🥃 Distilling Tiny Embeddings
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#1 opened about 14 hours ago
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Aexyno
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Do you like to "think" in Jupyter Notebooks? Want to integrate OpenCode with that? Then check this project out.
NCoder is an open-source AI coding agent that connects a local running OpenCode server to a Jupyter Notebook via TxtAI.
https://github.com/neuml/ncoder
NCoder is an open-source AI coding agent that connects a local running OpenCode server to a Jupyter Notebook via TxtAI.
https://github.com/neuml/ncoder
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Tired of paying a bunch of money for large LLMs? Then read this notebook and/or watch this video on how to distill knowledge into tiny LLMs.
Notebook: https://github.com/neuml/txtai/blob/master/examples/80_Distilling_Knowledge_into_Tiny_LLMs.ipynb
Video: https://www.youtube.com/watch?v=Ol560ktgkf0
Notebook: https://github.com/neuml/txtai/blob/master/examples/80_Distilling_Knowledge_into_Tiny_LLMs.ipynb
Video: https://www.youtube.com/watch?v=Ol560ktgkf0
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davidmezzetti
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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
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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🥃 Distilling Tiny Embeddings
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davidmezzetti
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davidmezzetti
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