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<!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> <!-- markdownlint-disable no-duplicate-header --> <div align="center"> <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V2" /> </div> <hr> <div align="center" style="line-height: 1;"> <a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;"> <img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/š¤%20Chat-DeepSeek%20V2-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;"> <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;"> <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;"> <img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;"> <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-CODE" style="margin: 2px;"> <img alt="Code License" src="https://img.shields.io/badge/Code_License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-MODEL" style="margin: 2px;"> <img alt="Model License" src="https://img.shields.io/badge/Model_License-Model_Agreement-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> </a> </div> <p align="center"> <a href="#2-model-downloads">Model Download</a> | <a href="#3-evaluation-results">Evaluation Results</a> | <a href="#5-api-platform">API Platform</a> | <a href="#6-how-to-run-locally">How to Use</a> | <a href="#7-license">License</a> | <a href="#8-citation">Citation</a> </p> <p align="center"> <a href="https://arxiv.org/pdf/2406.11931"><b>Paper Link</b>šļø</a> </p>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.
<p align="center"> <img width="100%" src="figures/performance.png"> </p>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.
<div align="center">Model | #Total Params | #Active Params | Context Length | Download |
---|---|---|---|---|
DeepSeek-Coder-V2-Lite-Base | 16B | 2.4B | 128k | š¤ HuggingFace |
DeepSeek-Coder-V2-Lite-Instruct | 16B | 2.4B | 128k | š¤ HuggingFace |
DeepSeek-Coder-V2-Base | 236B | 21B | 128k | š¤ HuggingFace |
DeepSeek-Coder-V2-Instruct | 236B | 21B | 128k | š¤ 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
<p align="center"> <img width="80%" src="figures/long_context.png"> </p>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.
<p align="center"> <img width="40%" src="figures/model_price.jpg"> </p>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 = """<ļ½fimābeginļ½>def quick_sort(arr):
if len(arr) <= 1:
return arr
pivot = arr[0]
left = []
right = []
<ļ½fimāholeļ½>
if arr[i] < pivot:
left.append(arr[i])
else:
right.append(arr[i])
return quick_sort(left) + [pivot] + quick_sort(right)<ļ½fimāendļ½>"""
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 <ļ½endāofāsentenceļ½> 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:
<ļ½begināofāsentenceļ½>User: {user_message_1}
Assistant: {assistant_message_1}<ļ½endāofāsentenceļ½>User: {user_message_2}
Assistant:
You can also add an optional system message:
<ļ½begināofāsentenceļ½>{system_message}
User: {user_message_1}
Assistant: {assistant_message_1}<ļ½endāofāsentenceļ½>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 https://github.com/deepseek-ai/DeepSeek-Coder-V2/issues/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: https://github.com/vllm-project/vllm/pull/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 service@deepseek.com.