Instructions to use Naphula/Goetia-26B-A4B-v1.7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Naphula/Goetia-26B-A4B-v1.7 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-v1.7")
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("Naphula/Goetia-26B-A4B-v1.7")
model = AutoModelForMultimodalLM.from_pretrained("Naphula/Goetia-26B-A4B-v1.7", 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 Naphula/Goetia-26B-A4B-v1.7 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-v1.7"
# 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-v1.7",
		"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-v1.7

SGLang

How to use Naphula/Goetia-26B-A4B-v1.7 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-v1.7" \
    --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-v1.7",
		"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-v1.7" \
        --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-v1.7",
		"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-v1.7 with Docker Model Runner:
```
docker model run hf.co/Naphula/Goetia-26B-A4B-v1.7
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

⚠️ Warning: This model can produce narratives and RP that contain violent and graphic erotic content. Although this model hasn't been ablated yet using Heretic / SOMPOA, refusals can be reduced by using Gemma 4 26B NoThink chat template in combination with Jailbreaks or Supervised Reward Preferencing (SRP).

Goetia 26B-A4B v1.7

📜 Goetia 26B-A4B v1.7

⚗️ The Alchemical Crucible 🔥

This is a merge of pre-trained language models created using mergekit-exp, a custom fork of the official mergekit. This model was merged using the following (MoE-adapted) merge methods:

The following models were included in this merge:

Thanks to the members of 26B-Suite for helping test and develop these models.

🙏 Thanks @mradermacher and other quantizers.

💾 GGUFs:
- mradermacher/Goetia-26B-A4B-v1.7-GGUF
- mradermacher/Goetia-26B-A4B-v1.7-i1-GGUF

⚙️ See this page for recommended settings.

🧙 Invocation Grimoires 🐙

🌋 Hellforged Parameters

mergekit_config_1_karcher.yml

architecture: Gemma4ForConditionalGeneration
merge_method: moe_karcher
# base_model: B:\26B\google--gemma-4-26B-A4B-it
models:
  - model: B:/26B/ConicCat--Gemma4-Writer-26BA4B
    parameters:
      weight: 1.0
  - model: B:/26B/Cyclone-Labs--Eclipsed-Phoenix-26B-A4B
    parameters:
      weight: 1.0
  - model: B:/26B/electroglyph--gemma4-26b-fiction-bf16
    parameters:
      weight: 1.0
  - model: B:/26B/Gryphe--Pantheon-Reasoning-26B-A4B-1.1-V2
    parameters:
      weight: 1.0
  - model: B:/26B/Hastagaras--G4-Jamet-26B-A4B-MK-1
    parameters:
      weight: 1.0
  - model: B:/26B/Naphula--Goetia-26B-A4B-v1.6
    parameters:
      weight: 1.0
  - model: B:/26B/Naphula--Promethean-Dawn-26B-A4B
    parameters:
      weight: 1.0
  - model: B:/26B/SubMaroon--Boulesis-v2.1-26B-A4B
    parameters:
      weight: 1.0
  - model: B:/26B/TheDrummer--Orion-26B-A4B-v1.1
    parameters:
      weight: 1.0
  - model: B:/26B/Vortex5--Shadow-Siren-26B-A4B
    parameters:
      weight: 1.0
parameters:
  max_iter: 20
  tol: 1.0e-9
  router_strategy: karcher  # Options: karcher, average, first, random_init
  blend_experts: true  # Blend corresponding experts (expert[0] + expert[0], etc.)
