InternVL2-26B-AWQ
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InternVL2-2B
OpenGVLabWe 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 ability to handle these types of inputs compared to InternVL 1.5. For more details, please refer to our blog and GitHub.
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View details ↗InternVL2-2B-AWQ
OpenGVLab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 ↗InternVL2-40B
OpenGVLab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 ↗InternVL2-40B-AWQ
OpenGVLab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 ↗InternVL2-4B
OpenGVLabWe 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 InternVL2-4B 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 ability to handle these types of inputs compared to InternVL 1.5. For more details, please refer to our blog and GitHub.
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View details ↗InternVL2_5-1B
OpenGVLabWe are excited to introduce **InternVL 2.5**, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2.0, maintaining its core model architecture while introducing significant enhancements in training and testing strategies as well as data quality. As shown in the following figure, InternVL 2.5 retains the same model architecture as its predecessors, InternVL 1.5 and 2.0, following the "ViT-MLP-LLM" paradigm. In this new version, we integrate a newly incrementally pre-trained InternViT with various pre-trained LLMs, including InternLM 2.5 and Qwen 2.5, using a randomly initialized MLP projector. As in the previous version, we applied a pixel unshuffle operation, reducing the number of visual tokens to one-quarter of the original. Besides, we adopted a similar dynamic resolution strategy as InternVL 1.5, dividing images into tiles of 448×448 pixels. The key difference, starting from InternVL 2.0, is that we additionally introduced support for multi-image and video data.
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View details ↗InternVL2_5-1B-MPO
OpenGVLab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 ↗InternVL2_5-26B
OpenGVLab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 ↗InternVL2_5-26B-AWQ
OpenGVLab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 ↗InternVL2_5-26B-MPO
OpenGVLab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 ↗InternVL2_5-26B-MPO-AWQ
OpenGVLab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 ↗InternVL2_5-2B
OpenGVLabWe are excited to introduce **InternVL 2.5**, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2.0, maintaining its core model architecture while introducing significant enhancements in training and testing strategies as well as data quality. As shown in the following figure, InternVL 2.5 retains the same model architecture as its predecessors, InternVL 1.5 and 2.0, following the "ViT-MLP-LLM" paradigm. In this new version, we integrate a newly incrementally pre-trained InternViT with various pre-trained LLMs, including InternLM 2.5 and Qwen 2.5, using a randomly initialized MLP projector. As in the previous version, we applied a pixel unshuffle operation, reducing the number of visual tokens to one-quarter of the original. Besides, we adopted a similar dynamic resolution strategy as InternVL 1.5, dividing images into tiles of 448×448 pixels. The key difference, starting from InternVL 2.0, is that we additionally introduced support for multi-image and video data.
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View details ↗InternVL2_5-2B-MPO
OpenGVLab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 ↗InternVL2_5-2B-MPO-hf
OpenGVLab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 ↗InternVL2_5-38B
OpenGVLabWe are excited to introduce **InternVL 2.5**, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2.0, maintaining its core model architecture while introducing significant enhancements in training and testing strategies as well as data quality. As shown in the following figure, InternVL 2.5 retains the same model architecture as its predecessors, InternVL 1.5 and 2.0, following the "ViT-MLP-LLM" paradigm. In this new version, we integrate a newly incrementally pre-trained InternViT with various pre-trained LLMs, including InternLM 2.5 and Qwen 2.5, using a randomly initialized MLP projector. As in the previous version, we applied a pixel unshuffle operation, reducing the number of visual tokens to one-quarter of the original. Besides, we adopted a similar dynamic resolution strategy as InternVL 1.5, dividing images into tiles of 448×448 pixels. The key difference, starting from InternVL 2.0, is that we additionally introduced support for multi-image and video data.
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View details ↗InternVL2_5-38B-AWQ
OpenGVLab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 ↗InternVL2_5-38B-MPO
OpenGVLab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 ↗InternVL2_5-38B-MPO-AWQ
OpenGVLab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 ↗InternVL2_5-4B
OpenGVLabWe are excited to introduce **InternVL 2.5**, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2.0, maintaining its core model architecture while introducing significant enhancements in training and testing strategies as well as data quality. As shown in the following figure, InternVL 2.5 retains the same model architecture as its predecessors, InternVL 1.5 and 2.0, following the "ViT-MLP-LLM" paradigm. In this new version, we integrate a newly incrementally pre-trained InternViT with various pre-trained LLMs, including InternLM 2.5 and Qwen 2.5, using a randomly initialized MLP projector. As in the previous version, we applied a pixel unshuffle operation, reducing the number of visual tokens to one-quarter of the original. Besides, we adopted a similar dynamic resolution strategy as InternVL 1.5, dividing images into tiles of 448×448 pixels. The key difference, starting from InternVL 2.0, is that we additionally introduced support for multi-image and video data.
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View details ↗InternVL2_5-4B-AWQ
OpenGVLab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 ↗InternVL2_5-4B-MPO
OpenGVLabWe introduce InternVL2.5-MPO, an advanced multimodal large language model (MLLM) series that demonstrates superior overall performance. This series builds upon InternVL2.5 and Mixed Preference Optimization. As shown in the following figure, InternVL2.5-MPO retains the same model architecture as InternVL 2.5 and its predecessors, InternVL 1.5 and 2.0, following the "ViT-MLP-LLM" paradigm. In this new version, we integrate a newly incrementally pre-trained InternViT with various pre-trained LLMs, including InternLM 2.5 and Qwen 2.5, using a randomly initialized MLP projector. As in the previous version, we applied a pixel unshuffle operation, reducing the number of visual tokens to one-quarter of the original. Besides, we adopted a similar dynamic resolution strategy as InternVL 1.5, dividing images into tiles of 448×448 pixels. The key difference, starting from InternVL 2.0, is that we additionally introduced support for multi-image and video data.
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View details ↗InternVL2_5-4B-MPO-AWQ
OpenGVLab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 ↗InternVL2_5-78B
OpenGVLab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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