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README.md

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 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, 5.7T training tokens
-- Outperforms 7B dense and 16B MoE on many benchmarks
+- 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.