🥃 Distilling Tiny Embeddings
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davidmezzetti
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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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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
davidmezzetti
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🥃 Distilling Tiny Embeddings
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davidmezzetti
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davidmezzetti
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davidmezzetti
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davidmezzetti
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davidmezzetti
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davidmezzetti
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