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DeepSeek LLM

Model Download | Evaluation Results | Model Architecture | API Platform | License | Citation

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. Model Downloads

Model Context Length Download
DeepSeek-V2 128k 🤗 HuggingFace
DeepSeek-V2-Chat (RL) 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.

3. Evaluation Results

Base Model

Standard Benchmark

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
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

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

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 8.35
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

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.

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.

5. Chat Website

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

6. 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.

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 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 vLLM (recommended)

To utilize vLLM for model inference, please merge this Pull Request into your vLLM codebase: https://github.com/vllm-project/vllm/pull/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)

8. 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.

9. 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}
}

10. Contact

If you have any questions, please raise an issue or contact us at [email protected].