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DeepSeek-V2

Model Download | Evaluation Results | API Platform | How to Use | License | Citation

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 an intermediate checkpoint of DeepSeek-V2 with additional 6 trillion tokens. Through this continued pre-training, DeepSeek-Coder-V2 substantially enhances the coding and mathematical reasoning capabilities of DeepSeek-V2, while maintaining comparable performance in general language tasks. Compared to DeepSeek-Coder-33B, 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 here.

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, 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 <|end▁of▁sentence|> 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:

In the last round of dialogue, note that "Assistant:" has no space after the colon. Adding a space might cause the following issues on the 16B-Lite model:

  • English questions receiving Chinese responses.
  • Responses containing garbled text.
  • Responses repeating excessively.

Older versions of Ollama had this bug (see deepseek-ai#12), but it has been fixed in the latest version.

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{zhu2024deepseek,
  title={DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence},
  author={Zhu, Qihao and Guo, Daya and Shao, Zhihong and Yang, Dejian and Wang, Peiyi and Xu, Runxin and Wu, Y and Li, Yukun and Gao, Huazuo and Ma, Shirong and others},
  journal={arXiv preprint arXiv:2406.11931},
  year={2024}
}

9. Contact

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

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