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--- |
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license: apache-2.0 |
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metrics: |
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- accuracy |
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--- |
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<!-- markdownlint-disable first-line-h1 --> |
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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 LLM" /> |
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</div> |
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<hr> |
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<div align="center"> |
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<a href="https://www.deepseek.com/" target="_blank"> |
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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"> |
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<a href="https://huggingface.co/deepseek-ai" target="_blank"> |
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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?raw=true" style="display: inline-block; vertical-align: middle;"/> |
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<a href="LICENSE-CODE"> |
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<a href="LICENSE-MODEL"> |
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<img alt="Model License" src="https://img.shields.io/badge/Model_License-Model_Agreement-f5de53?&color=f5de53?raw=true"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="#4-model-architecture">Model Architecture</a> | |
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<a href="#6-api-platform">API Platform</a> | |
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<a href="#8-license">License</a> | |
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<a href="#9-citation">Citation</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-V2/blob/main/deepseek-v2-tech-report.pdf"><b>Paper Link</b>👁️</a> |
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</p> |
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# DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model |
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## 1. Introduction |
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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. |
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<p align="center"> |
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<div style="display: flex; justify-content: center;"> |
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<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/activationparameters.png?raw=true" style="height:300px; width:auto; margin-right:10px"> |
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<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/trainingcost.png?raw=true" style="height:300px; width:auto; margin-left:10px"> |
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</div> |
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</p> |
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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. |
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## 2. Model Downloads |
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<div align="center"> |
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| **Model** | **Context Length** | **Download** | |
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| :------------: | :------------: | :------------: | |
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| DeepSeek-V2 | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V2) | |
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| DeepSeek-V2-Chat (RL) | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V2-Chat) | |
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</div> |
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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. |
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## 3. Evaluation Results |
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### Base Model |
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#### Standard Benchmark |
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<div align="center"> |
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| **Benchmark** | **Domain** | **LLaMA3 70B** | **Mixtral 8x22B** | **DeepSeek-V1 (Dense-67B)** | **DeepSeek-V2 (MoE-236B)** | |
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|:-----------:|:--------:|:------------:|:---------------:|:-------------------------:|:------------------------:| |
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| **MMLU** | English | 78.9 | 77.6 | 71.3 | 78.5 | |
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| **BBH** | English | 81.0 | 78.9 | 68.7 | 78.9 | |
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| **C-Eval** | Chinese | 67.5 | 58.6 | 66.1 | 81.7 | |
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| **CMMLU** | Chinese | 69.3 | 60.0 | 70.8 | 84.0 | |
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| **HumanEval** | Code | 48.2 | 53.1 | 45.1 | 48.8 | |
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| **MBPP** | Code | 68.6 | 64.2 | 57.4 | 66.6 | |
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| **GSM8K** | Math | 83.0 | 80.3 | 63.4 | 79.2 | |
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| **Math** | Math | 42.2 | 42.5 | 18.7 | 43.6 | |
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</div> |
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For more evaluation details, such as few-shot settings and prompts, please check our paper. |
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#### Context Window |
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<p align="center"> |
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<img width="80%" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/niah.png?raw=true"> |
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</p> |
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Evaluation results on the ``Needle In A Haystack`` (NIAH) tests. DeepSeek-V2 performs well across all context window lengths up to **128K**. |
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### Chat Model |
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#### Standard Benchmark |
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<div align="center"> |
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| Benchmark | Domain | QWen1.5 72B Chat | Mixtral 8x22B | LLaMA3 70B Instruct | DeepSeek-V1 Chat (SFT) | DeepSeek-V2 Chat (SFT) | DeepSeek-V2 Chat (RL) | |
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|:-----------:|:----------------:|:------------------:|:---------------:|:---------------------:|:-------------:|:-----------------------:|:----------------------:| |
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| **MMLU** | English | 76.2 | 77.8 | 80.3 | 71.1 | 78.4 | 77.8 | |
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| **BBH** | English | 65.9 | 78.4 | 80.1 | 71.7 | 81.3 | 79.7 | |
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| **C-Eval** | Chinese | 82.2 | 60.0 | 67.9 | 65.2 | 80.9 | 78.0 | |
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| **CMMLU** | Chinese | 82.9 | 61.0 | 70.7 | 67.8 | 82.4 | 81.6 | |
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| **HumanEval** | Code | 68.9 | 75.0 | 76.2 | 73.8 | 76.8 | 81.1 | |
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| **MBPP** | Code | 52.2 | 64.4 | 69.8 | 61.4 | 70.4 | 72.0 | |
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| **LiveCodeBench (0901-0401)** | Code | 18.8 | 25.0 | 30.5 | 18.3 | 28.7 | 32.5 | |
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| **GSM8K** | Math | 81.9 | 87.9 | 93.2 | 84.1 | 90.8 | 92.2 | |
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| **Math** | Math | 40.6 | 49.8 | 48.5 | 32.6 | 52.7 | 53.9 | |
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</div> |
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#### English Open Ended Generation Evaluation |
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We evaluate our model on AlpacaEval 2.0 and MTBench, showing the competitive performance of DeepSeek-V2-Chat-RL on English conversation generation. |
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<p align="center"> |
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<img width="50%" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/mtbench.png?raw=true" /> |
