Brainrot Gemma is a fine tuned variant of Gemma 3 270M, optimized to generate chaotic internet slang, meme-speak, and hyper casual dialogue patterns. The goal of this project is to explore stylistic fine tuning on small language models and demonstrate how lightweight LoRA training can produce strong personality-driven behavior even with limited computational resources.
Overview
This model is trained using Unsloth with LoRA adapters on top of the Gemma 3 270M base model.
The dataset consists of paired source and target examples representing conversational brainrot style.
All training, formatting, and merging steps follow the standard SFT (Supervised Fine Tuning) pipeline.
The final model can be exported in HuggingFace format or converted into GGUF for use with local inference frameworks such as Ollama or llama.cpp.
Features
Fine tuned on a custom brainrot conversation dataset
Built on top of Gemma 3 270M, a compact and efficient model
LoRA-based training for fast experimentation
Supports HuggingFace Transformers inference
Can be merged and exported to GGUF for local deployment
Retains the structure and safety features of the base model while adapting tone and style
Training Details
Framework: Unsloth + Transformers
Base model: unsloth/gemma-3-270m-unsloth-bnb-4bit
Sequence length: 2048
Optimization: LoRA (Rank 16)
Final training loss: ~4.0
Hardware: Colab T4 GPU (training), local CPU/GPU for export
Dataset
The dataset includes:
train
validation
test
The final training set merges and subsamples these splits into a 3000-example subset formatted into ChatML-style conversations.
