This repository provides a collection of deep learning models and examples built using the Burn framework, targeting researchers and developers working with modern AI architectures. It offers implementations of popular models like Llama, MobileNetV2, and YOLOX, enabling users to leverage advanced AI capabilities with the Burn ecosystem.

How It Works

The project showcases various deep learning architectures, including large language models (LLMs), convolutional neural networks (CNNs) for image classification and object detection, and speech recognition models. Each model is implemented within the Burn framework, highlighting its flexibility and performance for diverse AI tasks. The repository serves as a reference for building and deploying models using Burn's tensor manipulation and autodiff capabilities.

Quick Start & Requirements

• Installation and usage details are typically found within individual model sub-directories.
• Requirements vary per model but generally include Python and the Burn framework.
• Links to specific model repositories are provided for detailed instructions.

Highlighted Details

• Implements official models such as Llama 3, TinyLlama, MobileNetV2, SqueezeNet, ResNet, RoBERTa, and YOLOX.
• Features community contributions including Llama 2, Whisper, Stable Diffusion v1.4, and CRAFT.
• Demonstrates a range of AI applications from LLMs and speech recognition to image generation and object detection.
• Provides links to dedicated repositories for each model, facilitating deeper exploration.

Maintenance & Community

• The repository is maintained by the tracel-ai team.
• Community contributions are actively encouraged and linked.
• Specific community model repositories may have their own maintenance and community channels.

Licensing & Compatibility

• Models within this repository are distributed under the MIT and Apache License (Version 2.0).
• Users are advised to check the specific licenses of community-contributed models.
• Compatibility for commercial use is generally permissive under MIT/Apache 2.0, but community models require individual verification.

Limitations & Caveats

Community models linked may have different licensing terms and maintenance statuses, requiring users to consult their respective repositories. The project focuses on showcasing Burn's capabilities, and specific performance benchmarks or production-readiness details are model-dependent.