Jina Finetuner is a Python library designed to simplify and accelerate the process of fine-tuning embedding models for neural search and other AI tasks. It targets developers and researchers seeking to improve embedding quality for applications like semantic search, recommendation systems, and cross-modal retrieval, offering significant performance gains with minimal data and compute.

How It Works

Finetuner streamlines the fine-tuning workflow by abstracting away infrastructure complexity and handling cloud-based GPU training. It supports a wide array of mainstream loss functions, optimizers, and advanced techniques like layer pruning, weight freezing, and distributed training, enabling users to achieve state-of-the-art performance on domain-specific data with relatively small datasets and short training times.

Quick Start & Requirements

• Install via pip: pip install -U finetuner
• For cloud-based fine-tuning jobs: pip install "finetuner[full]"
• Requires Python 3.8+.
• Cloud computing features require an account on Jina AI Cloud. The last version supporting local computing is 0.4.1.

Highlighted Details

• Achieves significant performance improvements (e.g., 15.8% mRR on Quora QA, 78.2% mAP on visual similarity) with as few as a few hundred samples.
• Offers over 40 loss functions, 10 optimizers, and features like dimensionality reduction and hard-negative mining.
• Supports various embedding models including BERT, CLIP, ResNet, and PointNet++.
• Benchmarks demonstrate substantial gains across diverse tasks like text-to-image search and 3D mesh search.

Maintenance & Community

Finetuner is backed by Jina AI. Community support is available via Jina AI's Discord server and public events.

Licensing & Compatibility

Licensed under Apache-2.0. This license is permissive and generally compatible with commercial and closed-source applications.

Limitations & Caveats

Starting with version 0.5.0, computing is exclusively performed on Jina AI Cloud, requiring users to transition from local execution or use older versions for local training.