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
Introducing Kubestone, the operator designed for benchmarking within Kubernetes environments. Kubestone allows users to assess the performance metrics of their Kubernetes setups effectively. It offers a standardized suite of benchmarks to evaluate CPU, disk, network, and application performance. Users can exercise detailed control over Kubernetes scheduling elements, including affinity, anti-affinity, tolerations, storage classes, and node selection. It is straightforward to introduce new benchmarks by developing a fresh controller. The execution of benchmark runs is facilitated through custom resources, utilizing various Kubernetes components such as pods, jobs, deployments, and services. To get started, refer to the quickstart guide which provides instructions on deploying Kubestone and running benchmarks. You can execute benchmarks via Kubestone by creating the necessary custom resources within your cluster. Once the appropriate namespace is created, it can be utilized to submit benchmark requests, and all benchmark executions will be organized within that specific namespace. This streamlined process ensures that you can easily monitor and analyze the performance of your Kubernetes applications.
API Access
Has API
No
API Access
Has API
Yes
Integrations
Kubernetes
No
Pricing Details
Free
Open source
Free Trial
No
Free Version
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
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
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)
No
In Person
No
Vendor Details
Company Name
Apple
Founded
1976
Country
United States
Website
github.com/apple/ml-ferret
Vendor Details
Company Name
Kubestone
Website
kubestone.io/en/latest/