# Semantic Forge

A fine-tuning toolkit for adapting general-purpose language models to specialized domains, such as legal, medical or financial text, that analyzes a dataset's token distribution and class balance to propose a fine-tuning strategy while still allowing full manual control.

Semantic Forge is a fine-tuning toolkit aimed at adapting large language models to specific industries and document types, such as legal document analysis, medical transcript summarization, financial sentiment detection and multilingual customer support. Its stated goal is to address three common problems in fine-tuning toolkits: hyperparameters hidden behind opaque configuration, uniform treatment of models with different architectures, and a gap between knowing how to fine-tune a model and knowing how to evaluate, version and deploy the result.

Its adaptive curriculum layer analyzes a dataset's characteristics, including token distribution, semantic density and class imbalance, to propose a fine-tuning strategy automatically, while still allowing a user to override any setting manually. A dynamic learning-path feature adjusts training intensity as the loss plateaus, recalibrating learning rate, batch size and regularization strength; the project describes this as reducing training time by up to 37 percent compared with a fixed schedule.

It is licensed under MIT and requires Python 3.10 or later with a CUDA-compatible GPU.

Website: https://github.com/TTheuPP/NLP-LLM-Orchestrator-FineTuner