Opengvlab
Models by this creator
✅
InternVL-Chat-V1-5
299
InternVL-Chat-V1-5 is a multimodal large language model (MLLM) developed by OpenGVLab. It integrates the capabilities of the InternViT-6B-448px-V1-5 vision encoder and the InternLM2-Chat-20B language model. This model supports up to 4K image resolution and demonstrates strong performance on a variety of multimodal tasks, approaching the capabilities of commercial models like GPT-4V and Gemini Pro. Model inputs and outputs InternVL-Chat-V1-5 is a text-to-text model that can take both image and text as inputs and generate text outputs. It supports dynamic image resolution up to 4K, with the ability to break images into tiles ranging from 1 to 40 of 448x448 pixels. Inputs Image**: The model can accept images of varying resolutions, up to 4K, which are dynamically processed into 1 to 40 tiles of 448x448 pixels. Text**: The model can accept text-based prompts or questions. Outputs Text**: The model generates text-based responses, which can include detailed image descriptions, answers to questions, or other relevant outputs. Capabilities InternVL-Chat-V1-5 demonstrates strong multimodal understanding and generation capabilities, particularly in tasks involving vision and language. It excels at image captioning, visual question answering, document understanding, and Chinese-related tasks. The model's performance approaches or exceeds that of commercial models like GPT-4V and Gemini Pro on benchmarks such as MMMU, DocVQA, ChartQA, and MathVista. What can I use it for? InternVL-Chat-V1-5 can be useful for a variety of applications that require integrating visual and textual information, such as: Content analysis and understanding**: The model can be used to extract, organize, and summarize information from images, documents, and other visual media. Multimodal chatbots and assistants**: The model can be integrated into conversational agents that can understand and respond to both text and image inputs. Image captioning and visual question answering**: The model can be used to generate detailed captions for images and answer questions about their contents. Multilingual multimodal tasks**: The model's bilingual support and strong performance on Chinese-related tasks make it suitable for applications involving multiple languages. Things to try One interesting aspect of InternVL-Chat-V1-5 is its ability to handle high-resolution images up to 4K. This can be particularly useful for tasks that require detailed visual analysis, such as document understanding or diagram recognition. Developers could experiment with feeding the model high-quality images and observe how it processes and responds to the increased visual information. Additionally, the model's strong performance on Chinese-related tasks, such as OCR and question answering, suggests it could be a valuable tool for applications targeting Chinese-speaking users. Researchers and developers could explore the model's capabilities in this domain and consider ways to leverage its bilingual support.
Updated 5/28/2024
🧠
InternVL-Chat-V1-5-Int8
56
InternVL-Chat-V1-5 is a powerful multimodal large language model (MLLM) created by OpenGVLab. It builds on the strong vision encoding capabilities of InternViT-6B-448px-V1-5 and the high-quality language generation of InternLM2-Chat-20B. The model supports dynamic high-resolution input up to 4K images and excels at multimodal tasks like document understanding, chart analysis, and math problem-solving. Compared to proprietary models like GPT-4V and Gemini Pro, InternVL-Chat-V1-5 approaches their performance on various benchmarks. Model inputs and outputs Inputs Images**: The model can handle dynamic image resolutions up to 4K, with the ability to process up to 40 tiles of 448x448 pixels. Text**: The model accepts natural language prompts and conversations. Outputs Multimodal understanding**: The model can answer questions, summarize information, and provide insights based on the given image and text inputs. Image-to-text generation**: The model can generate detailed textual descriptions of images. Multimodal dialogue**: The model can engage in interactive conversations, combining visual and language understanding to provide coherent and informed responses. Capabilities InternVL-Chat-V1-5 excels at a wide range of multimodal tasks, including document understanding, chart analysis, and math problem-solving. It can understand complex visual information and provide detailed, contextually-relevant responses. The model's strong OCR capabilities also allow it to handle challenging text extraction and analysis from images. What can I use it for? InternVL-Chat-V1-5 can be useful for a variety of applications that require multimodal understanding and generation, such as: Intelligent document processing**: Automating the understanding and analysis of complex documents, forms, and reports. Multimodal search and retrieval**: Enabling users to search for and retrieve relevant information using a combination of text and images. Assistive technology**: Providing multimodal assistance and guidance for tasks that involve both visual and textual information. Educational applications**: Supporting interactive learning experiences that leverage both visual and textual content. Things to try Explore the model's capabilities by posing questions or prompts that combine visual and textual information. For example, try asking the model to describe the details of a chart or diagram, or to provide insights based on a document image and a specific query. The model's dynamic high-resolution input and strong OCR abilities make it well-suited for tasks that require a deep understanding of both visual and textual data.
