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Daniel Bourke PRO

mrdbourke
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https://www.mrdbourke.com

AI & ML interests

Computer vision. Small on-device models. VLMs. High-quality tutorials.

Recent Activity

upvoted a paper about 14 hours ago
FastVLM: Efficient Vision Encoding for Vision Language Models
reacted to IlyasMoutawwakil's post with 🔥 about 22 hours ago
After 2 months of refinement, I'm happy to announce that a lot of Transformers' modeling code is now significantly more torch-compile & export-friendly 🔥 Why it had to be done 👇 PyTorch's Dynamo compiler is increasingly becoming the default interoperability layer for ML systems. Anything that relies on torch.export or torch.compile, from model optimization to cross-framework integrations, benefits directly when models can be captured as a single dynamo-traced graph ! Transformers models are now easier to: ⚙️ Compile end-to-end with torch.compile backends 📦 Export reliably via torch.export and torch.onnx.export 🚀 Deploy to ONNX / ONNX Runtime, Intel Corporation's OpenVINO, NVIDIA AutoDeploy (TRT-LLM), AMD's Quark, Meta's Executorch and more hardware-specific runtimes. This work aims at unblocking entire TorchDynamo-based toolchains that rely on exporting Transformers across runtimes and accelerators. We are doubling down on Transformers commitment to be a first-class citizen of the PyTorch ecosystem, more exportable, more optimizable, and easier to deploy everywhere. There are definitely some edge-cases that we still haven't addressed so don't hesitate to try compiling / exporting your favorite transformers and to open issues / PRs. PR in the comments ! More updates coming coming soon !
reacted to danielhanchen's post with ❤️ about 22 hours ago
You can now fine-tune embedding models in our free Unsloth notebook! 🤗 Fine-tuning embedding models improves retrieval & RAG by aligning vectors to your domain-specific notion of similarity, improving search, clustering, and recommendations on your data. ⭐ Blog + Notebooks: https://unsloth.ai/docs/new/embedding-finetuning Unsloth trains embedding models 1.8-3.3x faster with 20% less VRAM, 2x longer context & no accuracy loss vs. FA2 setups. We'd like to thank Hugging Face and Unsloth contributor: electroglyph for making this possible!
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