internlm-xcomposer2d5-7b
Maintainer: internlm
165
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| Property | Value |
|---|---|
| Run this model | Run 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
internlm-xcomposer2d5-7b is a powerful text-image comprehension and composition model developed by internlm. It is based on the InternLM2 language model and excels at a variety of multimodal tasks, achieving GPT-4 level capabilities with just a 7B parameter LLM backbone.
The model is trained on 24,000 interleaved image-text contexts and can seamlessly extend to 96,000 long contexts via RoPE extrapolation. This long-context capability allows internlm-xcomposer2d5-7b to excel at tasks requiring extensive input and output contexts, such as detailed video understanding and complex image description.
Similar models developed by the internlm team include the internlm-xcomposer2-vl-7b, a vision-language large model (VLLM) for advanced text-image comprehension and composition, and the internlm-xcomposer2-4khd-7b, a VLLM with 4K resolution image understanding capabilities.
Model inputs and outputs
Inputs
- Text query: The text prompt describing the task or request, such as "Describe this video in detail."
- Image(s): The image(s) to be processed and understood in the context of the text query.
Outputs
- Detailed response: A long-form, coherent text response describing the image(s) in detail, tailored to the provided text query.
Capabilities
internlm-xcomposer2d5-7b excels at a variety of text-image understanding and generation tasks. For example, it can provide detailed video summaries, as demonstrated in the quickstart example, where it generates a comprehensive description of a video featuring an athlete competing in the Olympics. The model's long-context capability allows it to maintain coherence and focus over lengthy inputs and outputs.
What can I use it for?
internlm-xcomposer2d5-7b can be leveraged for a wide range of applications that require deep understanding and generation of text-image content. Some potential use cases include:
- Content creation: Generating detailed descriptions, captions, or stories to accompany images and videos for use in marketing, social media, or editorial content.
- Visual question answering: Answering complex questions about the contents and details of images.
- Multimodal assistants: Building AI assistants that can understand and respond to queries involving both text and visual information.
- Artistic and creative applications: Assisting with the ideation and description of conceptual artwork or illustrations.
Things to try
One interesting aspect of internlm-xcomposer2d5-7b is its ability to engage in multi-turn, context-aware conversations about visual content. The quickstart example demonstrates how the model can provide an initial detailed description of an image, and then generate further explanations in response to follow-up queries about specific details. Exploring this interactive, iterative process of understanding and describing visual information could lead to fascinating applications.
Another key feature of the model is its long-context capability, which allows it to maintain coherence and focus over lengthy inputs and outputs. Experimenting with prompts that involve extensive background information or complex, multi-part queries could uncover the full extent of this capability and unlock new use cases.
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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internlm-xcomposer2-4khd-7b is a general vision-language large model (VLLM) based on InternLM2, with the capability of 4K resolution image understanding. It was created by internlm, who has also released similar models like internlm-xcomposer2-vl-7b, internlm-xcomposer, and internlm-7b. Model inputs and outputs internlm-xcomposer2-4khd-7b is a vision-language model that can take images and text as input, and generate relevant text as output. The model is capable of understanding and describing images in high resolution (4K) detail. Inputs Images**: The model can take 4K resolution images as input. Text**: The model can also accept text prompts or questions related to the input image. Outputs Descriptive text**: The model can generate detailed text descriptions that explain the contents and fine details of the input image. Capabilities The internlm-xcomposer2-4khd-7b model excels at understanding and describing 4K resolution images. It can analyze the visual elements of an image in depth, and provide nuanced, coherent text descriptions that capture the key details and insights. This makes the model useful for applications that require high-quality image captioning or visual question answering. What can I use it for? The internlm-xcomposer2-4khd-7b model could be useful for a variety of applications that involve processing and understanding high-resolution images, such as: Automated image captioning for marketing, e-commerce, or social media Visual question answering systems to assist users with detailed image analysis Intelligent image search and retrieval tools that can understand image content Art, design, and creative applications that require detailed image interpretation Things to try One interesting aspect of the internlm-xcomposer2-4khd-7b model is its ability to understand and describe fine visual details in high-resolution images. You could try providing the model with complex, detailed images and see how it responds, paying attention to the level of detail and nuance in the generated text. Additionally, you could experiment with using the model in multimodal applications that combine image and text inputs to explore its capabilities in areas like visual question answering or image-based storytelling.
