Paper Link👁️

# 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.

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. Model Downloads

| **Model** | **Context Length** | **Download** | | :------------: | :------------: | :------------: | | DeepSeek-V2 | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V2) | | DeepSeek-V2-Chat(RL) | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V2-Chat) |

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. ## 3. Evaluation Results ### Base Model #### Standard Benchmark

| **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 |

For more evaluation details, such as few-shot settings and prompts, please check our paper. #### Context Window

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

| 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 |

#### 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.

#### Chinese Open Ended Generation Evaluation **Alignbench** (https://arxiv.org/abs/2311.18743)

| **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 | 8.35 | | 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 |

#### 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.

## 4. 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.

## 5. Chat Website You can chat with the DeepSeek-V2 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com/sign_in) ## 6. 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.

## 7. 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 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) ``` ## 8. 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. ## 9. Citation ``` @misc{deepseek-v2, author = {DeepSeek-AI}, title = {DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model}, year = {2024}, note = {GitHub repository}, url = {https://github.com/deepseek-ai/deepseek-v2} } ``` ## 10. Contact If you have any questions, please raise an issue or contact us at [service@deepseek.com](service@deepseek.com).