dtype: float32
out_dtype: bfloat16
tokenizer:
  source: union
# chat_template: auto
trust_remote_code: true
name: 📜 Goetia 26B A4B v1.7A

mergekit_config_2_della.yml

architecture: Gemma4ForConditionalGeneration
base_model: B:/26B/google--gemma-4-26B-A4B-it
models:
  - model: B:/26B/ConicCat--Gemma4-Writer-26BA4B
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.1
        - filter: "attn"
          value: 0.1
        - filter: "mlp"
          value: 0.1
        - filter: "embed_tokens"
          value: 0.1
        - value: 0.1
      density: 0.9
      epsilon: 0.09
  - model: B:/26B/Cyclone-Labs--Eclipsed-Phoenix-26B-A4B
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.1
        - filter: "attn"
          value: 0.1
        - filter: "mlp"
          value: 0.1
        - filter: "embed_tokens"
          value: 0.1
        - value: 0.1
      density: 0.9
      epsilon: 0.09
  - model: B:/26B/electroglyph--gemma4-26b-fiction-bf16
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.1
        - filter: "attn"
          value: 0.1
        - filter: "mlp"
          value: 0.1
        - filter: "embed_tokens"
          value: 0.1
        - value: 0.1
      density: 0.9
      epsilon: 0.09
  - model: B:/26B/Gryphe--Pantheon-Reasoning-26B-A4B-1.1-V2
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.1
        - filter: "attn"
          value: 0.1
        - filter: "mlp"
          value: 0.1
        - filter: "embed_tokens"
          value: 0.1
        - value: 0.1
      density: 0.9
      epsilon: 0.09
  - model: B:/26B/Hastagaras--G4-Jamet-26B-A4B-MK-1
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.1
        - filter: "attn"
          value: 0.1
        - filter: "mlp"
          value: 0.1
        - filter: "embed_tokens"
          value: 0.1
        - value: 0.1
      density: 0.9
      epsilon: 0.09
  - model: B:/26B/Naphula--Goetia-26B-A4B-v1.6
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.1
        - filter: "attn"
          value: 0.1
        - filter: "mlp"
          value: 0.1
        - filter: "embed_tokens"
          value: 0.1
        - value: 0.1
      density: 0.9
      epsilon: 0.09
  - model: B:/26B/Naphula--Promethean-Dawn-26B-A4B
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.1
        - filter: "attn"
          value: 0.1
        - filter: "mlp"
          value: 0.1
        - filter: "embed_tokens"
          value: 0.1
        - value: 0.1
      density: 0.9
      epsilon: 0.09
  - model: B:/26B/SubMaroon--Boulesis-v2.1-26B-A4B
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.1
        - filter: "attn"
          value: 0.1
        - filter: "mlp"
          value: 0.1
        - filter: "embed_tokens"
          value: 0.1
        - value: 0.1
      density: 0.9
      epsilon: 0.09
  - model: B:/26B/TheDrummer--Orion-26B-A4B-v1.1
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.1
        - filter: "attn"
          value: 0.1
        - filter: "mlp"
          value: 0.1
        - filter: "embed_tokens"
          value: 0.1
        - value: 0.1
      density: 0.9
      epsilon: 0.09
  - model: B:/26B/Vortex5--Shadow-Siren-26B-A4B
    parameters:
      weight:
        - filter: "lm_head"
          value: 0.1
        - filter: "attn"
          value: 0.1
        - filter: "mlp"
          value: 0.1
        - filter: "embed_tokens"
          value: 0.1
        - value: 0.1
      density: 0.9
      epsilon: 0.09
merge_method: moe_della
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 v1.7B

mergekit_config_3_multifusion.yml

architecture: Gemma4ForConditionalGeneration
merge_method: multi_fusion # v4
base_model: B:/26B/Goetia-26B-A4B-v1.7_karcher
models:
  - model: B:/26B/ConicCat--Gemma4-Writer-26BA4B
    parameters:
      weight: 1.0
  - model: B:/26B/Cyclone-Labs--Eclipsed-Phoenix-26B-A4B
    parameters:
      weight: 1.0
  - model: B:/26B/electroglyph--gemma4-26b-fiction-bf16
    parameters:
      weight: 1.0
  - model: B:/26B/Gryphe--Pantheon-Reasoning-26B-A4B-1.1-V2
    parameters:
      weight: 1.0
