Mini-InternVL-Chat-2B-V1-5
Maintainer: OpenGVLab
50
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| Property | Value |
|---|---|
| Model Link | View on HuggingFace |
| API Spec | View on HuggingFace |
| Github Link | No Github link provided |
| Paper Link | No paper link provided |
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Model overview
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-V1-5can 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-V1-5can 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.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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