tiny-llava-v1-hf
Maintainer: bczhou
49
✨
| 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 |
Create account to get full access
Model overview
tiny-llava-v1-hf is a small-scale large multimodal model developed by bczhou, part of the TinyLLaVA framework. It is a text-to-text model that can handle both images and text inputs, aiming to achieve high performance with fewer parameters compared to larger models. The model is built upon the foundational work of LLaVA and Video-LLaVA, utilizing a unified visual representation to enable simultaneous reasoning on both images and videos.
Model inputs and outputs
The tiny-llava-v1-hf model accepts both text and image inputs, allowing for multimodal interaction. It can generate text outputs in response to the provided prompts, leveraging the visual information to enhance its understanding and generation capabilities.
Inputs
- Text: The model can accept text prompts, which can include instructions, questions, or descriptions related to the provided images.
- Images: The model can handle image inputs, which are used to provide visual context for the text-based prompts.
Outputs
- Text: The primary output of the model is generated text, which can include answers, descriptions, or other relevant responses based on the provided inputs.
Capabilities
The tiny-llava-v1-hf model exhibits impressive multimodal capabilities, allowing it to leverage both text and visual information to perform a variety of tasks. It can answer questions about images, generate image captions, and even engage in open-ended conversations that involve both textual and visual elements.
What can I use it for?
The tiny-llava-v1-hf model can be useful for a wide range of applications that require multimodal understanding and generation, such as:
- Intelligent assistants: The model can be incorporated into chatbots or virtual assistants to provide enhanced visual understanding and reasoning capabilities.
- Visual question answering: The model can be used to answer questions about images, making it useful for applications in education, e-commerce, or information retrieval.
- Image captioning: The model can generate descriptive captions for images, which can be useful for accessibility, content moderation, or content generation purposes.
- Multimodal storytelling: The model can be used to create interactive stories that seamlessly combine text and visual elements, opening up new possibilities for creative and educational applications.
Things to try
One interesting aspect of the tiny-llava-v1-hf model is its ability to perform well with fewer parameters compared to larger models. Developers and researchers can experiment with different optimization techniques, such as 4-bit or 8-bit quantization, to further reduce the model size while maintaining its performance. Additionally, exploring various finetuning strategies on domain-specific datasets could unlock even more specialized capabilities for the model.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
🌿
llava-v1.5-7b
274
llava-v1.5-7b is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model based on the transformer architecture. The model was created by liuhaotian, and similar models include llava-v1.5-7B-GGUF, LLaVA-13b-delta-v0, llava-v1.6-mistral-7b, and llava-1.5-7b-hf. Model inputs and outputs llava-v1.5-7b is a large language model that can take in textual prompts and generate relevant responses. The model is particularly designed for multimodal tasks, allowing it to process and generate text based on provided images. Inputs Textual prompts in the format "USER: \nASSISTANT:" Optional image data, indicated by the `` token in the prompt Outputs Generated text responses relevant to the given prompt and image (if provided) Capabilities llava-v1.5-7b can perform a variety of tasks, including: Open-ended conversation Answering questions about images Generating captions for images Providing detailed descriptions of scenes and objects Assisting with creative writing and ideation The model's multimodal capabilities allow it to understand and generate text based on both textual and visual inputs. What can I use it for? llava-v1.5-7b can be a powerful tool for researchers and hobbyists working on projects related to computer vision, natural language processing, and artificial intelligence. Some potential use cases include: Building interactive chatbots and virtual assistants Developing image captioning and visual question answering systems Enhancing text generation models with multimodal understanding Exploring the intersection of language and vision in AI By leveraging the model's capabilities, you can create innovative applications that combine language and visual understanding. Things to try One interesting thing to try with llava-v1.5-7b is its ability to handle multi-image and multi-prompt generation. This means you can provide multiple images in a single prompt and the model will generate a response that considers all the visual inputs. This can be particularly useful for tasks like visual reasoning or complex scene descriptions. Another intriguing aspect of the model is its potential for synergy with other large language models, such as GPT-4. As mentioned in the LLaVA-13b-delta-v0 model card, the combination of llava-v1.5-7b and GPT-4 set a new state-of-the-art on the ScienceQA dataset. Exploring these types of model combinations and their capabilities can lead to exciting advancements in the field of multimodal AI.
