llm-gguf 0.2, now with embeddings. This new release of my llm-gguf plugin - which provides support for locally hosted GGUF LLMs - adds a new feature: it now supports embedding models distributed as GGUFs as well.
This means you can use models like the bafflingly small (30.8MB in its smallest quantization) mxbai-embed-xsmall-v1 with LLM like this:
llm install llm-gguf
llm gguf download-embed-model \
'https://huggingface.co/mixedbread-ai/mxbai-embed-xsmall-v1/resolve/main/gguf/mxbai-embed-xsmall-v1-q8_0.gguf'
Then to embed a string:
llm embed -m gguf/mxbai-embed-xsmall-v1-q8_0 -c 'hello'
The LLM docs have extensive coverage of things you can then do with this model, like embedding every row in a CSV file / file in a directory / record in a SQLite database table and running similarity and semantic search against them.
Under the hood this takes advantage of the create_embedding() method provided by the llama-cpp-python wrapper around llama.cpp.
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