fuyu-8b

Maintainer: adept

Total Score

951

Last updated 5/28/2024

🚀

PropertyValue
Run this modelRun on HuggingFace
API specView on HuggingFace
Github linkNo Github link provided
Paper linkNo paper link provided

Create account to get full access

or

If you already have an account, we'll log you in

Model overview

Fuyu-8B is a multi-modal text and image transformer model developed by Adept AI. It has a simple architecture compared to other multi-modal models, with a decoder-only transformer that linearly projects image patches into the first layer, bypassing the embedding lookup. This allows the model to handle arbitrary image resolutions without the need for separate high and low-resolution training stages. The model is optimized for digital agents, supporting tasks like answering questions about graphs and diagrams, UI-based questions, and fine-grained localization on screen images.

Model inputs and outputs

Inputs

  • Text: The model can consume text inputs.
  • Images: The model can also consume image inputs of arbitrary size, treating the image tokens like the sequence of text tokens.

Outputs

  • Text: The model generates text outputs in response to the provided text and image inputs.

Capabilities

The Fuyu-8B model is designed to be a versatile multi-modal AI assistant. It can understand and reason about both text and images, enabling it to perform tasks like visual question answering, image captioning, and multimodal chat. The model's fast inference speed, with responses for large images in under 100 milliseconds, makes it well-suited for real-time applications.

What can I use it for?

The Fuyu-8B model can be a powerful tool for a variety of applications, such as:

  • Digital Assistants: The model's multi-modal capabilities and focus on supporting digital agents make it a great fit for building conversational AI assistants that can understand and respond to both text and image inputs.
  • Content Creation: The model can be used to generate creative text formats like poetry, scripts, and marketing copy, while also incorporating relevant visual elements.
  • Visual Question Answering: The model can be used to build applications that can answer questions about images, diagrams, and other visual content.

Things to try

One interesting aspect of the Fuyu-8B model is its ability to handle arbitrary image resolutions. This means you can experiment with feeding the model different image sizes and observe how it responds. You can also try fine-tuning the model on specific datasets or tasks to see how it adapts and improves its performance.



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

AI model preview image

fuyu-8b

lucataco

Total Score

4

fuyu-8b is a multi-modal transformer model trained by Adept AI. It is capable of processing both text and images, allowing it to perform a variety of tasks such as image captioning, visual question answering, and image generation. Similar models created by the same maintainer, lucataco, include PixArt-Alpha 1024px, a text-to-image diffusion system, and SDXL v1.0, a general-purpose text-to-image generator. Model inputs and outputs The fuyu-8b model can accept two types of inputs: a text prompt and an optional image. The text prompt is used to guide the model's generation or analysis of the image. The output of the model is a text response that describes the image or answers a question about it. Inputs Prompt**: A text prompt that provides instructions or context for the model Image**: An optional image that the model can analyze or generate content for Outputs Text response**: A text output that describes the image or answers a question about it Capabilities The fuyu-8b model can perform a range of multi-modal tasks, such as image captioning, visual question answering, and image generation. For example, it can generate detailed captions for images, answer questions about the contents of an image, or create new images based on a text prompt. What can I use it for? The fuyu-8b model could be useful for a variety of applications, such as automating image captioning for social media, enhancing visual search engines, or generating custom images for marketing and design. By combining text and image processing capabilities, the model could also be used to build conversational AI assistants that can understand and respond to multimodal inputs. Things to try One interesting thing to try with the fuyu-8b model is to experiment with different types of text prompts and see how the model responds. You could try prompts that are very specific and descriptive, or more open-ended and creative. Additionally, you could try providing the model with different types of images, such as photographs, paintings, or digital art, and see how it interprets and generates content for them.

