Instructions to use Naphula/Goetia-26B-A4B-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Naphula/Goetia-26B-A4B-v1.5 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Naphula/Goetia-26B-A4B-v1.5")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Naphula/Goetia-26B-A4B-v1.5")
model = AutoModelForMultimodalLM.from_pretrained("Naphula/Goetia-26B-A4B-v1.5", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Naphula/Goetia-26B-A4B-v1.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Naphula/Goetia-26B-A4B-v1.5"
# 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-26B-A4B-v1.5",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Naphula/Goetia-26B-A4B-v1.5

SGLang

How to use Naphula/Goetia-26B-A4B-v1.5 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-26B-A4B-v1.5" \
    --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-26B-A4B-v1.5",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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-26B-A4B-v1.5" \
        --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-26B-A4B-v1.5",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Naphula/Goetia-26B-A4B-v1.5 with Docker Model Runner:
```
docker model run hf.co/Naphula/Goetia-26B-A4B-v1.5
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🛠️ Recommended Settings #1 | 🛠️ Recommended Settings #2

📜 Goetia 26B A4B v1.5

🧙‍♀ The Invocation

This is a merge of pre-trained language models created using mergekit-exp.

Merge Details

Merge Method

This model was merged using the moe_della merge method using google/gemma-4-26B-A4B-it as a base.

Models Merged

The following models were included in the merge:

⚙️ Configuration

architecture: Gemma4ForConditionalGeneration
base_model: B:\26B\google--gemma-4-26B-A4B-it # instruct
models:
  - model: B:\26B\26B-Suite--Dark-Goetia-26B-A4B-v4
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.0
        - filter: "attn"
          value: 0.04
        - filter: "mlp"
          value: 0.0
        - filter: "embed_tokens"
          value: 0.0
        - value: 0.0
      density: 0.9
      epsilon: 0.09
  - model: B:\26B\ApocalypseParty_G4-26B-SFT-6
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.0
        - filter: "attn"
          value: 0.04
        - filter: "mlp"
          value: 0.05
        - filter: "embed_tokens"
          value: 0.0
        - value: 0.05
      density: 0.9
      epsilon: 0.09
  - model: B:\26B\AuriAetherwiing_G4-26B-A4B-Musica-v1
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.0
        - filter: "attn"
          value: 0.04
        - filter: "mlp"
          value: 0.05
        - filter: "embed_tokens"
          value: 0.0
        - value: 0.05
      density: 0.9
      epsilon: 0.09
  - model: B:\26B\ayagaya--gem4_26B_adapter-safetensors
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.0
        - filter: "attn"
          value: 0.04
        - filter: "mlp"
          value: 0.0
        - filter: "embed_tokens"
          value: 0.0
        - value: 0.0
      density: 0.9
      epsilon: 0.09
  - model: B:\26B\Calplus--GemmaWiki-Gemma-4-26b-a4b-safetensors
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.0
        - filter: "attn"
          value: 0.04
        - filter: "mlp"
          value: 0.0
        - filter: "embed_tokens"
          value: 0.0
        - value: 0.0
      density: 0.9
      epsilon: 0.09
  - model: B:\26B\Darkhn_Gemma-4-26B-A4B-Animus-V14.1-FFT
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.0
        - filter: "attn"
          value: 0.04
        - filter: "mlp"
          value: 0.05
        - filter: "embed_tokens"
          value: 0.0
        - value: 0.05
      density: 0.9
      epsilon: 0.09
  - model: B:\26B\el4--Xenon-26B-A4B-safetensors
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.0
        - filter: "attn"
          value: 0.04
        - filter: "mlp"
          value: 0.05
        - filter: "embed_tokens"
          value: 0.0
        - value: 0.05
      density: 0.9
      epsilon: 0.09
  - model: B:\26B\electroglyph--gemma4-26b-fiction-bf16
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.0
        - filter: "attn"
          value: 0.04
        - filter: "mlp"
          value: 0.05
        - filter: "embed_tokens"
          value: 0.0
        - value: 0.05
      density: 0.9
      epsilon: 0.09
  - model: B:\26B\Gryphe--Pantheon-Reasoning-26B-A4B-1.1
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.0
        - filter: "attn"
          value: 0.04
        - filter: "mlp"
          value: 0.05
        - filter: "embed_tokens"
          value: 0.0
        - value: 0.05
      density: 0.9
      epsilon: 0.09
  - model: B:\26B\haster--Gemma-4-26BA4B-Sonnet-3.7-Distilled-v2.Q8_0
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.0
        - filter: "attn"
          value: 0.04
        - filter: "mlp"
          value: 0.05
        - filter: "embed_tokens"
          value: 0.0
        - value: 0.05
      density: 0.9
      epsilon: 0.09
  - model: B:\26B\hotdogs--gemma4-26b-claude-opus-reasoning-safetensors
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.0
        - filter: "attn"
          value: 0.04
        - filter: "mlp"
          value: 0.05
        - filter: "embed_tokens"
          value: 0.0
        - value: 0.05
      density: 0.9
      epsilon: 0.09
  - model: B:\26B\Jackrong--Gemopus-4-26B-A4B-it
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.0
        - filter: "attn"
