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DeepSeek-V2-Lite-Chat

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Model parameter quantity
16B
Model parameter quantity
Affiliated organization
DeepSeek
Affiliated organization
Open Source
License Type
Release time
May 14, 2024
Release time
Model Introduction
DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model presented by DeepSeek, DeepSeek-V2-Lite is a lite version of it.
Language comprehension ability Language comprehension ability
Language comprehension ability
Often makes semantic misjudgments, leading to obvious logical disconnects in responses.
3.1
Knowledge coverage scope Knowledge coverage scope
Knowledge coverage scope
Has significant knowledge blind spots, often showing factual errors and repeating outdated information.
4.1
Reasoning ability Reasoning ability
Reasoning ability
Unable to maintain coherent reasoning chains, often causing inverted causality or miscalculations.
2.8
Related model
DeepSeek-V2-Chat-0628 DeepSeek-V2 is 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.
DeepSeek-V2.5 DeepSeek-V2.5 is an upgraded version that combines DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct. The new model integrates the general and coding abilities of the two previous versions.
DeepSeek-V3-0324 DeepSeek-V3 outperforms other open-source models such as Qwen2.5-72B and Llama-3.1-405B in multiple evaluations and matches the performance of top-tier closed-source models like GPT-4 and Claude-3.5-Sonnet.
DeepSeek-V2-Lite-Chat DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model presented by DeepSeek, DeepSeek-V2-Lite is a lite version of it.
DeepSeek-V2-Chat DeepSeek-V2 is 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.
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