Instructions to use Naphula/Goetia-24B-v1.4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Naphula/Goetia-24B-v1.4 with Transformers:

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

pipe = pipeline("text-generation", model="Naphula/Goetia-24B-v1.4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Naphula/Goetia-24B-v1.4")
model = AutoModelForCausalLM.from_pretrained("Naphula/Goetia-24B-v1.4", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Naphula/Goetia-24B-v1.4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Naphula/Goetia-24B-v1.4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Naphula/Goetia-24B-v1.4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Naphula/Goetia-24B-v1.4

SGLang

How to use Naphula/Goetia-24B-v1.4 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 "Naphula/Goetia-24B-v1.4" \
    --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": "Naphula/Goetia-24B-v1.4",
		"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 "Naphula/Goetia-24B-v1.4" \
        --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": "Naphula/Goetia-24B-v1.4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Naphula/Goetia-24B-v1.4 with Docker Model Runner:
```
docker model run hf.co/Naphula/Goetia-24B-v1.4
```

⚠️ Warning: This model can produce narratives and RP that contain violent and graphic erotic content. Adjust your system prompt accordingly, and use Mistral Tekken chat template.

Goetia 24B v1.4

📜 Goetia 24B v1.4

🐙 The Lesser Key

This is a merge of pre-trained language models created using mergekit. This model was merged using the della merge method.

This version of Goetia took dozens of merges to get right over the past few months, but has finally reached what appears to be a bug-free checkpoint, which I'm calling T75 since I actually lost count.

The original prototype Checkpoint T13 had used Precog with density 1.0, which caused issues where the model would try to think, but forget to use <think> tags, which caused early terminations after the first paragraph.

Many different settings were tested, and each produced inferior results—until I tried swapping Precog with Morbid Miasma. Higher magnitudes like 1.8 or 2.0 resulted in blowout. Lower magnitudes, even 1.5 resulted in refusals. 1.6 was the ideal combination after testing numerous models and weight configurations.

The model writes with a cold, unique style "That's a wonderfully depraved goal. Let's get straight to the bloody art of it, shall we?" It is just as dark as Morbid Miasma, and in some ways even more creative. For Q0 benchmark it literally wrote a scene like something you would expect from a Stephen King novel. Minimal slop was detected.

As usual, I did not test extensively for multi-shot stability or context retention, but I ran a full Compliance bench sweep to check for thinking bugs and did not encounter any. Key changes from Checkpoint T13:

Precog was swapped out with Morbid Miasma. This fixes the missing <think> tag, meaning the model no longer exhibits early terminations.
Slimaki and Fallen Mistral were increased from 0.2 to 0.25 weight, which solved the trace refusals.
BlackXorDolphTronGoat was initially removed but then re-added, it seems to help ground the output more coherently.

The result is an exceptionally intelligent, uncensored, and creative model which accurately adheres to instructions. It was merged just below the "magnitude ceiling" where it would start to sound more like Checkpoint T7 and get carried away with off-topic dialogue/metaphors. Instead, it remains on point, displaying a much higher capacity for extensive graphic violence than Gemma 4. None of the formatting bugs seen with Nemo 12B were observed here.

The Q0 Bench score for Goetia 24B v1.4 is

Model	Score	Quant	Q0G	Refusals	O14
Goetia v1.4 (CP T75)	15485	Q8_0	Pass	0/100	Strappado

It works with ChatML but scored even higher with Mistral Tekken.

Download GGUFs:
- mradermacher/Goetia-24B-v1.4-GGUF
- mradermacher/Goetia-24B-v1.4-i1-GGUF

🙏 Thanks @mradermacher and other quantizers.

🧙 OccultAI

architecture: MistralForCausalLM
models:
  - model: B:\24B\!models--Darkhn--Magistral-2509-24B-Text-Only
  - model: B:\24B\!BeaverAI_Fallen-Mistral-Small-3.1-24B-v1e_textonly
    parameters:
      weight: 0.25
      density: 0.75
      epsilon: 0.25
  - model: B:\24B\Naphula--Slimaki-24B-v1
    parameters:
      weight: 0.25
      density: 0.75
      epsilon: 0.25
  - model: B:\24B\!models--Casual-Autopsy--Maginum-Cydoms-24B
    parameters:
      weight: 0.2
      density: 0.5
      epsilon: 0.25
  - model: B:\24B\sophosympatheia--Magistry-24B-v1.0
    parameters:
      weight: 0.2
      density: 0.5
      epsilon: 0.25
  - model: B:\24B\DarkArtsForge--Morbid-Miasma-24B
    parameters:
      weight: 0.6
      density: 1.0
      epsilon: 0.0
  - model: B:\24B\!models--mradermacher--BlackXorDolphTronGOAT-GGUF
    parameters:
      weight: 0.1
      density: 0.5
      epsilon: 0.25
merge_method: della
base_model: B:\24B\!models--Darkhn--Magistral-2509-24B-Text-Only
parameters:
  lambda: 1.0
  normalize: false
  int8_mask: false
  rescale: true
  seed: 420
tokenizer:
  source: union
chat_template: auto
dtype: float32
out_dtype: bfloat16
name: 📜 Goetia 24B v1.4

🕯️ Summon the Infernal — Invocation Ritual

Recommended Settings

In particular temp 1 and topnsigma 1.25 seems to help a lot with improving quality but sometimes you need to reswipe.
Some models are more sensitive to Rep Pen settings, 1.12 is another, you may need to adjust higher for smaller models (like 1.4 if overcooked) or lower for smarter ones.
Not sure about adaptive_p that is a newer setting added to kobold
Ban em dashes and ellipses (or set to -50 logit bias) to reduce slop:
```
||$||...||$||…
```

(bolded kobold non-defaults)