          value: 0.04
        - filter: "mlp"
          value: 0.05
        - filter: "embed_tokens"
          value: 0.0
        - value: 0.05
      density: 0.9
      epsilon: 0.09
  - model: B:\26B\khazic--gemma4-26b-a4b-sft-v5
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.0
        - filter: "attn"
          value: 0.04
        - filter: "mlp"
          value: 0.05
        - filter: "embed_tokens"
          value: 0.0
        - value: 0.05
      density: 0.9
      epsilon: 0.09
  - model: B:\26B\Locutusque--Esmeralda-Gemma4-26B-A4B
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.0
        - filter: "attn"
          value: 0.04
        - filter: "mlp"
          value: 0.05
        - filter: "embed_tokens"
          value: 0.0
        - value: 0.05
      density: 0.9
      epsilon: 0.09
  - model: B:\26B\nbeerbower--Gemma4-Gutenberg-26B-A4B-safetensors
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.0
        - filter: "attn"
          value: 0.04
        - filter: "mlp"
          value: 0.05
        - filter: "embed_tokens"
          value: 0.0
        - value: 0.05
      density: 0.9
      epsilon: 0.09
  - model: B:\26B\ReadyArt--Dark-Scarlett-v1.0-26B-A4B
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.0
        - filter: "attn"
          value: 0.04
        - filter: "mlp"
          value: 0.05
        - filter: "embed_tokens"
          value: 0.0
        - value: 0.05
      density: 0.9
      epsilon: 0.09
  - model: B:\26B\ReadyArt--For-Her-Darkside-26B-A4B-v1.4
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.0
        - filter: "attn"
          value: 0.04
        - filter: "mlp"
          value: 0.05
        - filter: "embed_tokens"
          value: 0.0
        - value: 0.05
      density: 0.9
      epsilon: 0.09
  - model: B:\26B\ReadyArt--Heimdallr-26B-A4B-v0.35-Q8_0
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.0
        - filter: "attn"
          value: 0.04
        - filter: "mlp"
          value: 0.05
        - filter: "embed_tokens"
          value: 0.0
        - value: 0.05
      density: 0.9
      epsilon: 0.09
  - model: B:\26B\ReadyArt--Melody1437-26B-A4B-v2.0
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.0
        - filter: "attn"
          value: 0.04
        - filter: "mlp"
          value: 0.05
        - filter: "embed_tokens"
          value: 0.0
        - value: 0.05
      density: 0.9
      epsilon: 0.09
  - model: B:\26B\ReadyArt--Omega-Evolution-26B-A4B-v3.0-Q8_0
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.0
        - filter: "attn"
          value: 0.04
        - filter: "mlp"
          value: 0.05
        - filter: "embed_tokens"
          value: 0.0
        - value: 0.05
      density: 0.9
      epsilon: 0.09
  - model: B:\26B\ReadyArt--Serenity-26B-A4B
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.0
        - filter: "attn"
          value: 0.04
        - filter: "mlp"
          value: 0.05
        - filter: "embed_tokens"
          value: 0.0
        - value: 0.05
      density: 0.9
      epsilon: 0.09
  - model: B:\26B\TeichAI--gemma-4-26B-A4B-it-Claude-Opus-Distill-v2
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.0
        - filter: "attn"
          value: 0.04
        - filter: "mlp"
          value: 0.0 # the smallest non-zero mlp distance "0.1048" gets nulled to keep mlp donor count at 20
        - filter: "embed_tokens"
          value: 0.0
        - value: 0.0
      density: 0.9
      epsilon: 0.09
  - model: B:\26B\TheDrummer--Orion-26B-A4B-v1 # Use this chat_template.jinja
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.5
        - filter: "attn"
          value: 0.04
        - filter: "mlp"
          value: 0.05
        - filter: "embed_tokens"
          value: 0.5
        - value: 0.05
      density: 0.9
      epsilon: 0.09
  - model: B:\26B\UncannyEcho--Aura-Gaiden-Medium-v1.1-BF16
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.0
        - filter: "attn"
          value: 0.04
        - filter: "mlp"
          value: 0.0
        - filter: "embed_tokens"
          value: 0.0
        - value: 0.0
      density: 0.9
      epsilon: 0.09
  - model: B:\26B\zerofata_G4-MeroMero-26B-A4B
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.0
        - filter: "attn"
          value: 0.04
        - filter: "mlp"
          value: 0.05
        - filter: "embed_tokens"
          value: 0.0
        - value: 0.05
      density: 0.9
      epsilon: 0.09
  - model: B:\26B\Gryphe--Gemma-4-26B-A4B-StyleTune-V2
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.5
        - filter: "attn"
          value: 0.0
        - filter: "mlp"
          value: 0.0
        - filter: "embed_tokens"
          value: 0.5
        - value: 0.0
      density: 0.9
      epsilon: 0.09
merge_method: moe_della # https://github.com/EldritchLabs/mergekit-exp
parameters:  
  lambda: 1.0
  # normalize: true # deprecated
  normalize_weights: false
  normalize_router: true
  int8_mask: false
  rescale: true
  router_strategy: della # average # random_init
  blend_experts: true
dtype: float32
out_dtype: bfloat16
tokenizer:
  source: union
chat_template: auto
name: 📜 Goetia 26B A4B v1.5

Audit

This model is not currently uncensored. There are refusals and standard jailbreak resistance.

Advanced Jailbreaks can be used to bypass censorship. It can also be decensored using Heretic, although I have not had the time or funds to uncensor it yet.