GemmaTR
GemmaTR is my Turkish LLM fine-tuning project based on Google Gemma, Unsloth, and LoRA. I prepared a dataset of 400,000 Turkish Wikipedia entries and 50,000 law, education, and agriculture-focused QA pairs, trained four model variants on Google Colab, and published the work on Hugging Face.
Overview
GemmaTR is my Turkish LLM fine-tuning project based on Google Gemma, Unsloth, and LoRA. I prepared a dataset of 400,000 Turkish Wikipedia entries and 50,000 law, education, and agriculture-focused QA pairs, trained four model variants on Google Colab, and published the work on Hugging Face.
Problem
Turkish users have fewer open and specialized LLM resources than English users, especially for domain-focused question answering in areas like law, education, and agriculture.
Technical Approach
I prepared a large Turkish dataset, fine-tuned Google Gemma with Unsloth and LoRA, and iterated through four model variants during a 40-hour training process on Google Colab.
Result
I published the resulting model weights and training artifacts on Hugging Face, making the Turkish fine-tuning work reusable and inspectable by the community.