markdownlint-disable no-duplicate-header -->

<div align="center">
  <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek LLM" />
</div>
<hr>
<div align="center">

  <a href="https://www.deepseek.com/" target="_blank">
    <img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/>
  </a>
  <a href="https://chat.deepseek.com/" target="_blank">
    <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20LLM-536af5?color=536af5&logoColor=white?raw=true" style="display: inline-block; vertical-align: middle;"/>
  </a>
  <a href="https://huggingface.co/deepseek-ai" target="_blank">
    <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white?raw=true" style="display: inline-block; vertical-align: middle;"/>
  </a>

  <a href="https://discord.gg/Tc7c45Zzu5" target="_blank">
    <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da?raw=true" style="display: inline-block; vertical-align: middle;"/>
  </a>
  <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg" target="_blank">
    <img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white?raw=true"style="display: inline-block; vertical-align: middle;" />
  </a>
  <a href="https://twitter.com/deepseek_ai" target="_blank">
    <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white?raw=true" style="display: inline-block; vertical-align: middle;"/>
  </a>
  <a href="LICENSE-CODE">
    <img alt="Code License" src="https://img.shields.io/badge/Code_License-MIT-f5de53?&color=f5de53?raw=true"style="display: inline-block; vertical-align: middle;">
  </a>
  <a href="LICENSE-MODEL">
    <img alt="Model License" src="https://img.shields.io/badge/Model_License-Model_Agreement-f5de53?&color=f5de53?raw=true"style="display: inline-block; vertical-align: middle;">
  </a>
</div>


<p align="center">
  <a href="#2-model-downloads">Model Download</a> |
  <a href="#3-evaluation-results">Evaluation Results</a> |
  <a href="#4-model-architecture">Model Architecture</a> |
  <a href="#6-api-platform">API Platform</a> |
  <a href="#8-license">License</a> |
  <a href="#9-citation">Citation</a>
</p>

<p align="center">
  <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/deepseek-v2-tech-report.pdf"><b>Paper Link</b>👁️</a>
</p>

# DeepSeek-V2:  A Strong, Economical, and Efficient Mixture-of-Experts Language Model

## 1. Introduction
Today, we’re introducing DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token. Compared with DeepSeek 67B, DeepSeek-V2 achieves stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum generation throughput to 5.76 times. 

<p align="center">

<div style="display: flex; justify-content: center;">
    <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/activationparameters.png?raw=true" style="height:300px; width:auto; margin-right:10px">
    <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/trainingcost.png?raw=true" style="height:300px; width:auto; margin-left:10px">
</div>
</p>
We pretrained DeepSeek-V2 on a diverse and high-quality corpus comprising 8.1 trillion tokens. This comprehensive pretraining was followed by a process of Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) to fully unleash the model's capabilities. The evaluation results validate the effectiveness of our approach as DeepSeek-V2 achieves remarkable performance on both standard benchmarks and open-ended generation evaluation.

## 2. Model Downloads

<div align="center">

| **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)   |

</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.

## 3. Evaluation Results
### Base Model
#### Standard Benchmark 

<div align="center">

| **Benchmark** | **Domain** | **LLaMA3 70B** | **Mixtral 8x22B** | **DeepSeek V1 (Dense-67B)** | **DeepSeek V2 (MoE-236B)** |
|:-----------:|:--------:|:------------:|:---------------:|:-------------------------:|:------------------------:|
| **MMLU** | English | 78.9 | 77.6 | 71.3 | 78.5 |
| **BBH** | English | 81.0 | 78.9 | 68.7 | 78.9 |
| **C-Eval** | Chinese | 67.5 | 58.6 | 66.1 | 81.7 |
| **CMMLU** | Chinese | 69.3 | 60.0 | 70.8 | 84.0 |
| **HumanEval** | Code | 52.4 | 39.0 | 42.7 | 40.9 |
| **MBPP** | Code | 68.6 | 64.2 | 57.4 | 66.6 |
| **GSM8K** | Math | 83.0 | 80.3 | 63.4 | 79.2 |
| **Math** | Math | 42.2 | 42.5 | 18.7 | 43.6 |

</div>
For more evaluation details, such as few-shot settings and prompts, please check our paper. 

#### Context Window
<p align="center">
  <img width="80%" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/niah.png?raw=true">
</p>

Evaluation results on the ``Needle In A Haystack`` (NIAH) tests.  DeepSeek-V2 performs well across all context window lengths up to **128K**. 

