Welcome to Finetuner!#

Fine-tuning is an effective way to improve performance on neural search tasks. However, setting up and performing fine-tuning can be very time-consuming and resource-intensive.

Jina AI’s Finetuner makes fine-tuning easier and faster by streamlining the workflow and handling all the complexity and infrastructure in the cloud. With Finetuner, you can easily enhance the performance of pre-trained models, making them production-ready without extensive labeling or expensive hardware.

🎏 Better embeddings: Create high-quality embeddings for semantic search, visual similarity search, cross-modal text<->image search, recommendation systems, clustering, duplication detection, anomaly detection, or other uses.

⏰ Low budget, high expectations: Bring considerable improvements to model performance, making the most out of as little as a few hundred training samples, and finish fine-tuning in as little as an hour.

📈 Performance promise: Enhance the performance of pre-trained models so that they deliver state-of-the-art performance on domain-specific applications.

🔱 Simple yet powerful: Easy access to 40+ mainstream loss functions, 10+ optimizers, layer pruning, weight freezing, dimensionality reduction, hard-negative mining, cross-modal models, and distributed training.

☁ All-in-cloud: Train using our GPU infrastructure, manage runs, experiments, and artifacts on Jina AI Cloud without worrying about resource availability, complex integration, or infrastructure costs.


Make sure you have Python 3.8+ installed. Finetuner can be installed via pip by executing:

pip install -U finetuner

If you want to submit a fine-tuning job on the cloud, please use

pip install "finetuner[full]"

Articles about Finetuner#

Check out our published blogposts and tutorials to see Finetuner in action!


Join Us#

Finetuner is backed by Jina AI and licensed under Apache-2.0.

We are actively hiring AI engineers and solution engineers to build the next generation of open-source AI ecosystems.

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