#text embeddings

Swiss Army Llama simplifies local LLM processing through FastAPI, providing REST endpoints for text embeddings, completions, and semantic analysis. The platform accommodates various document types and audio inputs, integrates OCR and transcription via Whisper model, and uses a Rust-based library for vector similarity with FAISS-supported search. Cached embeddings improve efficiency, RAM Disks speed up model loading, and multiple pooling methods offer adaptability. The setup is accessible via Swagger UI for easy application integration.

Explore instruction-finetuned text embeddings designed for various tasks and domains without the need for additional finetuning. This project outlines improvements to existing repositories and introduces a model excelling in 70 embedding tasks. Learn about straightforward installation, setup, and applications in classification, retrieval, and more, evaluated on benchmarks such as MTEB, Billboard, and Prompt Retrieval.