base on DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model <!-- 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="#4-model-architecture">Model Architecture</a> | <a href="#6-api-platform">API Platform</a> | <a href="#8-license">License</a> | <a href="#9-citation">Citation</a> </p> <p align="center"> <a href="https://arxiv.org/abs/2405.04434"><b>Paper Link</b>👁️</a> </p> # DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model ## 1. Introduction Today, we’re introducing DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token. Compared with DeepSeek 67B, DeepSeek-V2 achieves stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum generation throughput to 5.76 times. <p align="center"> <div style="display: flex; justify-content: center;"> <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/activationparameters.png?raw=true" style="height:300px; width:auto; margin-right:10px"> <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/trainingcost.png?raw=true" style="height:300px; width:auto; margin-left:10px"> </div> </p> We pretrained DeepSeek-V2 on a diverse and high-quality corpus comprising 8.1 trillion tokens. This comprehensive pretraining was followed by a process of Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) to fully unleash the model's capabilities. The evaluation results validate the effectiveness of our approach as DeepSeek-V2 achieves remarkable performance on both standard benchmarks and open-ended generation evaluation. ## 2. News - 2024.05.16: We released the DeepSeek-V2-Lite. - 2024.05.06: We released the DeepSeek-V2. ## 3. Model Downloads <div align="center"> | **Model** | **#Total Params** | **#Activated Params** | **Context Length** | **Download** | | :------------: | :------------: | :------------: | :------------: | :------------: | | DeepSeek-V2-Lite | 16B | 2.4B | 32k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite) | | DeepSeek-V2-Lite-Chat (SFT) | 16B | 2.4B | 32k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite-Chat) | | DeepSeek-V2 | 236B | 21B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V2) | | DeepSeek-V2-Chat (RL) | 236B | 21B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V2-Chat) | </div> Due to the constraints of HuggingFace, the open-source code currently experiences slower performance than our internal codebase when running on GPUs with Huggingface. To facilitate the efficient execution of our model, we offer a dedicated vllm solution that optimizes performance for running our model effectively. ## 4. Evaluation Results ### Base Model #### Standard Benchmark (Models larger than 67B) <div align="center"> | **Benchmark** | **Domain** | **LLaMA3 70B** | **Mixtral 8x22B** | **DeepSeek-V1 (Dense-67B)** | **DeepSeek-V2 (MoE-236B)** | |:-----------:|:--------:|:------------:|:---------------:|:-------------------------:|:------------------------:| | **MMLU** | English | 78.9 | 77.6 | 71.3 | 78.5 | | **BBH** | English | 81.0 | 78.9 | 68.7 | 78.9 | | **C-Eval** | Chinese | 67.5 | 58.6 | 66.1 | 81.7 | | **CMMLU** | Chinese | 69.3 | 60.0 | 70.8 | 84.0 | | **HumanEval** | Code | 48.2 | 53.1 | 45.1 | 48.8 | | **MBPP** | Code | 68.6 | 64.2 | 57.4 | 66.6 | | **GSM8K** | Math | 83.0 | 80.3 | 63.4 | 79.2 | | **Math** | Math | 42.2 | 42.5 | 18.7 | 43.6 | </div> #### Standard Benchmark (Models smaller than 16B) <div align="center"> | **Benchmark** | **Domain** | **DeepSeek 7B (Dense)** | **DeepSeekMoE 16B** | **DeepSeek-V2-Lite (MoE-16B)** | |:-------------:|:----------:|:--------------:|:-----------------:|:--------------------------:| | **Architecture** | - | MHA+Dense | MHA+MoE | MLA+MoE | | **MMLU** | English | 48.2 | 45.0 | 58.3 | | **BBH** | English | 39.5 | 38.9 | 44.1 | | **C-Eval** | Chinese | 45.0 | 40.6 | 60.3 | | **CMMLU** | Chinese | 47.2 | 42.5 | 64.3 | | **HumanEval** | Code | 26.2 | 26.8 | 29.9 | | **MBPP** | Code | 39.0 | 39.2 | 43.2 | | **GSM8K** | Math | 17.4 | 18.8 | 41.1 | | **Math** | Math | 3.3 | 4.3 | 17.1 | </div> For more evaluation details, such as few-shot settings and prompts, please check our paper. #### Context