Welcome to the Models repository! Here, you'll find a diverse collection of deep learning models and examples constructed using the Burn deep learning framework.
| Model | Description | Repository Link |
|---|---|---|
| Llama | Llama 3 and TinyLlama large language models. | llama-burn |
| MobileNetV2 | A CNN model targeted at mobile devices. | mobilenetv2-burn |
| SqueezeNet | A small CNN-based model for image classification. | squeezenet-burn |
| ResNet | A CNN based on residual blocks with skip connections. | resnet-burn |
| RoBERTa | A robustly optimized BERT pretraining approach. | bert-burn |
| YOLOX | A single-stage object detector based on the YOLO series. | yolox-burn |
Explore the curated list of models developed by the community ♥.
| Model | Description | Repository Link |
|---|---|---|
| Llama 2 | LLMs by Meta AI, ranging from 7 billion to 70 billion parameters. | Gadersd/llama2-burn |
| Whisper | A general-purpose speech recognition model by OpenAI. | Gadersd/whisper-burn |
| Stable Diffusion v1.4 | An image generation model developed by Stability AI. | Gadersd/stable-diffusion-burn |
| kord (music note predictor) | A music theory model that can detect notes in short audio clips. | twitchax/kord |
| Whisper-Live | A fork of Gadersd/whisper-burn which has been updated for Burn 13 and provides live transcription | sudomonikers/whisper-burn |
| Inception V3 | A CNN used for calculating FID scores. | varonroy/inception-v3-burn |
| CRAFT | A CNN for character-region aware text detection | wingertge/craft-burn |
Models implemented in this repository are distributed under the terms of both the MIT license and the Apache License (Version 2.0). See LICENSE-APACHE and LICENSE-MIT for complete details.
Please note that opening a pull request signals your agreement with these licensing terms. If you copy or adapt material from other resources or codebases, ensure that you include the original license information in the NOTICES.md file under the corresponding model directory.
Community models linked in this repository may fall under different licenses, so please consult the respective repositories for specific license information.