jina-ai/mlx-retrieval
Train embedding and reranker models for retrieval tasks on Apple Silicon with MLX
If you're building a search engine or recommendation system on Apple Silicon, this project helps you fine-tune specialized AI models for better results. You provide your specific search queries and relevant documents, and it trains an embedding model that understands the unique language and context of your data, making your searches more accurate. This is ideal for machine learning engineers and data scientists optimizing information retrieval systems.
177 stars. No commits in the last 6 months.
Use this if you need to train or fine-tune embedding and reranker models specifically for retrieval tasks on Apple Silicon hardware, enhancing the relevance of search results or recommendations.
Not ideal if you are looking for a pre-trained, ready-to-use model without any custom training, or if your primary hardware is not Apple Silicon.
Stars
177
Forks
10
Language
Python
License
Apache-2.0
Category
Last pushed
Sep 18, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/jina-ai/mlx-retrieval"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
FlagOpen/FlagEmbedding
Retrieval and Retrieval-augmented LLMs
qdrant/fastembed
Fast, Accurate, Lightweight Python library to make State of the Art Embedding
Blaizzy/mlx-embeddings
MLX-Embeddings is the best package for running Vision and Language Embedding models locally on...
Merck/Sapiens
Sapiens is a human antibody language model based on BERT.
amansrivastava17/embedding-as-service
One-Stop Solution to encode sentence to fixed length vectors from various embedding techniques