OpenGVLab/InternVL2-2B
image-text-to-text model by OpenGVLab
From the publisher
Model card & documentation
Source previewRead the publisher’s intended use, setup instructions, evaluations and limitations. The original model card is the source of truth.
[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL) [\[📜 InternVL 1.0\]](https://huggingface.co/papers/2312.14238) [\[📜 InternVL 1.5\]](https://huggingface.co/papers/2404.16821) [\[📜 Mini-InternVL\]](https://arxiv.org/abs/2410.16261) [\[📜 InternVL 2.5\]](https://huggingface.co/papers/2412.05271) [\[🆕 Blog\]](https://internvl.github.io/blog/) [\[🗨️ Chat Demo\]](https://internvl.opengvlab.com/) [\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL) [\[🚀 Quick Start\]](#quick-start) [\[📖 Documents\]](https://internvl.readthedocs.io/en/latest/) We are excited to announce the release of InternVL 2.0, the latest addition to the InternVL series of multimodal large language models. InternVL 2.0 features a variety of instruction-tuned models, ranging from 1 billion to 108 billion parameters. This repository contains the instruction-tuned InternVL2-2B model. Compared to the state-of-the-art open-source multimodal large language models, InternVL 2.0 surpasses most open-source models. It demonstrates competitive performance on par with proprietary commercial models across various capabilities, including document and chart comprehension, infographics QA, scene text understanding and OCR tasks, scientific and mathematical problem solving, as well as cultural understanding and integrated multimodal capabilities. InternVL 2.0 is trained with an 8k context window and utilizes training data consisting of long texts, multiple images, and videos, significantly improving its…
Read the full model card ↗ · Preview checked 2026-09-21T06:56:40.003Z
Inside the original model card — Document outline
- InternVL2-2B
- Introduction
- Model Details
- Performance
- Image Benchmarks
- Video Benchmarks
- Grounding Benchmarks
- Quick Start
- Model Loading
- 16-bit (bf16 / fp16)
- BNB 8-bit Quantization
- BNB 4-bit Quantization
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Documentation belongs to its respective authors. Reported project/model license: mit. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
Model overview
OpenGVLab/InternVL2-2B is a image-text-to-text model repository published by OpenGVLab on Hugging Face. The source reports the transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- image-text-to-text
- Library
- transformers
- Recent downloads (30 days)
- 653,784
- Cumulative likes
- 82
- Hugging Face trending score
- 1
- Reported safetensors parameters
- 2,205,754,368
- Architecture
- InternVLChatModel
- License
- mit
- Access
- Not gated by Hugging Face
- Created
- 2024-06-27T09:59:37.000Z
- Last modified
- 2025-03-25T05:55:24.000Z
Compatibility and lineage
Reported languages: multilingual
Reported base models: OpenGVLab/InternViT-300M-448px, internlm/internlm2-chat-1_8b
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-09-21T06:36:49.586Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.