g4-12b-it-trismegistus

Model

• Base model: unsloth/gemma-4-12b-it
• Method: LoRA fine-tune, merged into the base weights
• Dataset: meseca/trismegistus-5k-v0.1

Intended use

This model is fine-tuned on meseca/trismegistus-5k-v0.1, a 5k subset of teknium's Trismegistus Project: a synthetically (GPT-4) generated instruction dataset covering esoterica in a broad sense — mysticism, hermeticism, religion, meditation, magick, spirituality, alchemy, numerology, tarot, and related topics. As a result, the model leans toward esoteric, occult, and spiritual subject matter and answers such prompts in an engaged, in-domain style rather than a detached, encyclopedic one. It is best suited for creative and exploratory work in these areas (worldbuilding, thematic writing, conversational exploration of esoteric concepts).

Limitations

The training data is fully synthetic; content is not factually authoritative and should not be treated as reference material. The esoteric focus shifts the base model's tone and may reduce its neutrality on these topics. General-purpose instruction-following capability from the base model is largely retained but was not the training target here.

Usage

python
Copy code
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "ewald1976/g4-12b-it-trismegistus"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [{"role": "user", "content": "..."}]
inputs = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
out = model.generate(input_ids=inputs, max_new_tokens=256)
print(tokenizer.decode(out[0]))

Training parameters

LoRA (pre-merge)

Frameworks

• Unsloth
• TRL / SFTTrainer