Paper Link👁️

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.

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. News

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

3. Model Downloads

Model	#Total Params	#Activated Params	Context Length	Download
DeepSeek-V2-Lite	16B	2.4B	32k	🤗 HuggingFace
DeepSeek-V2-Lite-Chat (SFT)	16B	2.4B	32k	🤗 HuggingFace
DeepSeek-V2	236B	21B	128k	🤗 HuggingFace
DeepSeek-V2-Chat (RL)	236B	21B	128k	🤗 HuggingFace

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 (Models larger than 67B)

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	48.2	53.1	45.1	48.8
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

Standard Benchmark (Models smaller than 16B)

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

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

Context Window

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 (Models larger than 67B)

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

Standard Benchmark (Models smaller than 16B)

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

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.

Chinese Open Ended Generation Evaluation

Alignbench (https://arxiv.org/abs/2311.18743)

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	8.36
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
DeepSeek-V2-Lite 16B Chat	开源	6.01	4.71	7.32

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.

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.

6. Chat Website

You can chat with the DeepSeek-V2 on DeepSeek's official website: chat.deepseek.com

7. API Platform

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

8. 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 for model inference.

Text Completion

import torch
from 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)}
# `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

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

import torch
from 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)}
# `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.

An example of chat template is as belows:

<｜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:

<｜begin▁of▁sentence｜>{system_message}

User: {user_message_1}

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

Assistant:

Inference with SGLang (recommended)

SGLang currently supports MLA optimizations, FP8 (W8A8), FP8 KV Cache, and Torch Compile, offering the best latency and throughput among open-source frameworks. Here are some example commands to launch an OpenAI API-compatible server:

# BF16, tensor parallelism = 8
python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-V2-Chat --tp 8 --trust-remote-code

# BF16, w/ torch.compile (The compilation can take several minutes)
python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-V2-Lite-Chat --trust-remote-code --enable-torch-compile

# FP8, tensor parallelism = 8, FP8 KV cache
python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-V2-Chat --tp 8 --trust-remote-code --quant fp8 --kv-cache-dtype fp8_e5m2

After launching the server, you can query it with OpenAI API

import openai
client = openai.Client(
    base_url="http://127.0.0.1:30000/v1", api_key="EMPTY")

# Chat completion
response = client.chat.completions.create(
    model="default",
    messages=[
        {"role": "system", "content": "You are a helpful AI assistant"},
        {"role": "user", "content": "List 3 countries and their capitals."},
    ],
    temperature=0,
    max_tokens=64,
)
print(response)

Inference with vLLM (recommended)

To utilize vLLM for model inference, please merge this Pull Request into your vLLM codebase: vllm-project/vllm#4650.

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

max_model_len, tp_size = 8192, 8
model_name = "deepseek-ai/DeepSeek-V2-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. 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)

9. License

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

10. 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}
}

11. Contact

If you have any questions, please raise an issue or contact us at service@deepseek.com.