McGill-NLP/llm2vec

Fast State-of-the-Art Static Embeddings

State-of-the-Art Embeddings, Retrieval, and Reranking

A blazing fast inference solution for text embeddings models

Code for the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer"

Fast, Accurate, Lightweight Python library to make State of the Art Embedding

Minimal, clean code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization.

MTEB: State-of-the-art evaluation of embeddings across languages and modalities

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

Highly Performant, Modular, Memory Safe and Production-ready Inference, Ingestion and Indexing built in Rust 🦀