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What is LLaVA?

LLaVA, which stands for Large Language-and-Vision Assistant, is an innovative multimodal model that integrates a vision encoder with the Vicuna language model, facilitating a deeper comprehension of visual and textual data. Through its end-to-end training approach, LLaVA demonstrates impressive conversational skills akin to other advanced multimodal models like GPT-4. Notably, LLaVA-1.5 has achieved state-of-the-art outcomes across 11 benchmarks by utilizing publicly available data and completing its training in approximately one day on a single 8-A100 node, surpassing methods reliant on extensive datasets. The development of this model included creating a multimodal instruction-following dataset, generated using a language-focused variant of GPT-4. This dataset encompasses 158,000 unique language-image instruction-following instances, which include dialogues, detailed descriptions, and complex reasoning tasks. Such a rich dataset has been instrumental in enabling LLaVA to efficiently tackle a wide array of vision and language-related tasks. Ultimately, LLaVA not only improves interactions between visual and textual elements but also establishes a new standard for multimodal artificial intelligence applications. Its innovative architecture paves the way for future advancements in the integration of different modalities.

What is Ferret?

A sophisticated End-to-End MLLM has been developed to accommodate various types of references and effectively ground its responses. The Ferret Model employs a unique combination of Hybrid Region Representation and a Spatial-aware Visual Sampler, which facilitates detailed and adaptable referring and grounding functions within the MLLM framework. Serving as a foundational element, the GRIT Dataset consists of about 1.1 million entries, specifically designed as a large-scale and hierarchical dataset aimed at enhancing instruction tuning in the ground-and-refer domain. Moreover, the Ferret-Bench acts as a thorough multimodal evaluation benchmark that concurrently measures referring, grounding, semantics, knowledge, and reasoning, thus providing a comprehensive assessment of the model's performance. This elaborate configuration is intended to improve the synergy between language and visual information, which could lead to more intuitive AI systems that better understand and interact with users. Ultimately, advancements in these models may significantly transform how we engage with technology in our daily lives.

Media

Media

Integrations Supported

GPT-4
LLaMA-Factory

Integrations Supported

GPT-4
LLaMA-Factory

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

Free
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

LLaVA

Company Website

llava-vl.github.io

Company Facts

Organization Name

Apple

Date Founded

1976

Company Location

United States

Company Website

github.com/apple/ml-ferret

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