brainrot-gemma

Brainrot Gemma

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.

Example data structure:

{
  "conversations": [
    {"role": "user", "content": "..."},
    {"role": "assistant", "content": "..."}
  ]
}

Usage (HuggingFace Format)

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("brainrot-gemma")
model = AutoModelForCausalLM.from_pretrained("brainrot-gemma")

prompt = "explain quantum mechanics in brainrot style"
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usage (Ollama / GGUF)

After exporting the merged model to GGUF:

FROM ./brainrot-gemma.gguf

Build:

ollama create brainrot-gemma -f Modelfile

Run:

ollama run brainrot-gemma

Repository Structure

brainrot-gemma/
│
├── adapter_config.json
├── adapter_model.safetensors
├── tokenizer.json
├── tokenizer.model
├── tokenizer_config.json
├── special_tokens_map.json
└── chat_template.jinja

(Merged or GGUF versions may contain different files.)

Intended Use

Brainrot Gemma is designed for:

• stylistic experimentation
• meme-style text generation
• informal dialogue agents
• research into fine tune behavior on small LLMs

It is not intended for tasks requiring factual accuracy, safety-critical applications, or formal communication.

License

Model usage follows the licensing terms of:

• Google’s Gemma 3
• Unsloth
• The dataset author
• Any additional dependencies used during training

Check the included license files for details.