/DeepSeek-Coder-V2

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DeepSeek-V2
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Paper Link👁️

DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence

1. Introduction

We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained from DeepSeek-Coder-V2-Base with 6 trillion tokens sourced from a high-quality and multi-source corpus. Through this continued pre-training, DeepSeek-Coder-V2 substantially enhances the coding and mathematical reasoning capabilities of DeepSeek-Coder-V2-Base, while maintaining comparable performance in general language tasks. Compared to DeepSeek-Coder, DeepSeek-Coder-V2 demonstrates significant advancements in various aspects of code-related tasks, as well as reasoning and general capabilities. Additionally, DeepSeek-Coder-V2 expands its support for programming languages from 86 to 338, while extending the context length from 16K to 128K.

In standard benchmark evaluations, DeepSeek-Coder-V2 achieves superior performance compared to closed-source models such as GPT4-Turbo, Claude 3 Opus, and Gemini 1.5 Pro in coding and math benchmarks. The list of supported programming languages can be found in the paper.

2. Model Downloads

We release the DeepSeek-Coder-V2 with 16B and 236B parameters based on the DeepSeekMoE framework, which has actived parameters of only 2.4B and 21B , including base and instruct models, to the public.

Model #Total Params #Active Params Context Length Download
DeepSeek-Coder-V2-Lite-Base 16B 2.4B 128k 🤗 HuggingFace
DeepSeek-Coder-V2-Lite-Instruct 16B 2.4B 128k 🤗 HuggingFace
DeepSeek-Coder-V2-Base 236B 21B 128k 🤗 HuggingFace
DeepSeek-Coder-V2-Instruct 236B 21B 128k 🤗 HuggingFace

3. Evaluation Results

3.1 Code Generation

#TP #AP HumanEval MBPP+ LiveCodeBench USACO
Closed-Source Models
Gemini-1.5-Pro - - 83.5 74.6 34.1 4.9
Claude-3-Opus - - 84.2 72.0 34.6 7.8
GPT-4-Turbo-1106 - - 87.8 69.3 37.1 11.1
GPT-4-Turbo-0409 - - 88.2 72.2 45.7 12.3
GPT-4o-0513 - - 91.0 73.5 43.4 18.8
Open-Source Models
CodeStral 22B 22B 78.1 68.2 31.0 4.6
DeepSeek-Coder-Instruct 33B 33B 79.3 70.1 22.5 4.2
Llama3-Instruct 70B 70B 81.1 68.8 28.7 3.3
DeepSeek-Coder-V2-Lite-Instruct 16B 2.4B 81.1 68.8 24.3 6.5
DeepSeek-Coder-V2-Instruct 236B 21B 90.2 76.2 43.4 12.1

3.2 Code Completion

Model #TP #AP RepoBench (Python) RepoBench (Java) HumanEval FIM
CodeStral 22B 22B 46.1 45.7 83.0
DeepSeek-Coder-Base 7B 7B 36.2 43.3 86.1
DeepSeek-Coder-Base 33B 33B 39.1 44.8 86.4
DeepSeek-Coder-V2-Lite-Base 16B 2.4B 38.9 43.3 86.4

3.3 Code Fixing

#TP #AP Defects4J SWE-Bench Aider
Closed-Source Models
Gemini-1.5-Pro - - 18.6 19.3 57.1
Claude-3-Opus - - 25.5 11.7 68.4
GPT-4-Turbo-1106 - - 22.8 22.7 65.4
GPT-4-Turbo-0409 - - 24.3 18.3 63.9
GPT-4o-0513 - - 26.1 26.7 72.9
Open-Source Models
CodeStral 22B 22B 17.8 2.7 51.1
DeepSeek-Coder-Instruct 33B 33B 11.3 0.0 54.5
Llama3-Instruct 70B 70B 16.2 - 49.2
DeepSeek-Coder-V2-Lite-Instruct 16B 2.4B 9.2 0.0 44.4
DeepSeek-Coder-V2-Instruct 236B 21B 21.0 12.7 73.7