Window <p align="center"> <img width="80%" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/niah.png?raw=true"> </p> Evaluation results on the ``Needle In A Haystack`` (NIAH) tests. DeepSeek-V2 performs well across all context window lengths up to **128K**. ### Chat Model #### Standard Benchmark (Models larger than 67B) <div align="center"> | Benchmark | Domain | QWen1.5 72B Chat | Mixtral 8x22B | LLaMA3 70B Instruct | DeepSeek-V1 Chat (SFT) | DeepSeek-V2 Chat (SFT) | DeepSeek-V2 Chat (RL) | |:-----------:|:----------------:|:------------------:|:---------------:|:---------------------:|:-------------:|:-----------------------:|:----------------------:| | **MMLU** | English | 76.2 | 77.8 | 80.3 | 71.1 | 78.4 | 77.8 | | **BBH** | English | 65.9 | 78.4 | 80.1 | 71.7 | 81.3 | 79.7 | | **C-Eval** | Chinese | 82.2 | 60.0 | 67.9 | 65.2 | 80.9 | 78.0 | | **CMMLU** | Chinese | 82.9 | 61.0 | 70.7 | 67.8 | 82.4 | 81.6 | | **HumanEval** | Code | 68.9 | 75.0 | 76.2 | 73.8 | 76.8 | 81.1 | | **MBPP** | Code | 52.2 | 64.4 | 69.8 | 61.4 | 70.4 | 72.0 | | **LiveCodeBench (0901-0401)** | Code | 18.8 | 25.0 | 30.5 | 18.3 | 28.7 | 32.5 | | **GSM8K** | Math | 81.9 | 87.9 | 93.2 | 84.1 | 90.8 | 92.2 | | **Math** | Math | 40.6 | 49.8 | 48.5 | 32.6 | 52.7 | 53.9 | </div> #### Standard Benchmark (Models smaller than 16B) <div align="center"> | Benchmark | Domain | DeepSeek 7B Chat (SFT) | DeepSeekMoE 16B Chat (SFT) | DeepSeek-V2-Lite 16B Chat (SFT) | |:-----------:|:----------------:|:------------------:|:---------------:|:---------------------:| | **MMLU** | English | 49.7 | 47.2 | 55.7 | | **BBH** | English | 43.1 | 42.2 | 48.1 | | **C-Eval** | Chinese | 44.7 | 40.0 | 60.1 | | **CMMLU** | Chinese | 51.2 | 49.3 | 62.5 | | **HumanEval** | Code | 45.1 | 45.7 | 57.3 | | **MBPP** | Code | 39.0 | 46.2 | 45.8 | | **GSM8K** | Math | 62.6 | 62.2 | 72.0 | | **Math** | Math | 14.7 | 15.2 | 27.9 | </div> #### English Open Ended Generation Evaluation We evaluate our model on AlpacaEval 2.0 and MTBench, showing the competitive performance of DeepSeek-V2-Chat-RL on English conversation generation. <p align="center"> <img width="50%" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/mtbench.png?raw=true" /> </p> #### Chinese Open Ended Generation Evaluation **Alignbench** (https://arxiv.org/abs/2311.18743) <div align="center"> | **模型** | **开源/闭源** | **总分** | **中文推理** | **中文语言** | | :---: | :---: | :---: | :---: | :---: | | gpt-4-1106-preview | 闭源 | 8.01 | 7.73 | 8.29 | | DeepSeek-V2 Chat (RL) | 开源 | 7.91 | 7.45 | 8.36 | | erniebot-4.0-202404 (文心一言) | 闭源 | 7.89 | 7.61 | 8.17 | | DeepSeek-V2 Chat (SFT) | 开源 | 7.74 | 7.30 | 8.17 | | gpt-4-0613 | 闭源 | 7.53 | 7.47 | 7.59 | | erniebot-4.0-202312 (文心一言) | 闭源 | 7.36 | 6.84 | 7.88 | | moonshot-v1-32k-202404 (月之暗面) | 闭源 | 7.22 | 6.42 | 8.02 | | Qwen1.5-72B-Chat (通义千问) | 开源 | 7.19 | 6.45 | 7.93 | | DeepSeek-67B-Chat | 开源 | 6.43 | 5.75 | 7.11 | | Yi-34B-Chat (零一万物) | 开源 | 6.12 | 4.86 | 7.38 | | gpt-3.5-turbo-0613 | 闭源 | 6.08 | 5.35 | 6.71 | | DeepSeek-V2-Lite 16B Chat | 开源 | 6.01 | 4.71 | 7.32 | </div> #### Coding Benchmarks We evaluate our model on LiveCodeBench (0901-0401), a benchmark designed for live coding challenges. As illustrated, DeepSeek-V2 demonstrates considerable proficiency in LiveCodeBench, achieving a Pass@1 score that surpasses several other sophisticated models. This performance highlights the model's effectiveness in tackling live coding tasks. <p align="center"> <img width="50%" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/code_benchmarks.png?raw=true"> </p> ## 5. Model Architecture DeepSeek-V2 adopts innovative architectures to guarantee economical training and efficient inference： - For attention, we design MLA (Multi-head Latent Attention), which utilizes low-rank key-value union compression to eliminate the bottleneck of inference-time key-value cache, thus supporting efficient inference. - For Feed-Forward Networks (FFNs), we adopt DeepSeekMoE architecture, a high-performance MoE architecture that enables training stronger models at lower costs. <p align="center"> <img width="90%" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/architecture.png?raw=true" /> </p> ## 6. Chat Website You can chat with the DeepSeek-V2 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com/sign_in) ## 7. 