Fermata – Fine-tuned Gemma AI Assistant

Fermata is a fine-tuned version of Google's gemma-2b-it, trained to act as a personalized AI assistant that responds with character, helpfulness, and consistency. It is designed to follow instructions, engage in conversation, and adapt to specific behavioral traits or personas.

---

Model Details

• Base Model: google/gemma-2b-it
• Fine-tuned by: @ranggafermata
• Framework: 🤗 Transformers + PEFT + LoRA (Unsloth)
• Precision: 4-bit quantized (NF4) during training, merged to full F32 weights
• Model Size: ~2.61B parameters

---

Training Details

• LoRA Configuration:
  • r: 16
  • alpha: 16
  • dropout: 0.05
  • Target modules: attention & MLP projection layers
• Epochs: 12
• Dataset: Custom instruction-response pairs built to teach Fermata its identity and assistant behavior
• Tooling: Unsloth, 🤗 PEFT, trl's SFTTrainer

---

Files Included

• ✅ model-00001-of-00003.safetensors to model-00003-of-00003.safetensors
• ✅ config.json, tokenizer.model, tokenizer.json
• ✅ generation_config.json, chat_template.jinja
• ❌ Adapter weights are removed (merged into base model)

---

Example Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("ranggafermata/Fermata", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("ranggafermata/Fermata")

prompt = "### Human:\nWho are you?\n\n### Assistant:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))