3.4 Mathematical Reasoning

#TP #AP GSM8K MATH AIME 2024 Math Odyssey
Closed-Source Models
Gemini-1.5-Pro - - 90.8 67.7 2/30 45.0
Claude-3-Opus - - 95.0 60.1 2/30 40.6
GPT-4-Turbo-1106 - - 91.4 64.3 1/30 49.1
GPT-4-Turbo-0409 - - 93.7 73.4 3/30 46.8
GPT-4o-0513 - - 95.8 76.6 2/30 53.2
Open-Source Models
Llama3-Instruct 70B 70B 93.0 50.4 1/30 27.9
DeepSeek-Coder-V2-Lite-Instruct 16B 2.4B 86.4 61.8 0/30 44.4
DeepSeek-Coder-V2-Instruct 236B 21B 94.9 75.7 4/30 53.7

3.5 General Natural Language

Benchmark Domain DeepSeek-V2-Lite Chat DeepSeek-Coder-V2-Lite Instruct DeepSeek-V2 Chat DeepSeek-Coder-V2 Instruct
BBH English 48.1 61.2 79.7 83.9
MMLU English 55.7 60.1 78.1 79.2
ARC-Easy English 86.1 88.9 98.1 97.4
ARC-Challenge English 73.4 77.4 92.3 92.8
TriviaQA English 65.2 59.5 86.7 82.3
NaturalQuestions English 35.5 30.8 53.4 47.5
AGIEval English 42.8 28.7 61.4 60
CLUEWSC Chinese 80.0 76.5 89.9 85.9
C-Eval Chinese 60.1 61.6 78.0 79.4
CMMLU Chinese 62.5 62.7 81.6 80.9
Arena-Hard - 11.4 38.1 41.6 65.0
AlpaceEval 2.0 - 16.9 17.7 38.9 36.9
MT-Bench - 7.37 7.81 8.97 8.77
Alignbench - 6.02 6.83 7.91 7.84

3.6 Context Window

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

4. Chat Website

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

5. 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.

6. How to run locally

Here, we provide some examples of how to use DeepSeek-Coder-V2-Lite model. If you want to utilize DeepSeek-Coder-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.

Code Completion

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
input_text = "#write a quick sort algorithm"
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Code Insertion

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
input_text = """<｜fim▁begin｜>def quick_sort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[0]
    left = []
    right = []
<｜fim▁hole｜>
        if arr[i] < pivot:
            left.append(arr[i])
        else:
            right.append(arr[i])
    return quick_sort(left) + [pivot] + quick_sort(right)<｜fim▁end｜>"""
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(input_text):])

Chat Completion

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
messages=[
    { 'role': 'user', 'content': "write a quick sort algorithm in python."}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
# tokenizer.eos_token_id is the id of <|EOT|> token
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))

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: vllm-project/vllm#4650.

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

max_model_len, tp_size = 8192, 1
model_name = "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct"
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": "write a quick sort algorithm in python."}],
    [{"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)

7. License

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

8. Citation

@article{deepseek-coder-v2,
  author={Qihao Zhu and Daya Guo and Zhihong Shao and Dejian Yang and Peiyi Wang and Runxin Xu and Y. Wu and Yukun Li and Huazuo Gao and Shirong Ma and Wangding Zeng and Xiao Bi and Zihui Gu and Hanwei Xu and Damai Dai and Kai Dong and Liyue Zhang and Yishi Piao and Zhibin Gou and Zhenda Xie and Zhewen Hao and Bingxuan Wang and Junxiao Song and Deli Chen and Xin Xie and Kang Guan and Yuxiang You and Aixin Liu and Qiushi Du and Wenjun Gao and Xuan Lu and Qinyu Chen and Yaohui Wang and Chengqi Deng and Jiashi Li and Chenggang Zhao and Chong Ruan and Fuli Luo and Wenfeng Liang},
  title={DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence},
  year={2024},
  url={https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/paper.pdf}
}

9. Contact

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