deepseek-vl-1.3b-base
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deepseek-vl-1.3b-chat
DeepSeekIntroducing DeepSeek-VL, an open-source Vision-Language (VL) Model designed for real-world vision and language understanding applications. DeepSeek-VL possesses general multimodal understanding capabilities, capable of processing logical diagrams, web pages, formula recognition, scientific literature, natural images, and embodied intelligence in complex scenarios. Haoyu Lu*, Wen Liu*, Bo Zhang**, Bingxuan Wang, Kai Dong, Bo Liu, Jingxiang Sun, Tongzheng Ren, Zhuoshu Li, Hao Yang, Yaofeng Sun, Chengqi Deng, Hanwei Xu, Zhenda Xie, Chong Ruan (*Equal Contribution, **Project Lead) DeepSeek-VL-1.3b-chat is a tiny vision-language model. It uses the SigLIP-L as the vision encoder supporting 384 x 384 image input and is constructed based on the DeepSeek-LLM-1.3b-base which is trained on an approximate corpus of 500B text tokens. The whole DeepSeek-VL-1.3b-base model is finally trained around 400B vision-language tokens. The DeepSeek-VL-1.3b-chat is an instructed version based on DeepSeek-VL-1.3b-base.
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View details ↗deepseek-vl-7b-base
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View details ↗deepseek-vl-7b-chat
DeepSeekIntroducing DeepSeek-VL, an open-source Vision-Language (VL) Model designed for real-world vision and language understanding applications. DeepSeek-VL possesses general multimodal understanding capabilities, capable of processing logical diagrams, web pages, formula recognition, scientific literature, natural images, and embodied intelligence in complex scenarios. Haoyu Lu*, Wen Liu*, Bo Zhang**, Bingxuan Wang, Kai Dong, Bo Liu, Jingxiang Sun, Tongzheng Ren, Zhuoshu Li, Hao Yang, Yaofeng Sun, Chengqi Deng, Hanwei Xu, Zhenda Xie, Chong Ruan (*Equal Contribution, **Project Lead) DeepSeek-VL-7b-base uses the SigLIP-L and SAM-B as the hybrid vision encoder supporting 1024 x 1024 image input and is constructed based on the DeepSeek-LLM-7b-base which is trained on an approximate corpus of 2T text tokens. The whole DeepSeek-VL-7b-base model is finally trained around 400B vision-language tokens. DeekSeel-VL-7b-chat is an instructed version based on DeepSeek-VL-7b-base.
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View details ↗deepseek-vl2
DeepSeekIntroducing DeepSeek-VL2, an advanced series of large Mixture-of-Experts (MoE) Vision-Language Models that significantly improves upon its predecessor, DeepSeek-VL. 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. Our model series is composed of three variants: DeepSeek-VL2-Tiny, DeepSeek-VL2-Small and DeepSeek-VL2, with 1.0B, 2.8B and 4.5B activated parameters respectively. DeepSeek-VL2 achieves competitive or state-of-the-art performance with similar or fewer activated parameters compared to existing open-source dense and MoE-based models. Zhiyu Wu*, Xiaokang Chen*, Zizheng Pan*, Xingchao Liu*, Wen Liu**, Damai Dai, Huazuo Gao, Yiyang Ma, Chengyue Wu, Bingxuan Wang, Zhenda Xie, Yu Wu, Kai Hu, Jiawei Wang, Yaofeng Sun, Yukun Li, Yishi Piao, Kang Guan, Aixin Liu, Xin Xie, Yuxiang You, Kai Dong, Xingkai Yu, Haowei Zhang, Liang Zhao, Yisong Wang, Chong Ruan*** (* Equal Contribution, ** Project Lead, *** Corresponding author) On the basis of `Python >= 3.8` environment, install the necessary dependencies by running the following command:
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View details ↗deepseek-vl2-small
DeepSeekIntroducing DeepSeek-VL2, an advanced series of large Mixture-of-Experts (MoE) Vision-Language Models that significantly improves upon its predecessor, DeepSeek-VL. 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. Our model series is composed of three variants: DeepSeek-VL2-Tiny, DeepSeek-VL2-Small and DeepSeek-VL2, with 1.0B, 2.8B and 4.5B activated parameters respectively. DeepSeek-VL2 achieves competitive or state-of-the-art performance with similar or fewer activated parameters compared to existing open-source dense and MoE-based models. Zhiyu Wu*, Xiaokang Chen*, Zizheng Pan*, Xingchao Liu*, Wen Liu**, Damai Dai, Huazuo Gao, Yiyang Ma, Chengyue Wu, Bingxuan Wang, Zhenda Xie, Yu Wu, Kai Hu, Jiawei Wang, Yaofeng Sun, Yukun Li, Yishi Piao, Kang Guan, Aixin Liu, Xin Xie, Yuxiang You, Kai Dong, Xingkai Yu, Haowei Zhang, Liang Zhao, Yisong Wang, Chong Ruan*** (* Equal Contribution, ** Project Lead, *** Corresponding author) On the basis of `Python >= 3.8` environment, install the necessary dependencies by running the following command:
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View details ↗deepseek-vl2-tiny
DeepSeekIntroducing DeepSeek-VL2, an advanced series of large Mixture-of-Experts (MoE) Vision-Language Models that significantly improves upon its predecessor, DeepSeek-VL. 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. Our model series is composed of three variants: DeepSeek-VL2-Tiny, DeepSeek-VL2-Small and DeepSeek-VL2, with 1.0B, 2.8B and 4.5B activated parameters respectively. DeepSeek-VL2 achieves competitive or state-of-the-art performance with similar or fewer activated parameters compared to existing open-source dense and MoE-based models. Zhiyu Wu*, Xiaokang Chen*, Zizheng Pan*, Xingchao Liu*, Wen Liu**, Damai Dai, Huazuo Gao, Yiyang Ma, Chengyue Wu, Bingxuan Wang, Zhenda Xie, Yu Wu, Kai Hu, Jiawei Wang, Yaofeng Sun, Yukun Li, Yishi Piao, Kang Guan, Aixin Liu, Xin Xie, Yuxiang You, Kai Dong, Xingkai Yu, Haowei Zhang, Liang Zhao, Yisong Wang, Chong Ruan*** (* Equal Contribution, ** Project Lead, *** Corresponding author) DeepSeek-VL2-tiny is built on DeepSeekMoE-3B (total activated parameters are 1.0B).
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View details ↗dflash_gemma4_12b_block7
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View details ↗dflash_qwen3_14b_block7
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View details ↗dflash_qwen3_4b_block7
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View details ↗dflash_qwen3_8b_block7
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View details ↗dspark_gemma4_12b_block7
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View details ↗dspark_qwen3_14b_block7
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View details ↗dspark_qwen3_4b_block7
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View details ↗dspark_qwen3_8b_block7
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View details ↗eagle3_gemma4_12b_ttt7
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View details ↗eagle3_qwen3_14b_ttt7
DeepSeek基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗eagle3_qwen3_4b_ttt7
DeepSeek基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗eagle3_qwen3_8b_ttt7
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View details ↗ESFT-gate-code-lite
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View details ↗ESFT-gate-intent-lite
DeepSeek基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗ESFT-gate-law-lite
DeepSeek基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗ESFT-gate-math-lite
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View details ↗ESFT-gate-summary-lite
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