Read more

Updated Invalid Date

🏋️

Bunny-Llama-3-8B-V

BAAI

Total Score

71

Bunny-Llama-3-8B-V is a family of lightweight but powerful multimodal models developed by BAAI. It offers multiple plug-and-play vision encoders, like EVA-CLIP and SigLIP, as well as language backbones including Llama-3-8B-Instruct, Phi-1.5, StableLM-2, Qwen1.5, MiniCPM, and Phi-2. Model Inputs and Outputs Bunny-Llama-3-8B-V is a multimodal model that can consume both text and images, and produce text outputs. Inputs Text Prompt**: A text prompt or instruction that the model uses to generate a response. Image**: An optional image that the model can use to inform its text generation. Outputs Generated Text**: The model's response to the provided text prompt and/or image. Capabilities The Bunny-Llama-3-8B-V model is capable of generating coherent and relevant text outputs based on a given text prompt and/or image. It can be used for a variety of multimodal tasks, such as image captioning, visual question answering, and image-grounded text generation. What Can I Use It For? Bunny-Llama-3-8B-V can be used for a variety of multimodal applications, such as: Image Captioning**: Generate descriptive captions for images. Visual Question Answering**: Answer questions about the contents of an image. Image-Grounded Dialogue**: Generate responses in a conversation that are informed by a relevant image. Multimodal Content Creation**: Produce text outputs that are coherently grounded in visual information. Things to Try Some interesting things to try with Bunny-Llama-3-8B-V could include: Experimenting with different text prompts and image inputs to see how the model responds. Evaluating the model's performance on standard multimodal benchmarks like VQAv2, OKVQA, and COCO Captions. Exploring the model's ability to reason about and describe diagrams, charts, and other types of visual information. Investigating how the model's performance varies when using different language backbones and vision encoders.

Read more

Updated Invalid Date

🎯

RakutenAI-7B-chat

Rakuten

Total Score

51

RakutenAI-7B-chat is a Japanese language model developed by Rakuten. It builds upon the Mistral model architecture and the Mistral-7B-v0.1 pre-trained checkpoint. Rakuten has extended the vocabulary from 32k to 48k to improve the character-per-token rate for Japanese. According to an independent evaluation by Kamata et al., the instruction-tuned and chat versions of RakutenAI-7B achieve the highest performance among similar models like OpenCalm, Elyza, Youri, Nekomata and Swallow on Japanese language benchmarks. Model inputs and outputs Inputs Text prompts provided to the model in the form of a conversational exchange between a user and an AI assistant. Outputs Responses generated by the model to continue the conversation in a helpful and polite manner. Capabilities RakutenAI-7B-chat is capable of engaging in open-ended conversations and providing detailed, informative responses on a wide range of topics. Its strong performance on Japanese language benchmarks suggests it can understand and generate high-quality Japanese text. What can I use it for? RakutenAI-7B-chat could be used to power conversational AI assistants for Japanese-speaking users, providing helpful information and recommendations on various subjects. Developers could integrate it into chatbots, virtual agents, or other applications that require natural language interaction in Japanese. Things to try With RakutenAI-7B-chat, you can experiment with different types of conversational prompts to see how the model responds. Try asking it for step-by-step instructions, opinions on current events, or open-ended questions about its own capabilities. The model's strong performance on Japanese benchmarks suggests it could be a valuable tool for a variety of Japanese language applications.

Read more

Updated Invalid Date

🚀

Falcon-7B-Chat-v0.1

dfurman

Total Score

44

The Falcon-7B-Chat-v0.1 model is a chatbot model for dialogue generation, based on the Falcon-7B model. It was fine-tuned by dfurman on the OpenAssistant/oasst1 dataset using the peft library. Model inputs and outputs Inputs Instruction or prompt**: The input to the model is a conversational prompt or instruction, which the model will use to generate a relevant response. Outputs Generated text**: The output of the model is a generated response, continuing the conversation or addressing the provided instruction. Capabilities The Falcon-7B-Chat-v0.1 model is capable of engaging in open-ended dialogue, responding to prompts, and generating coherent and contextually appropriate text. It can be used for tasks like chatbots, virtual assistants, and creative text generation. What can I use it for? The Falcon-7B-Chat-v0.1 model can be used as a foundation for building conversational AI applications. For example, you could integrate it into a chatbot interface to provide helpful responses to user queries, or use it to generate creative writing prompts and story ideas. Its fine-tuning on the OpenAssistant dataset also makes it well-suited for assisting with tasks and answering questions. Things to try One interesting aspect of the Falcon-7B-Chat-v0.1 model is its ability to engage in multi-turn dialogues. You could try providing it with a conversational prompt and see how it responds, then continue the dialogue by feeding its previous output back as the new prompt. This can help to explore the model's conversational and reasoning capabilities. Another thing to try would be to provide the model with more specific instructions or prompts, such as requests to summarize information, answer questions, or generate creative content. This can help to showcase the model's versatility and understand its strengths and limitations in different task domains.

Read more

Updated Invalid Date