Project Ferret: Bridging Communication and Understanding
Ferret, an advanced machine learning model, is designed to revolutionize how we refer and ground information in any context. This project accompanies a research paper and aims to provide an end-to-end solution through a multilingual language model (MLLM) capable of tackling tasks related to referring, grounding, and reasoning.
Key Features of Ferret
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Comprehensive Model: The Ferret model combines hybrid region representation with a spatial-aware visual sampler, enabling precise and open-ended referencing and grounding within various contexts.
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GRIT Dataset: A pivotal component of Ferret is the GRIT Dataset, which encompasses around 1.1 million entries, providing a comprehensive set of training data that is large-scale, hierarchical, and robust. This dataset is crucial for instruction tuning in tasks that require referring and grounding.
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Ferret-Bench: This evaluation benchmark uniquely assesses multimodal capabilities by focusing on tasks that require referring, grounding, semantic understanding, knowledge, and reasoning—reflecting real-world communication needs.
Release Milestones
- October 8, 2024: The Ferret-UI, a user interface-centric model, was released, marking a significant milestone as it effectively handles referring, grounding, and reasoning tasks.
- July 10, 2024: Ferret-v2 received acceptance at the prestigious COLM 2024 conference.
- February 15, 2024: The Ferret project was spotlighted at the International Conference on Learning Representations (ICLR) 2024.
- December 14, 2023: Release of Ferret checkpoints for models of 7 billion and 13 billion parameters.
Installation and Training
For those interested in exploring Ferret, the project provides detailed steps for installation and model training:
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Installation: Users can clone the repository and install the necessary packages. This includes setting up Python environments and installing additional packages for specific training needs.
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Training: Ferret is equipped to train on advanced GPU setups, allowing for flexible batch size adjustments. Detailed scripts and guidelines ensure smooth training experiences, mimicking real-world application scenarios.
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Evaluation: A comprehensive document guides users through evaluating the model's performance, ensuring that Ferret's outcomes meet the robust standards set by its creators.
Intended Use
Ferret is primarily intended for research purposes, with restrictions in place to ensure ethical and appropriate use of its data and software. The models are licensed for non-commercial research applications, following the licensing agreement of LLaMA, Vicuna, and GPT-4.
How to Get Started
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Local Demo: Users can experience Ferret through a local demo, employing Gradio Web UI for interaction. Instructions guide users through setting up a controller, launching a web server, and connecting a model worker for real-time demonstration.
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Checkpoints and Offsets: By downloading Ferret's checkpoints and applying specific offsets, users can tailor the pre-trained models for customized applications, further enhancing their understanding and usage of the system.
Acknowledgements
The Ferret project acknowledges contributions and builds upon notable software repositories like LLaVA and Vicuna, embodying a collaborative spirit in advancing machine learning technologies.
Ferret stands as a testament to the advancements in machine learning, offering a versatile tool that can significantly impact how referencing and grounding are conducted in AI applications.