InternVL3-9B-AWQ
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InternVL3-9B-Instruct
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View details ↗InternVL3-9B-Pretrained
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View details ↗LCL-ViT-B-16-Laion
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View details ↗Mini-InternVL-Chat-2B-V1-5
OpenGVLabWe are delighted to introduce the Mini-InternVL-Chat series. In the era of large language models, many researchers have started to focus on smaller language models, such as Gemma-2B, Qwen-1.8B, and InternLM2-1.8B. Inspired by their efforts, we have distilled our vision foundation model InternViT-6B-448px-V1-5 down to 300M and used InternLM2-Chat-1.8B or Phi-3-mini-128k-instruct as our language model. This resulted in a small multimodal model with excellent performance. As shown in the figure below, we adopted the same model architecture as InternVL 1.5. We simply replaced the original InternViT-6B with InternViT-300M and InternLM2-Chat-20B with InternLM2-Chat-1.8B / Phi-3-mini-128k-instruct. For training, we used the same data as InternVL 1.5 to train this smaller model. Additionally, due to the lower training costs of smaller models, we used a context length of 8K during training. - Architecture: InternViT-300M-448px + MLP + InternLM2-Chat-1.8B - Image size: dynamic resolution, max to 40 tiles of 448 x 448 (4K resolution). - Params: 2.2B
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View details ↗Mini-InternVL-Chat-4B-V1-5
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View details ↗Mini-InternVL2-1B-DA-BDD
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View details ↗Mini-InternVL2-1B-DA-DriveLM
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View details ↗Mini-InternVL2-1B-DA-Medical
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View details ↗Mini-InternVL2-2B-DA-BDD
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View details ↗Mini-InternVL2-2B-DA-DriveLM
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View details ↗Mini-InternVL2-2B-DA-Medical
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View details ↗Mini-InternVL2-4B-DA-BDD
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View details ↗Mini-InternVL2-4B-DA-DriveLM
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View details ↗Mini-InternVL2-4B-DA-Medical
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View details ↗MM-Interleaved
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View details ↗Mono-InternVL-2B
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View details ↗Mono-InternVL-2B-S1-1
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View details ↗Mono-InternVL-2B-S1-2
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View details ↗Mono-InternVL-2B-S1-3
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View details ↗NaViL-2B
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View details ↗NaViL-9B
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View details ↗PATH-ViTB
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View details ↗PIIP
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