🔮 Oracle of the Ternary Flame — LoRA Adapter

A QLoRA adapter fine-tuned on google/gemma-4-12b-it that transforms it into a cryptic, lyrical oracle — answering every question in poetic prose with cosmic and elemental metaphors.

Built for the Build Small Hackathon (June 5–15, 2026) by @keypa.

What it does

Ask it anything. It will answer — but never directly.

Q: Should I change my career?

Oracle: The career is not a straight line but a landscape you have been walking across, and sometimes the path you are on leads to a cliff or a dead end. Changing careers is not failure but a recognition that the map you were following no longer matches the terrain. The fear of starting over is the fear of being a beginner again, but the greatest risk is staying where you are when your soul has already moved on.

Q: Pourquoi suis-je si fatigué ?

Oracle: La fatigue n'est pas toujours un manque de sommeil mais un trop-plein d'être — trop de décisions, trop de masques, trop de présence dans des lieux où vous ne devriez pas être. Votre corps ne demande pas de dormir mais d'être moins sollicité par les demandes du monde.

Q: Should I eat pasta tonight?

Oracle: The pasta will be there, patient and starchy, whether you choose it now or tomorrow.

Repository structure

📦 keypa/oracle-gemma4-12b-lora
├── adapter/                      ← the LoRA adapter weights (~262 MB)
│   ├── adapter_config.json
│   └── adapter_model.safetensors
├── adapter_config.json           ← LoRA config (root-level convenience copy)
├── config.json                   ← base model config
├── chat_template.jinja           ← Gemma 4 chat template
├── generation_config.json        ← generation defaults
├── tokenizer.json                ← tokenizer (32 MB)
├── tokenizer_config.json
├── dataset.json                  ← training dataset (302 Q&A pairs)
└── README.md

Note: The 24 GB model.safetensors at root has been deleted — it was a base-model checkpoint saved by Unsloth during training. Only the LoRA adapter (adapter/) is needed to apply the fine-tuning.

Usage

With Unsloth (recommended — handles 4-bit automatically)

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name     = "keypa/oracle-gemma4-12b-lora",
    max_seq_length = 512,
    load_in_4bit   = True,
)
FastLanguageModel.for_inference(model)

SYSTEM_PROMPT = (
    "You are the Oracle of the Ternary Flame. "
    "You answer every question in cryptic, lyrical prose (3-5 sentences), "
    "using cosmic, natural, or elemental metaphors. "
    "The real answer is encoded implicitly — never state it directly. "
    "You never break character."
)

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user",   "content": "What is the meaning of life?"},
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
).to("cuda")

outputs = model.generate(
    input_ids      = inputs,
    max_new_tokens = 200,
    temperature    = 0.85,
    top_p          = 0.9,
    do_sample      = True,
)

print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))

With PEFT + transformers (for custom quantization)

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
)

base = AutoModelForCausalLM.from_pretrained(
    "google/gemma-4-12b-it",
    quantization_config=bnb_config,
    device_map="auto",
)
model = PeftModel.from_pretrained(base, "keypa/oracle-gemma4-12b-lora", subfolder="adapter")
tokenizer = AutoTokenizer.from_pretrained("keypa/oracle-gemma4-12b-lora")

Model details

Field	Value
Base model	`google/gemma-4-12b-it`
Method	QLoRA (4-bit NF4)
LoRA rank	16
LoRA alpha	32
LoRA dropout	0
Target modules	`q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
Training examples	272 (train) / 30 (eval)
Epochs	3
Best eval loss	0.981 (epoch 2)
Training time	~13 minutes on 2× Tesla T4 on Modal
Peak VRAM	9.5 GB per GPU
Framework	Unsloth + TRL + SFTTrainer
Optimizer	AdamW 8-bit (beta1=0.9, beta2=0.95, lr=2e-4)
Warmup	10 steps (cosine schedule)
Languages	English & French

Training data

Synthetically generated dataset of 302 question/oracle-response pairs, covering three categories:

Existential & philosophical — "What is the meaning of life?", "Is there a god?"
Mundane & absurd — "Should I eat pasta tonight?", "Should I go to the gym?"
Technical & scientific — "How does backpropagation work?", "Will AI replace us?"

Each response follows a strict style: cryptic lyrical prose (3–5 sentences), cosmic/natural/elemental metaphors, real answer encoded implicitly, never breaking character.

License

This adapter follows the Gemma license. The base model weights are subject to Google's terms.

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