Source URL: https://simonwillison.net/2024/Nov/21/llm-gguf-embeddings/#atom-everything
Source: Simon Willison’s Weblog
Title: llm-gguf 0.2, now with embeddings
Feedly Summary: llm-gguf 0.2, now with embeddings
This new release of my llm-gguf plugin – which adds 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.
Tags: llm, generative-ai, projects, ai, embeddings
AI Summary and Description: Yes
Summary: The text describes a new release of the llm-gguf plugin that enhances support for locally hosted GGUF Large Language Models (LLMs) by integrating embedding models. This functionality allows efficient semantic search and similarity comparisons using various data sources, making it significant for professionals in AI security and infrastructure.
Detailed Description:
The provided content discusses the latest version of the llm-gguf plugin, which incorporates advanced features beneficial for AI practitioners, particularly those working with LLMs. Key highlights include:
– **Integration of Embeddings**: The new functionality supports embedding models that are distributed as GGUFs, enabling users to work with different types of AI models seamlessly.
– **Ease of Use**: Provides a straightforward command-line approach to installing and utilizing these models. An example usage shows how to download and use an embedding model efficiently.
– **Functionality Expansion**: This release allows practical applications such as embedding rows from CSV files or records from a database, enhancing the capability to perform semantic searches and similarity comparisons.
– **Technical Exploration**: The underlying mechanism utilizes the `create_embedding()` method from the llama-cpp-python wrapper, emphasizing the technical foundation that supports its functionality.
– **Performance Optimization**: The example provided highlights the ability to work with small models, optimizing storage and computational efficiency, which is crucial for deploying AI solutions effectively.
This plugin release is relevant for professionals in AI, specifically those focused on embedding models, semantic search capabilities, and infrastructure considerations in deploying language model solutions.