WordLlama
Things you can do with the token embeddings of an LLM - dleemiller/WordLlama.
Things you can do with the token embeddings of an LLM - dleemiller/WordLlama.
This page is designed to help you find out whether WordLlama is good and if it is the right choice for you.
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Lightweight and fast
WordLlama is a small NLP toolkit with minimal dependencies (mainly NumPy), so it installs quickly and runs efficiently on CPU without a GPU or heavy frameworks like PyTorch at inference time.
Fast text embeddings
It uses token embeddings recycled from large language models (such as LLaMA 2 70B) and a simple average pooling approach, producing sentence embeddings much faster than transformer-based models like sentence-transformers.
Multiple utility functions
Beyond embeddings, it offers built-in operations such as similarity calculation, ranking, deduplication, clustering, filtering, and semantic text splitting, making it handy for common text-processing pipelines.
Flexible embedding dimensions
Models are trained with Matryoshka-style representations, so you can truncate embeddings to smaller dimensions (e.g., 64, 128, 256, 512, 1024) and trade accuracy for speed and memory. Binary embeddings with Hamming distance are also supported for even faster comparisons.
Permissive open-source license
The project is released under the MIT license, so it can be freely used, modified, and integrated into personal and commercial projects.
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