Updated Invalid Date
🔎
llava-v1.6-34b
275
The llava-v1.6-34b is an open-source chatbot developed by liuhaotian that is trained by fine-tuning a large language model (LLM) on multimodal instruction-following data. It is based on the transformer architecture and uses the NousResearch/Nous-Hermes-2-Yi-34B as its base LLM. The model is part of the LLaVA family, which includes similar versions like llava-v1.5-13b, llava-v1.5-7b, llava-v1.6-mistral-7b, and LLaVA-13b-delta-v0. These models differ in their base LLM, training dataset, and model size. Model inputs and outputs Inputs The model accepts natural language instructions and prompts as input. It can also accept image data as input for multimodal tasks. Outputs The model generates human-like responses in natural language. For multimodal tasks, the model can generate relevant images as output. Capabilities The llava-v1.6-34b model has been trained to engage in a wide range of tasks, including natural language processing, computer vision, and multimodal reasoning. It has shown strong performance on tasks such as answering complex questions, following detailed instructions, and generating relevant images. What can I use it for? The primary use of the llava-v1.6-34b model is for research on large multimodal models and chatbots. It can be particularly useful for researchers and hobbyists working in computer vision, natural language processing, machine learning, and artificial intelligence. Some potential use cases for the model include: Building chatbots and virtual assistants with multimodal capabilities Developing visual question answering systems Exploring new techniques for instruction-following in language models Advancing research on multimodal reasoning and understanding Things to try One interesting aspect of the llava-v1.6-34b model is its ability to combine text and image data to perform complex tasks. Researchers could experiment with using the model to generate images based on textual descriptions, or to answer questions that require both visual and linguistic understanding. Another area to explore is the model's performance on tasks that require strong reasoning and problem-solving skills, such as scientific question answering or task-oriented dialogue. By probing the model's capabilities in these areas, researchers can gain valuable insights into the strengths and limitations of large multimodal language models.
Updated Invalid Date
👁️
llava-v1.5-13b
428
llava-v1.5-13b is an open-source chatbot trained by fine-tuning LLaMA and Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model based on the transformer architecture. The model was trained and released by liuhaotian, a prominent AI researcher. Similar models include the smaller llava-v1.5-7b, the fine-tuned llava-v1.5-7B-GGUF, and the LLaVA-13b-delta-v0 delta model. Model inputs and outputs llava-v1.5-13b is a multimodal language model that can process both text and images. It takes in a prompt containing both text and the `` tag, and generates relevant text output in response. Inputs Text prompt containing the `` tag One or more images Outputs Relevant text output generated in response to the input prompt and image(s) Capabilities llava-v1.5-13b excels at tasks involving multimodal understanding and instruction-following. It can answer questions about images, generate image captions, and perform complex reasoning over both text and visual inputs. The model has been evaluated on a variety of benchmarks, including academic VQA datasets and recent instruction-following datasets, and has demonstrated strong performance. What can I use it for? The primary intended uses of llava-v1.5-13b are research on large multimodal models and chatbots. Researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence can use the model to explore and develop new techniques in these domains. The model's capabilities in multimodal understanding and instruction-following make it a valuable tool for applications such as visual question answering, image captioning, and interactive AI assistants. Things to try One interesting aspect of llava-v1.5-13b is its ability to handle multiple images and prompts simultaneously. Users can experiment with providing the model with a prompt that references several images and see how it generates responses that integrate information from the different visual inputs. Additionally, the model's strong performance on instruction-following tasks suggests opportunities for exploring interactive, task-oriented applications that leverage its understanding of natural language and visual cues.
Updated Invalid Date
🏋️
Video-LLaVA-7B
75
Video-LLaVA-7B is a powerful AI model developed by LanguageBind that exhibits remarkable interactive capabilities between images and videos, despite the absence of image-video pairs in the dataset. The model combines a pre-trained large language model with a pre-trained vision encoder, enabling it to perform visual reasoning on both images and videos simultaneously. The model's key highlight is its "simple baseline, learning united visual representation by alignment before projection", which allows it to bind unified visual representations to the language feature space. This enables the model to leverage the complementarity of image and video modalities, showcasing significant superiority compared to models specifically designed for either images or videos. Similar models include video-llava by nateraw, llava-v1.6-mistral-7b-hf by llava-hf, nanoLLaVA by qnguyen3, and llava-13b by yorickvp, all of which aim to push the boundaries of visual-language models. Model inputs and outputs Video-LLaVA-7B is a multimodal model that takes both text and visual inputs to generate text outputs. The model can handle a wide range of visual-language tasks, from image captioning to visual question answering. Inputs Text prompt**: A natural language prompt that describes the task or provides instructions for the model. Image/Video**: An image or video that the model will use to generate a response. Outputs Text response**: The model's generated response, which could be a caption, answer, or other relevant text, depending on the task. Capabilities Video-LLaVA-7B is capable of performing a variety of visual-language tasks, including image captioning, visual question answering, and multimodal chatbot use cases. The model's unique ability to handle both images and videos sets it apart from models designed for a single visual modality. What can I use it for? You can use Video-LLaVA-7B for a wide range of applications that involve both text and visual inputs, such as: Image and video description generation**: Generate captions or descriptions for images and videos. Multimodal question answering**: Answer questions about the content of images and videos. Multimodal dialogue systems**: Develop chatbots that can understand and respond to both text and visual inputs. Visual reasoning**: Perform tasks that require understanding and reasoning about visual information. Things to try One interesting thing to try with Video-LLaVA-7B is to explore its ability to handle both images and videos. You could, for example, ask the model questions about the content of a video or try generating captions for a sequence of frames. Additionally, you could experiment with the model's performance on specific visual-language tasks and compare it to models designed for single-modal inputs.
Updated Invalid Date