commit c84bfab8c9b6f5472fd9fb45d453a695fccae158 Author: guoday Date: Mon Jun 17 20:12:50 2024 +0800 upload readme and paper diff --git a/LICENSE-CODE b/LICENSE-CODE new file mode 100644 index 0000000..d84f527 --- /dev/null +++ b/LICENSE-CODE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 DeepSeek + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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+ DeepSeek-V2 +
+
+
+ + Homepage + + + Chat + + + Hugging Face + +
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+ + Discord + + + Wechat + + + Twitter Follow + +
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+ + Code License + + + Model License + +
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+ 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 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](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. + +
+ +| **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) | + +
+ + + +## 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](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/). 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](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 <|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: + +```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: +``` + +### 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{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. 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