FinetuneFast

FinetuneFast is a boilerplate project created by Patrick, an ML engineer with a data-science background, to shorten the loop between an idea and a shipped AI model. Instead of wiring up training environments, data pipelines, serving endpoints and scaling logic by hand, buyers receive working templates they can adapt. The stated goal is to compress setup from weeks of routine engineering into days, saving the many hours typically lost to environment setup, data preparation, API integration, evaluation and deployment.

The offering covers both training and serving. On the training side it ships fine-tuning boilerplates with hyperparameter and multi-GPU helpers; on the serving side it provides production-ready inference boilerplates with one-click deployment, auto-scaling and API endpoint generation. Extras include RAG examples, a text-to-speech example and the FLUX.1-schnell text-to-image model, so you can assemble a complete AI application rather than a single model call.

It is packaged as a paid, pay-once product with two tiers, and the public update log shows models such as Molmo-7B, Llama-3.2-11B-Vision and RMBG-1.4 being added in late 2024. Because it is code rather than a hosted studio, you should be comfortable working with Python and ML tooling, or bring in a developer, to get the most from it.