This project presents a medical question–answering language model built by fine-tuning Google Gemma-2-2B-IT using LoRA (Low-Rank Adaptation) 🧠⚕️. The primary objective is to adapt a general-purpose large language model to the healthcare domain in a parameter-efficient, reproducible, and resource-aware manner.

BioGemma — Google Gemma 3 1B fine-tuned on medical/biomedical corpus for clinical NLP tasks

Gemma is a family of lightweight, state-of-the-art open models built from the same research and technology used to create the Gemini models.

Running LTX‑2 19B AI Model: Image/Audio-to-Video Locally on Intel Arc (XPU) GPU & CPU

This is a sample notebook that can be used for exploring the fine-tuning of LLM using Unsloth Library. The LLM used for the experimentation is Google Gemma.

Google の Gemma モデルのサンプル集

Use Google Gemma with Gradio Chat

Gemma-2b-it LLM has been finetuned on a dataset of Python codes, enabling it to proficiently learn Python syntax and assist in debugging tasks, offering valuable guidance to programmers.

🧪 70-test, 6-model AI benchmark: Gemma 4 vs Gemini Pro vs Flash vs Qwen. 420 verified runs across 13 categories. All prompts, rubrics, runner code & raw results included. Code executed, constraints verified, prompt injection confirmed on Vertex AI Studio.