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--- |
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license: other |
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license_name: deepseek-license |
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license_link: LICENSE |
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--- |
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<!-- markdownlint-disable first-line-h1 --> |
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<!-- markdownlint-disable html --> |
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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 href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;"> |
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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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<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="#4-api-platform">API Platform</a> | |
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<a href="#5-how-to-run-locally">How to Use</a> | |
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<a href="#6-license">License</a> | |
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</p> |
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<p align="center"> |
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<a href="https://github.com/deepseek-ai/DeepSeek-Coder-V2/paper.pdf"><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 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. |
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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 in the paper. |
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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. Chat Website |
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You can chat with the DeepSeek-Coder-V2 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com/sign_in) |
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## 4. API Platform |
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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. |
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<p align="center"> |
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<img width="40%" src="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/figures/model_price.jpg?raw=true"> |
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</p> |
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## 5. 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 <|EOT|> 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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### 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."}], |
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[{"role": "user", "content": "Write a piece of quicksort code in C++."}], |
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] |
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prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list] |
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outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params) |
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generated_text = [output.outputs[0].text for output in outputs] |
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print(generated_text) |
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``` |
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## 6. License |
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This code repository is licensed under [the MIT License](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/LICENSE-CODE). The use of DeepSeek-Coder-V2 Base/Instruct models is subject to [the Model License](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/LICENSE-MODEL). DeepSeek-Coder-V2 series (including Base and Instruct) supports commercial use. |
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## 7. Contact |
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If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]). |
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