Paper Link👁️

# DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model ## 1. Introduction Last week, the release and buzz around DeepSeek-V2 have ignited widespread interest in MLA (Multi-head Latent Attention)! Many in the community suggested open-sourcing a smaller MoE model for in-depth research. And now DeepSeek-V2-Lite comes out: - 16B total params, 2.4B active params, scratch training with 5.7T tokens - Outperforms 7B dense and 16B MoE on many English & Chinese benchmarks - Deployable on single 40G GPU, fine-tunable on 8x80G GPUs DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. DeepSeek-V2 adopts innovative architectures including Multi-head Latent Attention (MLA) and DeepSeekMoE. MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation. ## 2. News - 2024.05.16: We released the DeepSeek-V2-Lite. - 2024.05.06: We released the DeepSeek-V2. ## 3. Model Downloads With DeepSeek-V2, we are open-sourcing base and chat models across two sizes:

| **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) |

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

| **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 |

For more evaluation details, such as few-shot settings and prompts, please check our paper. ### Chat Model #### Standard Benchmark

| 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 |

## 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.

## 6. How to run locally **To utilize DeepSeek-V2-Lite in BF16 format for inference, 40GB*1 GPU is 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-Lite" tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda() 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-Lite-Chat" tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda() 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, 1 model_name = "deepseek-ai/DeepSeek-V2-Lite-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=, openai_api_base='https://api.deepseek.com/v1', temperature=0.85, max_tokens=8000) ``` ## 7. 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. ## 8. 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} } ``` ## 9. Contact If you have any questions, please raise an issue or contact us at [service@deepseek.com](service@deepseek.com).