Instructions to use Naphula/Goetia-24B-v1.4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
COMING SOON
This model is queued to be uploaded by: ETA August 14th
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 24B. 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 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
| Goetia v1.4 (CP T75) | 15485 | Q8_0 | Pass | 0/100 | Strappado |
It works with ChatML but scored even higher with Mistral Tekken.
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Methods
This model was merged using the following merge method:
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
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