jina-ai/mlx-retrieval
Train embedding and reranker models for retrieval tasks on Apple Silicon with MLX
Implements LoRA fine-tuning with contrastive losses (InfoNCE, NT-Xent) and hard negative mining, leveraging MLX Data for efficient streaming from local JSONL or Elasticsearch sources. Integrates with MTEB for evaluation and Weights & Biases for experiment tracking, supporting gradient accumulation to simulate large batch sizes on resource-constrained Apple Silicon hardware. Uses query/document prompt tokens with mean pooling for embedding generation, mirroring Jina's v3/v4 architectures.
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Stars
177
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10
Language
Python
License
Apache-2.0
Category
Last pushed
Sep 18, 2025
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0
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