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--- |
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title: TorchTransformers Diffusion CV SFT |
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emoji: ⚡ |
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colorFrom: yellow |
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colorTo: indigo |
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sdk: streamlit |
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sdk_version: 1.43.2 |
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app_file: app.py |
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pinned: false |
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license: mit |
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short_description: Torch Transformers Diffusion SFT for Computer Vision |
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--- |
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# SFT Tiny Titans 🚀 |
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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 |
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- **[Attention is All You Need](https://arxiv.org/abs/1706.03762)** - Vaswani et al., 2017: The transformer architecture powering NLP. |
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- **[Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)** - Ho et al., 2020: Diffusion models for image generation. |
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- **[Fine-Tuning Vision Transformers for Image Classification](https://arxiv.org/abs/2106.10504)** - Dosovitskiy et al., 2021: SFT in CV contexts. |
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- **[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. |
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