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internlm-xcomposer2-vl-7b is a vision-language large model (VLLM) based on InternLM2 for advanced text-image comprehension and composition. The model was developed by internlm, who have also released the internlm-xcomposer model for similar capabilities. internlm-xcomposer2-vl-7b achieves strong performance on various multimodal benchmarks by leveraging the powerful InternLM2 as the initialization for the language model component. Model inputs and outputs internlm-xcomposer2-vl-7b is a large multimodal model that can accept both text and image inputs. The model can generate detailed textual descriptions of images, as well as compose text and images together in creative ways. Inputs Text**: The model can take text prompts as input, such as instructions or queries about an image. Images**: The model can accept images of various resolutions and aspect ratios, up to 4K resolution. Outputs Text**: The model can generate coherent and detailed textual responses based on the input image and text prompt. Interleaved text-image compositions**: The model can create unique compositions by generating text that is interleaved with the input image. Capabilities internlm-xcomposer2-vl-7b demonstrates strong multimodal understanding and generation capabilities. It can accurately describe the contents of images, answer questions about them, and even compose new text-image combinations. The model's performance rivals or exceeds other state-of-the-art vision-language models, making it a powerful tool for tasks like image captioning, visual question answering, and creative text-image generation. What can I use it for? internlm-xcomposer2-vl-7b can be used for a variety of multimodal applications, such as: Image captioning**: Generate detailed textual descriptions of images. Visual question answering**: Answer questions about the contents of images. Text-to-image composition**: Create unique compositions by generating text that is interleaved with an input image. Multimodal content creation**: Combine text and images in creative ways for applications like advertising, education, and entertainment. The model's strong performance and efficient design make it well-suited for both academic research and commercial use cases. Things to try One interesting aspect of internlm-xcomposer2-vl-7b is its ability to handle high-resolution images at any aspect ratio. This allows the model to perceive fine-grained visual details, which can be beneficial for tasks like optical character recognition (OCR) and scene text understanding. You could try inputting images with small text or complex visual scenes to see how the model performs. Additionally, the model's strong multimodal capabilities enable interesting creative applications. You could experiment with generating text-image compositions on a variety of topics, from abstract concepts to specific scenes or narratives. The model's ability to interweave text and images in novel ways opens up possibilities for innovative multimodal content creation.
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The internlm2-5-7b-chat model is a 7 billion parameter language model developed by internlm. It is part of the InternLM family of models, which also includes the internlm2-chat-7b and internlm-chat-7b models. The InternLM models are known for their outstanding reasoning capabilities, long-context support, and stronger tool use abilities compared to other open-source models of similar size. The internlm2-5-7b-chat model specifically demonstrates state-of-the-art performance on math reasoning tasks, surpassing models like LLaMA-3 and Gemma2-9B. It also excels at finding relevant information in long, 1 million character contexts, as shown by its leading results on the LongBench benchmark. Additionally, the model supports gathering information from over 100 web pages, with the corresponding implementation to be released in the Lagent project soon. Model inputs and outputs Inputs Natural language text prompts for the model to generate a response to. Outputs Generated natural language text responses to the input prompts. Capabilities The internlm2-5-7b-chat model showcases several advanced capabilities. It demonstrates outstanding reasoning skills, particularly in mathematical tasks, outperforming larger models like LLaMA-3 and Gemma2-9B. The model also has an exceptional ability to process long input contexts of up to 1 million characters, making it highly effective at "finding needles in haystacks" for tasks that require gathering and synthesizing information from large amounts of text. Additionally, the internlm2-5-7b-chat model has stronger tool use abilities compared to other open-source models. It can leverage over 100 web pages to gather information, and the upcoming Lagent project will further expand its tool utilization capabilities for complex, multi-step tasks. What can I use it for? The internlm2-5-7b-chat model's advanced reasoning, long-context, and tool use capabilities make it well-suited for a variety of applications, such as: Answering complex, multi-part questions that require gathering and synthesizing information from large amounts of text Solving challenging mathematical and logical problems Assisting with research and analysis tasks that involve sifting through large volumes of information Developing intelligent virtual assistants and chatbots with sophisticated language understanding and reasoning abilities Things to try One key aspect to explore with the internlm2-5-7b-chat model is its impressive ability to process and reason over long input contexts. Try providing the model with prompts that require it to draw insights and connections from extensive amounts of text, and observe how it is able to efficiently locate and integrate relevant information to formulate a coherent response. Another intriguing area to investigate is the model's evolving tool use capabilities. As the Lagent project progresses, experiment with prompts that involve the model leveraging various tools and data sources to tackle complex, multi-step tasks. This will help uncover the model's potential to serve as a versatile and adaptable assistant for a wide range of applications.
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