README.md

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<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://arxiv.org/abs/2405.04434"><b>Paper Link</b>👁️</a>
</p>

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

## 1. Introduction

Last week, the release and buzz around DeepSeek-V2 have ignited widespread interest in MLA (Multi-head Latent Attention)! Many in the community suggested open-sourcing a smaller MoE model for in-depth research. And now DeepSeek-V2-Lite comes out:

- 16B total params, 2.4B active params, scratch training with 5.7T tokens
- Outperforms 7B dense and 16B MoE on many English & Chinese benchmarks
- Deployable on single 40G GPU, fine-tunable on 8x80G GPUs

DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. DeepSeek-V2 adopts innovative architectures including Multi-head Latent Attention (MLA) and DeepSeekMoE. MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation. 

## 2. News

- 2024.05.16: We released the DeepSeek-V2-Lite.
- 2024.05.06: We released the DeepSeek-V2.

## 3. Model Downloads

With DeepSeek-V2, we are open-sourcing base and chat models across two sizes:

<div align="center">

| **Model** | **#Total Params** | **#Activated Params** | **Context Length** | **Download** |
| :------------: | :------------: | :------------: | :------------: | :------------: |
| DeepSeek-V2-Lite | 16B | 2.4B | 32k   | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite)   |
| DeepSeek-V2-Lite-Chat (SFT)   | 16B | 2.4B | 32k   | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite-Chat)   |
| DeepSeek-V2   | 236B | 21B |  128k   | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V2)   |
| DeepSeek-V2-Chat (RL)   | 236B | 21B |  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.

## 4. Evaluation Results
### Base Model
#### Standard Benchmark
<div align="center">

| **Benchmark** | **Domain** | **DeepSeek 7B (Dense)** | **DeepSeekMoE 16B** | **DeepSeek-V2-Lite (MoE-16B)** |
|:-------------:|:----------:|:--------------:|:-----------------:|:--------------------------:|
| **Architecture**      | -    | MHA+Dense           | MHA+MoE              | MLA+MoE                       |
| **MMLU**      | English    | 48.2           | 45.0              | 58.3                       |
| **BBH**       | English    | 39.5           | 38.9              | 44.1                       |
| **C-Eval**    | Chinese    | 45.0           | 40.6              | 60.3                       |
| **CMMLU**     | Chinese    | 47.2           | 42.5              | 64.3                       |
| **HumanEval** | Code       | 26.2           | 26.8              | 29.9                       |
| **MBPP**      | Code       | 39.0           | 39.2              | 43.2                       |
| **GSM8K**     | Math       | 17.4           | 18.8              | 41.1                       |
| **Math**      | Math       | 3.3           | 4.3             | 17.1                       |

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


### Chat Model
#### Standard Benchmark

<div align="center">

| Benchmark | Domain         | DeepSeek 7B Chat (SFT) | DeepSeekMoE 16B Chat (SFT) | DeepSeek-V2-Lite 16B Chat (SFT) |
|:-----------:|:----------------:|:------------------:|:---------------:|:---------------------:|
| **MMLU**      | English        | 49.7             | 47.2          | 55.7                |
| **BBH**       | English        | 43.1             | 42.2          | 48.1                |
| **C-Eval**    | Chinese        | 44.7             | 40.0          | 60.1                |
| **CMMLU**     | Chinese        | 51.2             | 49.3          | 62.5                |
| **HumanEval** | Code           | 45.1             | 45.7          | 57.3                |
| **MBPP**      | Code           | 39.0             | 46.2          | 45.8                |
| **GSM8K**     | Math           | 62.6             | 62.2          | 72.0                |
| **Math**      | Math           | 14.7             | 15.2          | 27.9                |

</div>


## 5. 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>

## 6. How to run locally

**To utilize DeepSeek-V2-Lite in BF16 format for inference, 40GB*1 GPU is 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 torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig

model_name = "deepseek-ai/DeepSeek-V2-Lite"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
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="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=100)

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

#### Chat Completion
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig

model_name = "deepseek-ai/DeepSeek-V2-Lite-Chat"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
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.

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, 1
model_name = "deepseek-ai/DeepSeek-V2-Lite-Chat"
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)
```

### LangChain Support
Since our API is compatible with OpenAI, you can easily use it in [langchain](https://www.langchain.com/).
Here is an example:

```
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(
    model='deepseek-chat',
    openai_api_key=<your-deepseek-api-key>,
    openai_api_base='https://api.deepseek.com/v1',
    temperature=0.85,
    max_tokens=8000)
``` 
## 7. 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.

## 8. 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}
}
```

## 9. Contact
If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]).