mirror of
https://github.com/deepseek-ai/DeepSeek-Coder-V2
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359 lines
20 KiB
Markdown
359 lines
20 KiB
Markdown
<!-- markdownlint-disable first-line-h1 -->
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<!-- markdownlint-disable html -->
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<!-- markdownlint-disable no-duplicate-header -->
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<div align="center">
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<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V2" />
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</div>
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<hr>
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<div align="center" style="line-height: 1;">
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<a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;">
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<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;"/>
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</a>
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<a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;">
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<img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20V2-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
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</a>
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<a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;">
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<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;"/>
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</a>
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</div>
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<div align="center" style="line-height: 1;">
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<a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;">
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<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;"/>
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<a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;">
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<img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
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<a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;">
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<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;"/>
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</div>
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<div align="center" style="line-height: 1;">
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<a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-CODE" style="margin: 2px;">
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<img alt="Code License" src="https://img.shields.io/badge/Code_License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
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</a>
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<a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-MODEL" style="margin: 2px;">
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<img alt="Model License" src="https://img.shields.io/badge/Model_License-Model_Agreement-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
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</a>
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</div>
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<p align="center">
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<a href="#2-model-downloads">Model Download</a> |
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<a href="#3-evaluation-results">Evaluation Results</a> |
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<a href="#5-api-platform">API Platform</a> |
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<a href="#6-how-to-run-locally">How to Use</a> |
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<a href="#7-license">License</a> |
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<a href="#8-citation">Citation</a>
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</p>
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<p align="center">
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<a href="https://arxiv.org/pdf/2406.11931"><b>Paper Link</b>👁️</a>
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</p>
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# DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence
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## 1. Introduction
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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.
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<p align="center">
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<img width="100%" src="figures/performance.png">
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</p>
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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](supported_langs.txt).
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## 2. Model Downloads
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We release the DeepSeek-Coder-V2 with 16B and 236B parameters based on the [DeepSeekMoE](https://arxiv.org/pdf/2401.06066) framework, which has actived parameters of only 2.4B and 21B , including base and instruct models, to the public.
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<div align="center">
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| **Model** | **#Total Params** | **#Active Params** | **Context Length** | **Download** |
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| :-----------------------------: | :---------------: | :----------------: | :----------------: | :----------------------------------------------------------: |
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| DeepSeek-Coder-V2-Lite-Base | 16B | 2.4B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Base) |
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| DeepSeek-Coder-V2-Lite-Instruct | 16B | 2.4B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct) |
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| DeepSeek-Coder-V2-Base | 236B | 21B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Base) |
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| DeepSeek-Coder-V2-Instruct | 236B | 21B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Instruct) |
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</div>
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## 3. Evaluation Results
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### 3.1 Code Generation
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| | #TP | #AP | HumanEval | MBPP+ | LiveCodeBench | USACO |
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|:------------|:--------:|:--------:|:--------:|:--------:|:--------:|:-----------:|
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| **Closed-Source Models** | | | | | | |
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| **Gemini-1.5-Pro** | - | - | 83.5 | **74.6** | 34.1 | 4.9 |
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| **Claude-3-Opus** | - | - | 84.2 | 72.0 | 34.6 | 7.8 |
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| **GPT-4-Turbo-1106** | - | - | 87.8 | 69.3 | 37.1 | 11.1 |
