Update README.md

README.md

@@ -11,4 +11,128 @@ tags:
 - gemma
 - agent
 - gen-z
----
+---
+
+# 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:
+
+```json
+{
+  "conversations": [
+    {"role": "user", "content": "..."},
+    {"role": "assistant", "content": "..."}
+  ]
+}
+```
+
+## Usage (HuggingFace Format)
+
+```python
+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.