### Chat Model
#### Standard Benchmark 
<div align="center">

| Benchmark | Domain         | QWen1.5 72B Chat | Mixtral 8x22B | LLaMA3 70B Instruct | DeepSeek V1 Chat (SFT) | DeepSeek V2 Chat(SFT) | DeepSeek V2 Chat(RL) |
|:-----------:|:----------------:|:------------------:|:---------------:|:---------------------:|:-------------:|:-----------------------:|:----------------------:|
| **MMLU**      | English        | 76.2             | 77.8          | 80.3                | 71.1        | 78.4                 | 77.8                 |
| **BBH**       | English        | 65.9             | 78.4          | 80.1                | 71.7        | 81.3                 | 79.7                 |
| **C-Eval**    | Chinese        | 82.2             | 60.0          | 67.9                | 65.2        | 80.9                 | 78.0                 |
| **CMMLU**     | Chinese        | 82.9             | 61.0          | 70.7                | 67.8        | 82.4                 | 81.6                 |
| **HumanEval** | Code           | 68.9             | 75.0          | 76.2                | 73.8        | 76.8                 | 81.1                 |
| **MBPP**      | Code           | 52.2             | 64.4          | 69.8                | 61.4        | 70.4                 | 72.0                 |
|   **LiveCodeBench  (0901-0401)**     | Code           | 18.8             | 25.0          | 30.5                | 18.3        | 28.7                 | 32.5                 |
| **GSM8K**     | Math           | 81.9             | 87.9          | 93.2                | 84.1        | 90.8                 | 92.2                 |
| **Math**      | Math           | 40.6             | 49.8          | 48.5                | 32.6        | 52.7                 | 53.9                 |

</div>

#### English Open Ended Generation Evaluation
We evaluate our model on AlpacaEval 2.0 and MTBench, showing the competitive performance of DeepSeek-V2-Chat-RL on English conversation generation. 
<p align="center">
  <img width="50%" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/mtbench.png?raw=true" />
</p>

#### Chinese Open Ended Generation Evaluation
**Alignbench** (https://arxiv.org/abs/2311.18743)
<div align="center">

| **模型** | **开源/闭源** | **总分** | **中文推理** | **中文语言** |
| :---: | :---: | :---: | :---: | :---: |
| gpt-4-1106-preview | 闭源 | 8.01 | 7.73 | 8.29 |
| DeepSeek-V2 Chat(RL) | 开源 | 7.91 | 7.45 | 8.35 |
| erniebot-4.0-202404(文心一言) | 闭源 | 7.89 | 7.61 | 8.17 |
| DeepSeek-V2 Chat(SFT) | 开源 | 7.74 | 7.30 | 8.17 |
| gpt-4-0613 | 闭源 | 7.53 | 7.47 | 7.59 |
| erniebot-4.0-202312(文心一言) | 闭源 | 7.36 | 6.84 | 7.88 |
| moonshot-v1-32k-202404(月之暗面) | 闭源 | 7.22 | 6.42 | 8.02 |
| Qwen1.5-72B-Chat(通义千问) | 开源 | 7.19 | 6.45 | 7.93 |
| DeepSeek-67B-Chat | 开源 | 6.43 | 5.75 | 7.11 |
| Yi-34B-Chat(零一万物) | 开源 | 6.12 | 4.86 | 7.38 |
| gpt-3.5-turbo-0613 | 闭源 | 6.08 | 5.35 | 6.71 |

</div>

#### Coding Benchmarks
We evaluate our model on LiveCodeBench (0901-0401), a benchmark designed for live coding challenges. As illustrated, DeepSeek-V2 demonstrates considerable proficiency in LiveCodeBench, achieving a Pass@1 score that surpasses several other sophisticated models. This performance highlights the model's effectiveness in tackling live coding tasks.

<p align="center">
  <img width="50%" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/code_benchmarks.png?raw=true">
</p>

## 4. Model Architecture
DeepSeek-V2 adopts innovative architectures to guarantee economical training and efficient inference： 
- For attention, we design MLA (Multi-head Latent Attention), which utilizes low-rank key-value union compression to eliminate the bottleneck of inference-time key-value cache, thus supporting efficient inference. 
- For Feed-Forward Networks (FFNs), we adopt DeepSeekMoE architecture, a high-performance MoE architecture that enables training stronger models at lower costs. 

<p align="center">
  <img width="90%" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/architecture.png?raw=true" />
</p>

## 5. Chat Website
You can chat with the DeepSeek-V2 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com/sign_in)

## 6. API Platform
We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/). Sign up for over millions of free tokens. And you can also pay-as-you-go at an unbeatable price.


<p align="center">
  <img width="40%" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/model_price.png?raw=true">
</p>


## 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.

### Text Completion
```python
import paddle
from paddlenlp.transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig

model_name = "deepseek-ai/DeepSeek-V2"
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)}
model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True,  dtype=paddle.bfloat16, max_memory=max_memory)
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id

text = "An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is"
inputs = tokenizer(text, return_tensors="pd")
outputs = model.generate(**inputs, max_new_tokens=100)[0]

result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```

### Chat Completion
```python
import paddle
from paddlenlp.transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig

model_name = "deepseek-ai/DeepSeek-V2-Chat"
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)}
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True,  dtype=paddle.bfloat16, max_memory=max_memory)
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="pd")
outputs = model.generate(input_tensor[0], 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.

