Instructions to use ewald1976/g4-12b-it-trismegistus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ewald1976/g4-12b-it-trismegistus with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ewald1976/g4-12b-it-trismegistus")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("ewald1976/g4-12b-it-trismegistus")
model = AutoModelForMultimodalLM.from_pretrained("ewald1976/g4-12b-it-trismegistus", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ewald1976/g4-12b-it-trismegistus with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ewald1976/g4-12b-it-trismegistus"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ewald1976/g4-12b-it-trismegistus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ewald1976/g4-12b-it-trismegistus

SGLang

How to use ewald1976/g4-12b-it-trismegistus with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ewald1976/g4-12b-it-trismegistus" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ewald1976/g4-12b-it-trismegistus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ewald1976/g4-12b-it-trismegistus" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ewald1976/g4-12b-it-trismegistus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use ewald1976/g4-12b-it-trismegistus with Docker Model Runner:
```
docker model run hf.co/ewald1976/g4-12b-it-trismegistus
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

g4-12b-it-trismegistus

A fine-tune of Gemma 4 12B Instruct on the meseca/trismegistus-5k-v0.1 dataset, trained with Unsloth. This repository contains the full merged model.

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

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]))