InternLM團隊發佈開源輕量級多模態推理模型Intern-S1-mini。該模型僅8B參數，融合Qwen3-8B語言模型與0.3B視覺編碼器InternViT，具備強大處理能力。經過超5萬億token數據預訓練，其中2.5萬億來自化學、物理等科學領域，使其在科學任務中表現優異。
基於InternViT-300M-448px的增強版本，提升視覺特徵提取能力。
基於InternViT-6B-448px-V1-5的增強版視覺模型
Baidu
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32
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gwkrsrch2
這是一個發佈在Hugging Face模型中心的Transformer模型，模型卡片為自動生成。由於缺乏具體信息，無法提供詳細的模型介紹。
FriendliAI
InternVL3-1B是InternVL3系列中的1B參數規模多模態大語言模型，整合了InternViT視覺編碼器和Qwen2.5語言模型，具備卓越的多模態感知和推理能力。
OpenGVLab
InternViT-6B-448px-V2_5是基於InternViT-6B-448px-V1-5的重大升級版本，通過ViT增量學習與NTP損失提升了視覺特徵提取能力，特別擅長處理多語言OCR數據和數學圖表等複雜場景。
InternViT-300M-448px-V2_5是基於InternViT-300M-448px的重大升級版本，通過ViT增量學習和NTP損失提升了視覺特徵提取能力，特別擅長處理多語言OCR數據和數學圖表等複雜場景。
timm
InternViT-300M是一個由OpenGVLab團隊開發的視覺Transformer模型，通過從InternViT-6B蒸餾預訓練而來，支持多種視覺任務。
InternViT-300M-448px是一個高效的視覺基礎模型，通過從InternViT-6B-448px-V1-5蒸餾知識開發而來，具備448×448的動態輸入分辨率，支持1至40個圖塊處理。
InternViT-6B-448px-V1-5是基於InternViT-6B-448px-V1-2微調的視覺基礎模型，具備強大的魯棒性、OCR能力及高分辨率處理能力。
InternViT-6B-448px-V1-2是一個視覺基礎模型，特徵骨幹，具有5540萬參數，支持448x448像素的圖像處理。
InternViT-6B-448px-V1-0是一個視覺基礎模型，專注於圖像特徵提取，支持448x448分辨率，增強了OCR能力和中文對話支持。
InternViT-6B-224px 是一個視覺基礎模型，專注於圖像特徵提取，具有5903百萬參數，支持224x224像素的圖像輸入。
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