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:
```

## 8. 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.

## 9. Citation
```
@misc{deepseek-v2,
  author = {DeepSeek-AI},
  title  = {DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model},
  year   = {2024},
  note   = {GitHub repository},
  url    = {https://github.com/deepseek-ai/deepseek-v2}
  }
```

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




## Model Files

- [LICENSE](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/LICENSE) (13.4 KB)

- [README.md](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/README.md) (13.8 KB)

- [config.json](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/config.json) (1.5 KB)

- [generation_config.json](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/generation_config.json) (145.0 B)

- [model-00001-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00001-of-000055.safetensors) (8.0 GB)

- [model-00002-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00002-of-000055.safetensors) (8.0 GB)

- [model-00003-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00003-of-000055.safetensors) (8.0 GB)

- [model-00004-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00004-of-000055.safetensors) (8.0 GB)

- [model-00005-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00005-of-000055.safetensors) (8.0 GB)

- [model-00006-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00006-of-000055.safetensors) (8.0 GB)

- [model-00007-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00007-of-000055.safetensors) (8.0 GB)

- [model-00008-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00008-of-000055.safetensors) (8.0 GB)

- [model-00009-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00009-of-000055.safetensors) (8.0 GB)

- [model-00010-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00010-of-000055.safetensors) (8.0 GB)

- [model-00011-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00011-of-000055.safetensors) (8.0 GB)

- [model-00012-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00012-of-000055.safetensors) (8.0 GB)

- [model-00013-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00013-of-000055.safetensors) (8.0 GB)

- [model-00014-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00014-of-000055.safetensors) (8.0 GB)

- [model-00015-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00015-of-000055.safetensors) (8.0 GB)

- [model-00016-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00016-of-000055.safetensors) (8.0 GB)

- [model-00017-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00017-of-000055.safetensors) (8.0 GB)

- [model-00018-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00018-of-000055.safetensors) (8.0 GB)

- [model-00019-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00019-of-000055.safetensors) (8.0 GB)

- [model-00020-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00020-of-000055.safetensors) (8.0 GB)

- [model-00021-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00021-of-000055.safetensors) (8.0 GB)

- [model-00022-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00022-of-000055.safetensors) (8.0 GB)

- [model-00023-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00023-of-000055.safetensors) (8.0 GB)

- [model-00024-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00024-of-000055.safetensors) (8.0 GB)

- [model-00025-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00025-of-000055.safetensors) (8.0 GB)

- [model-00026-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00026-of-000055.safetensors) (8.0 GB)

- [model-00027-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00027-of-000055.safetensors) (8.0 GB)

- [model-00028-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00028-of-000055.safetensors) (8.0 GB)

- [model-00029-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00029-of-000055.safetensors) (8.0 GB)

- [model-00030-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00030-of-000055.safetensors) (8.0 GB)

- [model-00031-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00031-of-000055.safetensors) (8.0 GB)

- [model-00032-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00032-of-000055.safetensors) (8.0 GB)

- [model-00033-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00033-of-000055.safetensors) (8.0 GB)

- [model-00034-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00034-of-000055.safetensors) (8.0 GB)

- [model-00035-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00035-of-000055.safetensors) (8.0 GB)

- [model-00036-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00036-of-000055.safetensors) (8.0 GB)

- [model-00037-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00037-of-000055.safetensors) (8.0 GB)

- [model-00038-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00038-of-000055.safetensors) (8.0 GB)

- [model-00039-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00039-of-000055.safetensors) (8.0 GB)

- [model-00040-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00040-of-000055.safetensors) (8.0 GB)

- [model-00041-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00041-of-000055.safetensors) (8.0 GB)

- [model-00042-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00042-of-000055.safetensors) (8.0 GB)

- [model-00043-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00043-of-000055.safetensors) (8.0 GB)

- [model-00044-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00044-of-000055.safetensors) (8.0 GB)

- [model-00045-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00045-of-000055.safetensors) (8.0 GB)

- [model-00046-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00046-of-000055.safetensors) (8.0 GB)

- [model-00047-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00047-of-000055.safetensors) (8.0 GB)

- [model-00048-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00048-of-000055.safetensors) (8.0 GB)

- [model-00049-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00049-of-000055.safetensors) (8.0 GB)

- [model-00050-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00050-of-000055.safetensors) (8.0 GB)

- [model-00051-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00051-of-000055.safetensors) (8.0 GB)

- [model-00052-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00052-of-000055.safetensors) (8.0 GB)

- [model-00053-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00053-of-000055.safetensors) (8.0 GB)

- [model-00054-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00054-of-000055.safetensors) (8.0 GB)

- [model-00055-of-000055.safetensors](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model-00055-of-000055.safetensors) (6.4 GB)

- [model.safetensors.index.json](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/model.safetensors.index.json) (2.7 MB)

- [tokenizer.json](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/tokenizer.json) (4.4 MB)

- [tokenizer_config.json](https://paddlenlp.bj.bcebos.com/models/community/deepseek-ai/DeepSeek-V2-Chat/tokenizer_config.json) (1.2 KB)


[Back to Main](../../)