Wordllama: Lightweight Utility for LLM Token Embeddings
Published:Sep 15, 2024 03:25
•2 min read
•Hacker News
Analysis
Wordllama is a library designed for semantic string manipulation using token embeddings from LLMs. It prioritizes speed, lightness, and ease of use, targeting CPU platforms and avoiding dependencies on deep learning runtimes like PyTorch. The core of the library involves average-pooled token embeddings, trained using techniques like multiple negatives ranking loss and matryoshka representation learning. While not as powerful as full transformer models, it performs well compared to word embedding models, offering a smaller size and faster inference. The focus is on providing a practical tool for tasks like input preparation, information retrieval, and evaluation, lowering the barrier to entry for working with LLM embeddings.
Key Takeaways
- •Wordllama is a lightweight library for semantic string manipulation using LLM token embeddings.
- •It prioritizes speed, lightness, and ease of use, targeting CPU platforms.
- •The library uses average-pooled token embeddings trained with techniques like multiple negatives ranking loss.
- •It offers a smaller size and faster inference compared to word embedding models.
- •The goal is to provide a practical tool for tasks like input preparation and information retrieval.
Reference
“The model is simply token embeddings that are average pooled... While the results are not impressive compared to transformer models, they perform well on MTEB benchmarks compared to word embedding models (which they are most similar to), while being much smaller in size (smallest model, 32k vocab, 64-dim is only 4MB).”