MODEL PROFILE

Mini-InternVL-Chat-2B-V1-5

OpenGVLab · Mini-InternVL-Chat-2B-V1-5

We 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

Visual QAlicense:mit
Context windowNot disclosed
Input → OutputText、Image → Text
Model weightsNot verified
Profile updated2026/10/29 21:28:56
OVERVIEW

Model overview

We 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

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Frequently asked questions

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Sources & traceability

许可证:mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。