txtai
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Build and run custom text processing workflows
David Mezzetti
davidmezzetti
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about 3 hours ago
NeuML/bert-hash-nano:Not working with transformer 5.0
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a Space
7 days ago
NeuML/txtai
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a Space
7 days ago
NeuML/wikisummary
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Not working with transformer 5.0
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#1 opened about 4 hours ago
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Aexyno
updated
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Spaces
7 days ago
updated
a
model
11 days ago
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collection
24 days ago
posted
an
update
about 1 month ago
Post
212
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
replied to
their
post
about 1 month ago
There is nothing inherent about it that requires a GPU. Did you run into an issue?
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their
post
about 1 month ago
Thank you!
replied to
their
post
about 1 month ago
The training dataset is from here: https://hf.co/datasets/mecha-org/linux-command-dataset
posted
an
update
about 1 month ago
Post
1192
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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updated
a
Space
about 1 month ago
updated
a
dataset
about 1 month ago
commented on
๐ฅ Distilling Tiny Embeddings
about 1 month ago
Glad to hear it, good luck!
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an
update
about 1 month ago
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2190
๐ฅ 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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Article
๐ฅ Distilling Tiny Embeddings
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