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| **GPT-4-Turbo-0409** | - | - | 88.2 | 72.2 | **45.7** | 12.3 |
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| **GPT-4o-0513** | - | - | **91.0** | 73.5 | 43.4 | **18.8** |
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| **Open-Source Models** | | | | | | |
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| **CodeStral** | 22B | 22B | 78.1 | 68.2 | 31.0 | 4.6 |
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| **DeepSeek-Coder-Instruct** | 33B | 33B | 79.3 | 70.1 | 22.5 | 4.2 |
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| **Llama3-Instruct** | 70B | 70B | 81.1 | 68.8 | 28.7 | 3.3 |
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| **DeepSeek-Coder-V2-Lite-Instruct** | 16B | 2.4B | 81.1 | 68.8 | 24.3 | 6.5 |
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| **DeepSeek-Coder-V2-Instruct** | 236B | 21B | **90.2** | **76.2** | **43.4** | **12.1** |
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### 3.2 Code Completion
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| Model | #TP | #AP | RepoBench (Python) | RepoBench (Java) | HumanEval FIM |
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| :------------------------------ | :--: | :--: | :----------------: | :--------------: | :-----------: |
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| **CodeStral** | 22B | 22B | **46.1** | **45.7** | 83.0 |
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| **DeepSeek-Coder-Base** | 7B | 7B | 36.2 | 43.3 | 86.1 |
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| **DeepSeek-Coder-Base** | 33B | 33B | 39.1 | 44.8 | **86.4** |
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| **DeepSeek-Coder-V2-Lite-Base** | 16B | 2.4B | 38.9 | 43.3 | **86.4** |
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### 3.3 Code Fixing
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| | #TP | #AP | Defects4J | SWE-Bench | Aider |
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| ----------------------------------- | :--: | :--: | :-------: | :-------: | :------: |
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| **Closed-Source Models** | | | | | |
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| **Gemini-1.5-Pro** | - | - | 18.6 | 19.3 | 57.1 |
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| **Claude-3-Opus** | - | - | 25.5 | 11.7 | 68.4 |
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| **GPT-4-Turbo-1106** | - | - | 22.8 | 22.7 | 65.4 |
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| **GPT-4-Turbo-0409** | - | - | 24.3 | 18.3 | 63.9 |
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| **GPT-4o-0513** | - | - | **26.1** | **26.7** | **72.9** |
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| **Open-Source Models** | | | | | |
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| **CodeStral** | 22B | 22B | 17.8 | 2.7 | 51.1 |
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| **DeepSeek-Coder-Instruct** | 33B | 33B | 11.3 | 0.0 | 54.5 |
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| **Llama3-Instruct** | 70B | 70B | 16.2 | - | 49.2 |
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| **DeepSeek-Coder-V2-Lite-Instruct** | 16B | 2.4B | 9.2 | 0.0 | 44.4 |
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| **DeepSeek-Coder-V2-Instruct** | 236B | 21B | **21.0** | **12.7** | **73.7** |
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### 3.4 Mathematical Reasoning
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| | #TP | #AP | GSM8K | MATH | AIME 2024 | Math Odyssey |
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| ----------------------------------- | :--: | :--: | :------: | :------: | :-------: | :----------: |
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| **Closed-Source Models** | | | | | | |
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| **Gemini-1.5-Pro** | - | - | 90.8 | 67.7 | 2/30 | 45.0 |
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| **Claude-3-Opus** | - | - | 95.0 | 60.1 | 2/30 | 40.6 |
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| **GPT-4-Turbo-1106** | - | - | 91.4 | 64.3 | 1/30 | 49.1 |
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| **GPT-4-Turbo-0409** | - | - | 93.7 | 73.4 | **3/30** | 46.8 |
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| **GPT-4o-0513** | - | - | **95.8** | **76.6** | 2/30 | **53.2** |
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| **Open-Source Models** | | | | | | |
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| **Llama3-Instruct** | 70B | 70B | 93.0 | 50.4 | 1/30 | 27.9 |
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| **DeepSeek-Coder-V2-Lite-Instruct** | 16B | 2.4B | 86.4 | 61.8 | 0/30 | 44.4 |
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| **DeepSeek-Coder-V2-Instruct** | 236B | 21B | **94.9** | **75.7** | **4/30** | **53.7** |
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### 3.5 General Natural Language
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| Benchmark | Domain | DeepSeek-V2-Lite Chat | DeepSeek-Coder-V2-Lite Instruct | DeepSeek-V2 Chat | DeepSeek-Coder-V2 Instruct |
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| :------------------: | :-----: | :-------------------: | :-----------------------------: | :--------------: | :------------------------: |
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| **BBH** | English | 48.1 | 61.2 | 79.7 | **83.9** |
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| **MMLU** | English | 55.7 | 60.1 | 78.1 | **79.2** |
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| **ARC-Easy** | English | 86.1 | 88.9 | **98.1** | 97.4 |
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| **ARC-Challenge** | English | 73.4 | 77.4 | 92.3 | **92.8** |
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| **TriviaQA** | English | 65.2 | 59.5 | **86.7** | 82.3 |
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| **NaturalQuestions** | English | 35.5 | 30.8 | **53.4** | 47.5 |
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| **AGIEval** | English | 42.8 | 28.7 | **61.4** | 60 |
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| **CLUEWSC** | Chinese | 80.0 | 76.5 | **89.9** | 85.9 |
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| **C-Eval** | Chinese | 60.1 | 61.6 | 78.0 | **79.4** |
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| **CMMLU** | Chinese | 62.5 | 62.7 | **81.6** | 80.9 |
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| **Arena-Hard** | - | 11.4 | 38.1 | 41.6 | **65.0** |
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| **AlpaceEval 2.0** | - | 16.9 | 17.7 | **38.9** | 36.9 |
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| **MT-Bench** | - | 7.37 | 7.81 | **8.97** | 8.77 |
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| **Alignbench** | - | 6.02 | 6.83 | **7.91** | 7.84 |
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### 3.6 Context Window
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<p align="center">
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<img width="80%" src="figures/long_context.png">
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</p>
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Evaluation results on the ``Needle In A Haystack`` (NIAH) tests. DeepSeek-Coder-V2 performs well across all context window lengths up to **128K**.
