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In a Training Loop 🔄
40.9
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Mariusz Kurman
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mkurman
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🥃 Distilling Tiny Embeddings. We're happy to build on the BERT Hash Series of models with this new set of fixed dimensional tiny embeddings models. Ranging from 244K parameters to 970K and 50 dimensions to 128 dimensions these tiny models pack quite a punch. Use cases include on-device semantic search, similarity comparisons, LLM chunking and Retrieval Augmented Generation (RAG). The advantage is that data never needs to leave the device while still having solid performance. https://huggingface.co/blog/NeuML/bert-hash-embeddings
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Our engineer Alan from https://robonine.com team has assembled the mechanical frame of our 6-DoF manipulator prototype - without servo motors for now. At this stage we are evaluating how easy the structure is to assemble, checking for any mechanical play, and validating the kinematics. Good news: the structure feels solid and Alan reports no detectable backlash so far.
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mkurman/medical-SYNTH-reasoning-preview
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mkurman/basic-math-operations
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mkurman/count_to_x
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mkurman/counting-letters-RL
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mkurman/spelling-RL
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mkurman/medmcqa-hard
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mkurman/MedSpellCount-QA
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mkurman/hindawi-journals-2007-2023
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mkurman/simplescaling-s1K-R1
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