InternVideo2-Stage2-6B-Audio
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InternViT-300M-448px
OpenGVLabThis update primarily focuses on enhancing the efficiency of the vision foundation model. We developed InternViT-300M-448px by distilling knowledge from the robust vision foundation model, InternViT-6B-448px-V1-5. Like its predecessor, InternViT-300M-448px features a dynamic input resolution of 448×448, with a basic tile size of 448×448. During training, it allows for 1 to 12 tiles, and expands to 1 to 40 tiles during testing. Additionally, it inherits the powerful robustness, OCR capability, and high-resolution processing capacity from InternViT-6B-448px-V1-5. > \[!Warning\] > 🚨 Note: In our experience, the InternViT V2.5 series is better suited for building MLLMs than traditional computer vision tasks.
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View details ↗InternViT-300M-448px-V2_5
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View details ↗InternViT-6B-224px
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View details ↗InternViT-6B-448px-V1-0
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View details ↗InternViT-6B-448px-V1-2
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View details ↗InternViT-6B-448px-V1-5
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View details ↗InternViT-6B-448px-V2_5
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View details ↗InternVL
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View details ↗InternVL-14B-224px
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View details ↗InternVL-14B-Flickr30K-FT-364px
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View details ↗InternVL-14B-FlickrCN-FT-364px
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View details ↗InternVL_2_5_HiCo_R16
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View details ↗InternVL_2_5_HiCo_R64
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View details ↗InternVL-Chat-V1-1
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View details ↗InternVL-Chat-V1-2
OpenGVLabWe are excited to introduce 🤗 InternVL-Chat-V1-2. Inspired by LLaVA-NeXT-34B, we have also adopted Nous-Hermes-2-Yi-34B as the language model. Below is the pipeline. From the experimental results, we've observed that **a stronger language model (34B) can better leverage the powerful capabilities of our vision foundation model.** For better training reproducibility, we follow the minimalist design and data efficiency similar to LLaVA-NeXT. To reduce training costs, we provide a pre-trained MLP projector and only employ around 1.2 million visual instruction tuning samples for SFT. Our model has a total of 40 billion parameters and can be trained within 1.5 days using 32 A100 GPUs. The code, data, and model have been made publicly available.
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View details ↗InternVL-Chat-V1-2-Plus
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View details ↗InternVL-Chat-V1-5
OpenGVLabWe introduce InternVL 1.5, an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. 1. **Strong Vision Encoder:** we explored a continuous learning strategy for the large-scale vision foundation model---InternViT-6B, boosting its visual understanding capabilities, and making it can be transferred and reused in different LLMs. 2. **Dynamic High-Resolution:** we divide images into tiles ranging from 1 to 40 of 448 × 448 pixels according to the aspect ratio and resolution of the input images, which supports up to 4K resolution input during inference. 3. **High-Quality Bilingual Dataset:** we carefully collected a high-quality bilingual dataset that covers common scenes, document images, and annotated them with English and Chinese question-answer pairs, significantly enhancing performance in OCR- and Chinese-related tasks. Limitations: Although we have made efforts to ensure the safety of the model during the training process and to encourage the model to generate text that complies with ethical and legal requirements, the model may still produce unexpected outputs due to its size and probabilistic generation paradigm. For example, the generated responses may contain biases, discrimination, or other harmful content. Please do not propagate such content. We are not responsible for any consequences resulting from the dissemination of harmful information.
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View details ↗InternVL-Chat-V1-5-AWQ
OpenGVLabLMDeploy adopts AWQ algorithm for 4bit weight-only quantization. By developed the high-performance cuda kernel, the 4bit quantized model inference achieves up to 2.4x faster than FP16. LMDeploy's `api_server` enables models to be easily packed into services with a single command. The provided RESTful APIs are compatible with OpenAI's interfaces. Below are an example of service startup: This project is released under the MIT License. This project uses the pre-trained internlm2-chat-20b as a component, which is licensed under the Apache License 2.0.
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View details ↗InternVL-Chat-ViT-6B-Vicuna-13B
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View details ↗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 ↗InternVL-Chat-ViT-6B-Vicuna-7B
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View details ↗InternVL2-1B
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 InternVL2-1B 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 ↗InternVL2-26B
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