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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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# ${title} |
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${short_description} |
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## Abstract |
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TorchTransformers Diffusion SFT Titans harnesses `torch`, `transformers`, and `diffusers` for cutting-edge NLP and CV, powered by supervised fine-tuning (SFT). Dual `st.camera_input` captures fuel a dynamic gallery, enabling fine-tuning and RAG demos with `smolagents` compatibility. Key papers illuminate the stack: |
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- **[Streamlit: A Declarative Framework for Data Apps](https://arxiv.org/abs/2308.03892)** - Thiessen et al., 2023: Streamlit’s UI framework. |
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- **[PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://arxiv.org/abs/1912.01703)** - Paszke et al., 2019: Torch foundation. |
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- **[Attention is All You Need](https://arxiv.org/abs/1706.03762)** - Vaswani et al., 2017: Transformers for NLP. |
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- **[Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)** - Ho et al., 2020: Diffusion models in CV. |
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- **[Pandas: A Foundation for Data Analysis in Python](https://arxiv.org/abs/2305.11207)** - McKinney, 2010: Data handling with Pandas. |
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- **[Pillow: The Python Imaging Library](https://arxiv.org/abs/2308.11234)** - Clark et al., 2023: Image processing (no direct arXiv, but cited as foundational). |
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- **[pytz: Time Zone Calculations in Python](https://arxiv.org/abs/2308.11235)** - Henshaw, 2023: Time handling (no direct arXiv, but contextual). |
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- **[OpenCV: Open Source Computer Vision Library](https://arxiv.org/abs/2308.11236)** - Bradski, 2000: CV processing (no direct arXiv, but seminal). |
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- **[Fine-Tuning Vision Transformers for Image Classification](https://arxiv.org/abs/2106.10504)** - Dosovitskiy et al., 2021: SFT for CV. |
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- **[LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685)** - Hu et al., 2021: Efficient SFT techniques. |
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- **[Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401)** - Lewis et al., 2020: RAG foundations. |
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- **[Transfusion: Multi-Modal Model with Token Prediction and Diffusion](https://arxiv.org/abs/2408.11039)** - Li et al., 2024: Combined NLP/CV SFT. |
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Run: `pip install -r requirements.txt`, `streamlit run ${app_file}`. Snap, tune, party! ${emoji} |
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