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*This model was published in HF papers on 2024-05-07 and contributed to Hugging Face Transformers on 2025-07-09.*

# DeepSeek-V2

## Overview

The DeepSeek-V2 model was proposed in [DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model](https://huggingface.co/papers/2405.04434) by DeepSeek-AI Team.

The abstract from the paper is the following:
We present 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, and supports a context length of 128K tokens. 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. Compared with DeepSeek 67B, DeepSeek-V2 achieves significantly 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 pretrain DeepSeek-V2 on a high-quality and multi-source corpus consisting of 8.1T tokens, and further perform Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) to fully unlock its potential. Evaluation results show that, even with only 21B activated parameters, DeepSeek-V2 and its chat versions still achieve top-tier performance among open-source models.

This model was contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber).
The original code can be found [here](https://huggingface.co/deepseek-ai/DeepSeek-V2).

### Usage tips

The model uses Multi-head Latent Attention (MLA) and DeepSeekMoE architectures for efficient inference and cost-effective training. It employs an auxiliary-loss-free strategy for load balancing and multi-token prediction training objective. The model can be used for various language tasks after being pre-trained on 14.8 trillion tokens and going through Supervised Fine-Tuning and Reinforcement Learning stages.

## DeepseekV2Config

[[autodoc]] DeepseekV2Config

## DeepseekV2Model

[[autodoc]] DeepseekV2Model
    - forward

## DeepseekV2ForCausalLM

[[autodoc]] DeepseekV2ForCausalLM
    - forward

## DeepseekV2ForSequenceClassification

[[autodoc]] DeepseekV2ForSequenceClassification
    - forward