Updated 6/13/2024
🌀
Mini-InternVL-Chat-2B-V1-5
51
MiniInternVL-Chat-2B-V1-5 is a smaller version of the InternVL-Chat-V1-5 multimodal large language model (MLLM) developed by OpenGVLab. It was created by distilling the InternViT-6B-448px-V1-5 vision foundation model down to 300M parameters and using a smaller InternLM2-Chat-1.8B or Phi-3-mini-128k-instruct language model. This resulted in a powerful 2.2B parameter multimodal model that maintains excellent performance. Similar to the larger InternVL-Chat-V1-5 model, MiniInternVL-Chat-2B-V1-5 uses a dynamic high-resolution approach to process images, dividing them into tiles ranging from 1 to 40 of 448x448 pixels to support up to 4K resolution inputs. It was trained on the same high-quality bilingual dataset as the larger model, enhancing performance on OCR and Chinese-related tasks. Model inputs and outputs Inputs Images**: MiniInternVL-Chat-2B-V1-5 can accept dynamic resolution images up to 4K, with a maximum of 40 tiles of 448x448 pixels. Text**: The model can process textual inputs for multimodal understanding and generation tasks. Outputs Multimodal responses**: The model can generate coherent and contextual responses based on the provided image and text inputs, showcasing its strong multimodal understanding and generation capabilities. Insights and analysis**: MiniInternVL-Chat-2B-V1-5 can provide detailed descriptions, insights, and analysis of the input images and related information. Capabilities MiniInternVL-Chat-2B-V1-5 has demonstrated strong performance on a variety of multimodal tasks, including image captioning, visual question answering, and document understanding. It can excel at tasks that require a deep understanding of both visual and textual information, such as analyzing the contents of images, answering questions about them, and generating relevant responses. What can I use it for? With its compact size and powerful multimodal capabilities, MiniInternVL-Chat-2B-V1-5 is well-suited for a wide range of applications, including: Intelligent visual assistants**: The model can be integrated into interactive applications that can understand and respond to visual and textual inputs, making it a valuable tool for customer service, education, and other domains. Multimodal content generation**: The model can be used to generate high-quality multimodal content, such as image captions, visual stories, and multimedia presentations, which can be beneficial for content creators, publishers, and marketers. Multimodal data analysis**: The model's strong performance on tasks like document understanding and visual question answering makes it useful for analyzing and extracting insights from large, complex multimodal datasets, which can be valuable for businesses, researchers, and data analysts. Things to try One interesting aspect of MiniInternVL-Chat-2B-V1-5 is its ability to process high-resolution images at any aspect ratio. This can be particularly useful for applications that deal with a variety of image formats, as the model can effectively handle inputs ranging from low-resolution thumbnails to high-quality, high-resolution images. Developers can experiment with the model's multimodal capabilities by feeding it a diverse set of images and text prompts, and observing how it interprets and responds to the information. For example, you could try asking the model to describe the contents of an image, answer questions about it, or generate a short story or poem inspired by the visual and textual inputs. Another area to explore is the model's potential for fine-tuning and adaptation. By leveraging the provided InternVL 1.5 Technical Report and InternVL 1.0 Paper, researchers and developers can gain insights into the training strategies used to create the model, and potentially adapt it for specific domains or applications through further fine-tuning.
Updated 7/2/2024
🎲
Mini-InternVL-Chat-4B-V1-5
50
Mini-InternVL-Chat-4B-V1-5 is a multimodal large language model (MLLM) developed by OpenGVLab. It is part of the Mini-InternVL-Chat series, which aims to create smaller yet high-performing multimodal models. The model uses the InternViT-300M-448px vision model and either the InternLM2-Chat-1.8B or Phi-3-mini-128k-instruct language model, resulting in a 4.2B parameter model. This smaller model maintains excellent performance while reducing the computational requirements compared to the larger InternVL 1.5 model. Model inputs and outputs Inputs Images**: The model accepts dynamic resolution images, up to a maximum of 40 tiles of 448 x 448 pixels (4K resolution). Outputs Multimodal responses**: The model generates text responses based on the input image and any additional context provided. Capabilities Mini-InternVL-Chat-4B-V1-5 is capable of understanding and generating multimodal responses, combining visual and linguistic information. It can be used for a variety of tasks, such as image captioning, visual question answering, and multimodal dialog. What can I use it for? The Mini-InternVL-Chat-4B-V1-5 model can be used in a wide range of applications that require multimodal understanding and generation, such as: Interactive chatbots that can understand and respond to images Assistants that can provide detailed captions and explanations for images Visual question answering systems that can answer questions about the content of an image Things to try With Mini-InternVL-Chat-4B-V1-5, you can experiment with various multimodal tasks, such as: Generating creative image captions that go beyond simple descriptions Engaging in open-ended conversations about images, exploring the model's reasoning and understanding Combining the model's visual and language understanding to tackle complex multimodal tasks, such as visual reasoning or multimodal story generation.
Updated 7/2/2024
🎲
Mini-InternVL-Chat-4B-V1-5
50
Mini-InternVL-Chat-4B-V1-5 is a multimodal large language model (MLLM) developed by OpenGVLab. It is part of the Mini-InternVL-Chat series, which aims to create smaller yet high-performing multimodal models. The model uses the InternViT-300M-448px vision model and either the InternLM2-Chat-1.8B or Phi-3-mini-128k-instruct language model, resulting in a 4.2B parameter model. This smaller model maintains excellent performance while reducing the computational requirements compared to the larger InternVL 1.5 model. Model inputs and outputs Inputs Images**: The model accepts dynamic resolution images, up to a maximum of 40 tiles of 448 x 448 pixels (4K resolution). Outputs Multimodal responses**: The model generates text responses based on the input image and any additional context provided. Capabilities Mini-InternVL-Chat-4B-V1-5 is capable of understanding and generating multimodal responses, combining visual and linguistic information. It can be used for a variety of tasks, such as image captioning, visual question answering, and multimodal dialog. What can I use it for? The Mini-InternVL-Chat-4B-V1-5 model can be used in a wide range of applications that require multimodal understanding and generation, such as: Interactive chatbots that can understand and respond to images Assistants that can provide detailed captions and explanations for images Visual question answering systems that can answer questions about the content of an image Things to try With Mini-InternVL-Chat-4B-V1-5, you can experiment with various multimodal tasks, such as: Generating creative image captions that go beyond simple descriptions Engaging in open-ended conversations about images, exploring the model's reasoning and understanding Combining the model's visual and language understanding to tackle complex multimodal tasks, such as visual reasoning or multimodal story generation.
Updated 7/2/2024