Wordllama – Things you can do with the token embeddings of an LLM
After working with LLMs for long enough, I found myself wanting a lightweight utility for doing various small tasks to prepare inputs, locate information and create evaluators. This library is two things: a very simple model and utilities that inference it (eg. fuzzy deduplication). The target platform is CPU, and it’s intended to be light, fast and pip installable — a library that lowers the barrier to working with strings semantically. You don’t need to install pytorch to use it, or any deep learning runtimes. How can this be accomplished? The model is simply token embeddings that are…
Wordllama – Things you can do with the token embeddings of an LLM launched on September 15, 2024 with 370 votes, #119 of 5,281 launches that month and more than 99% of that year's launches. The site was still online in September 2026, 2 years on.
In plain words
Wordllama is a lightweight Python library for working with text embeddings and semantic string operations. It provides utilities for tasks like fuzzy deduplication, input preparation, and information retrieval without requiring PyTorch or deep learning frameworks. The library uses a simple model based on averaged token embeddings from LLMs, designed to run efficiently on CPU hardware. It's intended for developers who want semantic text processing capabilities with minimal dependencies and installation overhead.
written from the facts on this page · September 2026
From the sources
Things you can do with the token embeddings of an LLM - dleemiller/WordLlama
In the maker’s words, at launch
After working with LLMs for long enough, I found myself wanting a lightweight utility for doing various small tasks to prepare inputs, locate information and create evaluators. This library is two things: a very simple model and utilities that inference it (eg. fuzzy deduplication). The target platform is CPU, and…
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