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</p> |
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#### Chinese Open Ended Generation Evaluation |
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**Alignbench** (https://arxiv.org/abs/2311.18743) |
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<div align="center"> |
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| **模型** | **开源/闭源** | **总分** | **中文推理** | **中文语言** | |
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| :---: | :---: | :---: | :---: | :---: | |
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| gpt-4-1106-preview | 闭源 | 8.01 | 7.73 | 8.29 | |
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| DeepSeek-V2 Chat (RL) | 开源 | 7.91 | 7.45 | 8.35 | |
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| erniebot-4.0-202404 (文心一言) | 闭源 | 7.89 | 7.61 | 8.17 | |
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| DeepSeek-V2 Chat (SFT) | 开源 | 7.74 | 7.30 | 8.17 | |
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| gpt-4-0613 | 闭源 | 7.53 | 7.47 | 7.59 | |
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| erniebot-4.0-202312 (文心一言) | 闭源 | 7.36 | 6.84 | 7.88 | |
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| moonshot-v1-32k-202404 (月之暗面) | 闭源 | 7.22 | 6.42 | 8.02 | |
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| Qwen1.5-72B-Chat (通义千问) | 开源 | 7.19 | 6.45 | 7.93 | |
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| DeepSeek-67B-Chat | 开源 | 6.43 | 5.75 | 7.11 | |
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| Yi-34B-Chat (零一万物) | 开源 | 6.12 | 4.86 | 7.38 | |
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| gpt-3.5-turbo-0613 | 闭源 | 6.08 | 5.35 | 6.71 | |
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</div> |
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#### Coding Benchmarks |
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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. |
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<p align="center"> |
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<img width="50%" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/code_benchmarks.png?raw=true"> |
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</p> |
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## 4. Model Architecture |
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DeepSeek-V2 adopts innovative architectures to guarantee economical training and efficient inference： |
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- 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. |
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- For Feed-Forward Networks (FFNs), we adopt DeepSeekMoE architecture, a high-performance MoE architecture that enables training stronger models at lower costs. |
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<p align="center"> |
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<img width="90%" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/architecture.png?raw=true" /> |
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</p> |
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## 5. Chat Website |
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You can chat with the DeepSeek-V2 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com/sign_in) |
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## 6. 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-V2/blob/main/figures/model_price.png?raw=true"> |
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</p> |
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## 7. How to run locally |
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**To utilize DeepSeek-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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#### Text Completion |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig |
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model_name = "deepseek-ai/DeepSeek-V2" |
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) |
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# `max_memory` should be set based on your devices |
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max_memory = {i: "75GB" for i in range(8)} |
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# `device_map` cannot be set to `auto` |
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model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, device_map="sequential", torch_dtype=torch.bfloat16, max_memory=max_memory, attn_implementation="eager") |
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model.generation_config = GenerationConfig.from_pretrained(model_name) |
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model.generation_config.pad_token_id = model.generation_config.eos_token_id |
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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" |
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inputs = tokenizer(text, return_tensors="pt") |
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outputs = model.generate(**inputs.to(model.device), max_new_tokens=100) |
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result = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(result) |
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``` |
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#### Chat Completion |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig |
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model_name = "deepseek-ai/DeepSeek-V2-Chat" |
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) |
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# `max_memory` should be set based on your devices |
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max_memory = {i: "75GB" for i in range(8)} |
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# `device_map` cannot be set to `auto` |
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model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, device_map="sequential", torch_dtype=torch.bfloat16, max_memory=max_memory, attn_implementation="eager") |
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model.generation_config = GenerationConfig.from_pretrained(model_name) |
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model.generation_config.pad_token_id = model.generation_config.eos_token_id |
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messages = [ |
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{"role": "user", "content": "Write a piece of quicksort code in C++"} |
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] |
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input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt") |
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outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100) |
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result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True) |
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print(result) |
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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, 8 |
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model_name = "deepseek-ai/DeepSeek-V2-Chat" |
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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": "Translate the following content into Chinese directly: DeepSeek-V2 adopts innovative architectures to guarantee economical training and efficient inference."}], |
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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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## 8. License |
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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. |
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## 9. Citation |
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``` |
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@misc{deepseekv2, |
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title={DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model}, |
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author={DeepSeek-AI}, |
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year={2024}, |
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eprint={2405.04434}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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## 10. Contact |
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If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]). |
