base on DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence <!-- 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/🤖%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>👁️</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](supported_langs.txt). ## 2. Model Downloads 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. <div align="center"> | **Model** | **#Total Params** | **#Active Params** | **Context Length** | **Download** | | :-----------------------------: | :---------------: | :----------------: | :----------------: | :----------------------------------------------------------: | | DeepSeek-Coder-V2-Lite-Base | 16B | 2.4B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Base) | | DeepSeek-Coder-V2-Lite-Instruct | 16B | 2.4B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct) | | DeepSeek-Coder-V2-Base | 236B | 21B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Base) | | DeepSeek-Coder-V2-Instruct | 236B | 21B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Instruct) | </div> ## 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](https://coder.deepseek.com/sign_in) ## 5. API Platform 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. <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](https://github.com/huggingface/transformers) for model inference. #### Code Completion ```python 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 ```python 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 ```python 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: ```bash <｜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: ```bash <｜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 https://github.com/deepseek-ai/DeepSeek-Coder-V2/issues/12), but it has been fixed in the latest version. ### Inference with SGLang (recommended) [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: ```bash # BF16, tensor parallelism = 8 python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-Coder-V2-Instruct --tp 8 --trust-remote-code # BF16, w/ torch.compile (The compilation can take several minutes) python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct --trust-remote-code --enable-torch-compile # FP8, tensor parallelism = 8, FP8 KV cache python3 -m sglang.launch_server --model neuralmagic/DeepSeek-Coder-V2-Instruct-FP8 --tp 8 --trust-remote-code --kv-cache-dtype fp8_e5m2 ``` After launching the server, you can query it with OpenAI API ``` import openai client = openai.Client( base_url="http://127.0.0.1:30000/v1", api_key="EMPTY") # Chat completion response = client.chat.completions.create( model="default", messages=[ {"role": "system", "content": "You are a helpful AI assistant"}, {"role": "user", "content": "List 3 countries and their capitals."}, ], temperature=0, max_tokens=64, ) print(response) ``` ### Inference with vLLM (recommended) 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. ```python 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](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 [[email protected]]([email protected]). ", Assign "at most 3 tags" to the expected json: {"id":"12613","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"