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Description
Florence-2-large is a cutting-edge vision foundation model created by Microsoft, designed to tackle an extensive range of vision and vision-language challenges such as caption generation, object recognition, segmentation, and optical character recognition (OCR). Utilizing a sequence-to-sequence framework, it leverages the FLD-5B dataset, which comprises over 5 billion annotations and 126 million images, to effectively engage in multi-task learning. This model demonstrates remarkable proficiency in both zero-shot and fine-tuning scenarios, delivering exceptional outcomes with minimal training required. In addition to detailed captioning and object detection, it specializes in dense region captioning and can interpret images alongside text prompts to produce pertinent answers. Its versatility allows it to manage an array of vision-related tasks through prompt-driven methods, positioning it as a formidable asset in the realm of AI-enhanced visual applications. Moreover, users can access the model on Hugging Face, where pre-trained weights are provided, facilitating a swift initiation into image processing and the execution of various tasks. This accessibility ensures that both novices and experts can harness its capabilities to enhance their projects efficiently.
Description
Recent breakthroughs in natural language processing, comprehension, and generation have been greatly influenced by the development of large language models. This research presents a system that employs Ascend 910 AI processors and the MindSpore framework to train a language model exceeding one trillion parameters, specifically 1.085 trillion, referred to as PanGu-{\Sigma}. This model enhances the groundwork established by PanGu-{\alpha} by converting the conventional dense Transformer model into a sparse format through a method known as Random Routed Experts (RRE). Utilizing a substantial dataset of 329 billion tokens, the model was effectively trained using a strategy called Expert Computation and Storage Separation (ECSS), which resulted in a remarkable 6.3-fold improvement in training throughput through the use of heterogeneous computing. Through various experiments, it was found that PanGu-{\Sigma} achieves a new benchmark in zero-shot learning across multiple downstream tasks in Chinese NLP, showcasing its potential in advancing the field. This advancement signifies a major leap forward in the capabilities of language models, illustrating the impact of innovative training techniques and architectural modifications.
API Access
Has API
API Access
Has API
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Integrations
PanGu Chat
Pricing Details
Free
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Microsoft
Founded
1975
Country
United States
Website
huggingface.co/microsoft/Florence-2-large
Vendor Details
Company Name
Huawei
Founded
1987
Country
China
Website
huawei.com