Introduction to DeepSeek-V2: A Strong, Cost-Effective Mixture-of-Experts Language Model
Introduction
DeepSeek-V2 is an advanced Mixture-of-Experts (MoE) language model designed to enhance performance while minimizing training and inference costs. The model is built with 236 billion total parameters, of which 21 billion are activated per token. Compared to its predecessor, DeepSeek 67B, DeepSeek-V2 not only delivers improved performance but also reduces training expenses by 42.5%, decreases the key-value (KV) cache requirement by 93.3%, and amplifies the maximum generation throughput to 5.76 times. The model was pretrained using a vast, high-quality corpus of 8.1 trillion tokens and further refined through Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL).
News
- May 16, 2024: Release of DeepSeek-V2-Lite.
- May 6, 2024: Launch of DeepSeek-V2.
Model Downloads
DeepSeek-V2 and its variations are available for download on HuggingFace. Different versions of the model are tailored to specific contexts, ensuring a wide range of applications:
- DeepSeek-V2-Lite: 16B Total Params, 2.4B Activated Params, Context Length: 32k
- DeepSeek-V2-Lite-Chat (SFT): Similar configuration with a focus on chat functionality
- DeepSeek-V2: 236B Total Params, 21B Activated Params, Context Length: 128k
- DeepSeek-V2-Chat (RL): Configured for chat with reinforcement learning enhancements
HuggingFace distribution currently has some performance limitations when used on GPUs.
Evaluation Results
Base Model Benchmarks
DeepSeek-V2 has demonstrated exceptional performance across various benchmarks, including English and Chinese language tasks, code generation, and mathematical problem-solving. The results indicate that DeepSeek-V2 surpasses other models, particularly in Chinese language tasks and coding challenges.
For smaller models, DeepSeek-V2-Lite significantly outperforms previous iterations and other similar-sized models.
Chat Model Benchmarks
In conversational tasks, especially those involving English and Chinese language dialogues, DeepSeek-V2-Chat has shown competitive performance. The model excels in standardized benchmarks and open-ended generation evaluations.
Coding and Math Benchmarks
DeepSeek-V2 showcases impressive performance in live coding and mathematical reasoning, illustrating its proficiency in these domains.
Model Architecture
DeepSeek-V2 incorporates advanced architectural techniques to ensure cost-effective training and efficient inference. It utilizes Multi-head Latent Attention (MLA) to address inference-time key-value cache bottlenecks and employs a high-performance Mixture-of-Experts architecture in its Feed-Forward Networks (FFNs).
Chat Website
DeepSeek-V2 is accessible for interactive use on the official DeepSeek chat platform, allowing users to engage with the model directly.
API Platform
DeepSeek offers an OpenAI-compatible API, providing users with access to millions of free tokens and a highly competitive pricing model for expanded use.
How to Run Locally
To run DeepSeek-V2 locally in the BF16 format, 80GB*8 GPUs are necessary. The model is compatible with Huggingface's Transformers for easy integration and inference for both text and chat completions.
In summary, DeepSeek-V2 represents a significant leap forward in the development of efficient and powerful language models, offering a versatile array of capabilities across various linguistic and problem-solving benchmarks.