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## 4. Chat Website
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You can chat with the DeepSeek-Coder-V2 on DeepSeek's official website: [coder.deepseek.com](https://coder.deepseek.com/sign_in)
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## 5. API Platform
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We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/), and you can also pay-as-you-go at an unbeatable price.
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<p align="center">
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<img width="40%" src="figures/model_price.jpg">
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</p>
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## 6. How to run locally
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**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.**
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### Inference with Huggingface's Transformers
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You can directly employ [Huggingface's Transformers](https://github.com/huggingface/transformers) for model inference.
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#### Code Completion
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
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input_text = "#write a quick sort algorithm"
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inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_length=128)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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#### Code Insertion
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
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input_text = """<｜fim▁begin｜>def quick_sort(arr):
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if len(arr) <= 1:
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return arr
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pivot = arr[0]
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left = []
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right = []
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<｜fim▁hole｜>
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if arr[i] < pivot:
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left.append(arr[i])
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else:
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right.append(arr[i])
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return quick_sort(left) + [pivot] + quick_sort(right)<｜fim▁end｜>"""
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inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_length=128)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(input_text):])
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```
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#### Chat Completion
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
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messages=[
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{ 'role': 'user', 'content': "write a quick sort algorithm in python."}
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]
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inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
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# tokenizer.eos_token_id is the id of <｜end▁of▁sentence｜> token
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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)
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print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
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```
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The complete chat template can be found within `tokenizer_config.json` located in the huggingface model repository.
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An example of chat template is as belows:
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```bash
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<｜begin▁of▁sentence｜>User: {user_message_1}
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Assistant: {assistant_message_1}<｜end▁of▁sentence｜>User: {user_message_2}
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Assistant:
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```
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You can also add an optional system message:
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```bash
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<｜begin▁of▁sentence｜>{system_message}
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User: {user_message_1}
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Assistant: {assistant_message_1}<｜end▁of▁sentence｜>User: {user_message_2}
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Assistant:
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```
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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:
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- English questions receiving Chinese responses.
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- Responses containing garbled text.
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- Responses repeating excessively.
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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.
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### Inference with SGLang (recommended)
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[SGLang](https://github.com/sgl-project/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:
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```bash
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# BF16, tensor parallelism = 8
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python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-Coder-V2-Instruct --tp 8 --trust-remote-code
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# BF16, w/ torch.compile (The compilation can take several minutes)
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python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct --trust-remote-code --enable-torch-compile
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# FP8, tensor parallelism = 8, FP8 KV cache
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python3 -m sglang.launch_server --model neuralmagic/DeepSeek-Coder-V2-Instruct-FP8 --tp 8 --trust-remote-code --kv-cache-dtype fp8_e5m2
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```
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After launching the server, you can query it with OpenAI API
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```
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import openai
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client = openai.Client(
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base_url="http://127.0.0.1:30000/v1", api_key="EMPTY")
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# Chat completion
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response = client.chat.completions.create(
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model="default",
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messages=[
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{"role": "system", "content": "You are a helpful AI assistant"},
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{"role": "user", "content": "List 3 countries and their capitals."},
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],
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temperature=0,
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max_tokens=64,
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)
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print(response)
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```
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### Inference with vLLM (recommended)
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To utilize [vLLM](https://github.com/vllm-project/vllm) for model inference, please merge this Pull Request into your vLLM codebase: https://github.com/vllm-project/vllm/pull/4650.
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```python
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from transformers import AutoTokenizer
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from vllm import LLM, SamplingParams
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max_model_len, tp_size = 8192, 1
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model_name = "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True, enforce_eager=True)
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sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])
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messages_list = [
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[{"role": "user", "content": "Who are you?"}],
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||
[{"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](LICENSE-CODE). The use of DeepSeek-Coder-V2 Base/Instruct models is subject to [the Model License](LICENSE-MODEL). DeepSeek-Coder-V2 series (including Base and Instruct) supports commercial use.
|
||
|
||
## 8. Citation
|
||
```latex
|
||
@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](service@deepseek.com).
|