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| title: TorchTransformers Diffusion CV SFT | |
| emoji: ⚡ | |
| colorFrom: yellow | |
| colorTo: indigo | |
| sdk: streamlit | |
| sdk_version: 1.43.2 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| short_description: Torch Transformers Diffusion SFT for Computer Vision | |
| # SFT Tiny Titans 🚀 | |
| Tune NLP 🧠 or CV 🎨 fast! Texts 📝 or pics 📸, SFT shines ✨. `pip install -r requirements.txt`, `streamlit run app.py`. Snap cams 📷, craft art—AI’s lean & mean! 🎉 #SFTSpeed | |
| - **[Attention is All You Need](https://arxiv.org/abs/1706.03762)** - Vaswani et al., 2017: The transformer architecture powering NLP. | |
| - **[Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)** - Ho et al., 2020: Diffusion models for image generation. | |
| - **[Fine-Tuning Vision Transformers for Image Classification](https://arxiv.org/abs/2106.10504)** - Dosovitskiy et al., 2021: SFT in CV contexts. | |
| - **[PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://arxiv.org/abs/1912.01703)** - Paszke et al., 2019: The backbone of our deep learning stack. | |