deepseek-ai/DeepSeek-R1-Distill-Qwen-7B Text Generation • Updated 2 days ago • 31.1k • 173 deepseek-ai/DeepSeek-R1-Distill-Qwen-32B

We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2.

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, unravel the mystery of AGI with curiosity. Answer the essential question with long-termism. 🎉 DeepSeek-R1 is now live and open source, rivaling OpenAI's Model o1.

2024年12月13日 · DeepSeek-VL2 demonstrates superior capabilities across various tasks, including but not limited to visual question answering, optical character recognition, document/table/chart understanding, and visual grounding.

2024-03-14: Demo for DeepSeek-VL-7B available on Hugging Face. Check out the gradio demo of DeepSeek-VL-7B at https://huggingface.co/spaces/deepseek-ai/DeepSeek-VL-7B. Experience its capabilities firsthand! 2024-03-13: Support DeepSeek-VL gradio demo.

2024年12月27日 · DeepSeek-V3：开源 FP8 权重，支持 SGLang、LMDeploy、TensorRT-LLM 等工具，用户可在不同硬件平台上高效运行。 DeepSeek-V3-Base：已在 Hugging Face 上开源，开发者可通过 API 或网页界面体验。

2024年12月29日 · DeepSeek V3 的生成速度从 20 TPS 提升至 60 TPS，处理多模态数据和长文本时表现优异。该模型已在 Hugging Face 上开源，方便开发者使用和集成。 DeepSeek V3 的主要功能. 自然语言查询处理：能理解和处理用户的自然语言查询，提供快速准确的回答。

2024年5月6日 · 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.

2024年12月10日 · 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. For model details, please visit DeepSeek-V2 page for more information.