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. <p align="center"> <img width="40%" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/model_price.png?raw=true"> </p> ## 8. How to run locally **To utilize DeepSeek-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. #### Text Completion ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig model_name = "deepseek-ai/DeepSeek-V2" tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) # `max_memory` should be set based on your devices max_memory = {i: "75GB" for i in range(8)} # `device_map` cannot be set to `auto` model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, device_map="sequential", torch_dtype=torch.bfloat16, max_memory=max_memory, attn_implementation="eager") model.generation_config = GenerationConfig.from_pretrained(model_name) model.generation_config.pad_token_id = model.generation_config.eos_token_id text = "An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is" inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs.to(model.device), max_new_tokens=100) result = tokenizer.decode(outputs[0], skip_special_tokens=True) print(result) ``` #### Chat Completion ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig model_name = "deepseek-ai/DeepSeek-V2-Chat" tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) # `max_memory` should be set based on your devices max_memory = {i: "75GB" for i in range(8)} # `device_map` cannot be set to `auto` model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, device_map="sequential", torch_dtype=torch.bfloat16, max_memory=max_memory, attn_implementation="eager") model.generation_config = GenerationConfig.from_pretrained(model_name) model.generation_config.pad_token_id = model.generation_config.eos_token_id messages = [ {"role": "user", "content": "Write a piece of quicksort code in C++"} ] input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt") outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100) result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True) print(result) ``` 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 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-V2-Chat --tp 8 --trust-remote-code # BF16, w/ torch.compile (The compilation can take several minutes) python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-V2-Lite-Chat --trust-remote-code --enable-torch-compile # FP8, tensor parallelism = 8, FP8 KV cache python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-V2-Chat --tp 8 --trust-remote-code --quant fp8 --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, 8 model_name = "deepseek-ai/DeepSeek-V2-Chat" 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": "Translate the following content into Chinese directly: DeepSeek-V2 adopts innovative architectures to guarantee economical training and efficient inference."}], [{"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) ``` ### LangChain Support Since our API is compatible with OpenAI, you can easily use it in [langchain](https://www.langchain.com/). Here is an example: ``` from langchain_openai import ChatOpenAI llm = ChatOpenAI( model='deepseek-chat', openai_api_key=<your-deepseek-api-key>, openai_api_base='https://api.deepseek.com/v1', temperature=0.85, max_tokens=8000) ``` ## 9. License This code repository is licensed under [the MIT License](LICENSE-CODE). The use of DeepSeek-V2 Base/Chat models is subject to [the Model License](LICENSE-MODEL). DeepSeek-V2 series (including Base and Chat) supports commercial use. ## 10. Citation ``` @misc{deepseekv2, title={DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model}, author={DeepSeek-AI}, year={2024}, eprint={2405.04434}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## 11. 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":"10018","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"