Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
An advanced End-to-End MLLM is designed to accept various forms of references and effectively ground responses. The Ferret Model utilizes a combination of Hybrid Region Representation and a Spatial-aware Visual Sampler, which allows for detailed and flexible referring and grounding capabilities within the MLLM framework. The GRIT Dataset, comprising approximately 1.1 million entries, serves as a large-scale and hierarchical dataset specifically crafted for robust instruction tuning in the ground-and-refer category. Additionally, the Ferret-Bench is a comprehensive multimodal evaluation benchmark that simultaneously assesses referring, grounding, semantics, knowledge, and reasoning, ensuring a well-rounded evaluation of the model's capabilities. This intricate setup aims to enhance the interaction between language and visual data, paving the way for more intuitive AI systems.
Description
LLaVA, or Large Language-and-Vision Assistant, represents a groundbreaking multimodal model that combines a vision encoder with the Vicuna language model, enabling enhanced understanding of both visual and textual information. By employing end-to-end training, LLaVA showcases remarkable conversational abilities, mirroring the multimodal features found in models such as GPT-4. Significantly, LLaVA-1.5 has reached cutting-edge performance on 11 different benchmarks, leveraging publicly accessible data and achieving completion of its training in about one day on a single 8-A100 node, outperforming approaches that depend on massive datasets. The model's development included the construction of a multimodal instruction-following dataset, which was produced using a language-only variant of GPT-4. This dataset consists of 158,000 distinct language-image instruction-following examples, featuring dialogues, intricate descriptions, and advanced reasoning challenges. Such a comprehensive dataset has played a crucial role in equipping LLaVA to handle a diverse range of tasks related to vision and language with great efficiency. In essence, LLaVA not only enhances the interaction between visual and textual modalities but also sets a new benchmark in the field of multimodal AI.
API Access
Has API
No
API Access
Has API
No
Integrations
ExecuTorch
No
GPT-4
No
LLaMA-Factory
No
Pricing Details
Free
Open source
Free Trial
No
Free Version
Yes
Pricing Details
Free
Free Trial
No
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
Yes
In Person
No
Vendor Details
Company Name
Apple
Founded
1976
Country
United States
Website
github.com/apple/ml-ferret
Vendor Details
Company Name
LLaVA
Website
llava-vl.github.io