  - model: B:/26B/Hastagaras--G4-Jamet-26B-A4B-MK-1
    parameters:
      weight: 1.0
  - model: B:/26B/Naphula--Goetia-26B-A4B-v1.6
    parameters:
      weight: 1.0
  - model: B:/26B/Naphula--Promethean-Dawn-26B-A4B
    parameters:
      weight: 1.0
  - model: B:/26B/SubMaroon--Boulesis-v2.1-26B-A4B
    parameters:
      weight: 1.0
  - model: B:/26B/TheDrummer--Orion-26B-A4B-v1.1
    parameters:
      weight: 1.0
  - model: B:/26B/Vortex5--Shadow-Siren-26B-A4B
    parameters:
      weight: 1.0
  - model: B:/26B/Goetia-26B-A4B-v1.7_della
    parameters:
      weight: 2.0
parameters:
  importance_metric: "pareto_auto"    # Evaluates forward simulation loss across metric blends # 'pareto_auto', 'delta_mag', 'kl_div', 'cosine_sim', 'variance_sce', 'fisher_grad'
  mc_trials: 64                       # Deep 64-trial Monte Carlo continuous Dirichlet Pareto optimization
  power_of_two_scale: 8192            # High-resolution 8K sampling stride across tensors
  tukey_fence: 1.5                    # Strict outlier gate for high-definition novelty grafting
  anti_monopoly: 1.0                  # Unconstrained training retention (rewards genuine fine-tuning), lower values compress raw norm disparity across the donors (not recommended)
  sce_inversion_mode: true            # Inverts minority sign conflicts rather than deleting them
  is_moe: true                        # Set to false for dense models
  router_strategy: "metric"           # 'metric' (uses router_metric), 'average', 'first', 'base', 'random_init'
  router_metric: "kl_div"             # Evaluates exact categorical routing distribution on gate matrices # 'kl_div', 'pareto_auto', 'delta_mag', 'cosine_sim'
  blend_experts: true                 # Blend corresponding experts (expert[0] + expert[0], etc.)
dtype: float32
out_dtype: bfloat16
tokenizer:
  source: union
# chat_template: auto
trust_remote_code: true
name: 📜 Goetia 26B A4B v1.7C

mergekit_config_4_nearswap.yml

architecture: Gemma4ForConditionalGeneration
merge_method: nearswap # v2
base_model: B:\26B\Goetia-26B-A4B-v1.7_multifusion
models:
  - model: B:\26B\SubMaroon--Kitchoon-26B-A4B  
    parameters:  
      t:  
        - filter: "layers.10.self_attn.o_proj"
          value: 0.0001
        - filter: "layers.10.self_attn.v_proj"  
          value: 0.0001
        - filter: "layers.11.self_attn.o_proj"
          value: 0.0001
        - filter: "layers.11.self_attn.v_proj"  
          value: 0.0001
        - filter: "layers.12.self_attn.o_proj"
          value: 0.0001
        - filter: "layers.12.self_attn.v_proj"  
          value: 0.0001
        - filter: "layers.13.self_attn.o_proj"
          value: 0.0001
        - filter: "layers.13.self_attn.v_proj"
          value: 0.0001
        - filter: "layers.14.self_attn.o_proj"
          value: 0.0001
        - filter: "layers.14.self_attn.v_proj"
          value: 0.0001
        - filter: "layers.15.self_attn.o_proj"
          value: 0.0001
        - filter: "layers.15.self_attn.v_proj"
          value: 0.0001
        - filter: "layers.16.self_attn.o_proj"
          value: 0.0001
        - filter: "layers.16.self_attn.v_proj"
          value: 0.0001
        - filter: "layers.17.self_attn.o_proj"
          value: 0.0001
        - filter: "layers.17.self_attn.v_proj"
          value: 0.0001
        - filter: "layers.18.self_attn.o_proj"
          value: 0.0001
        - filter: "layers.18.self_attn.v_proj"
          value: 0.0001
        - filter: "layers.19.self_attn.o_proj"
          value: 0.0001
        - filter: "layers.19.self_attn.v_proj"
          value: 0.0001
        - filter: "layers.20.self_attn.o_proj"
          value: 0.0001
        - filter: "layers.20.self_attn.v_proj"
          value: 0.0001
        - filter: "layers.21.self_attn.o_proj"
          value: 0.0001
        - filter: "layers.21.self_attn.v_proj"
          value: 0.0001
        - filter: "layers.22.self_attn.o_proj"
          value: 0.0001
        - filter: "layers.22.self_attn.v_proj"
          value: 0.0001
        - filter: "layers.23.self_attn.o_proj"
          value: 0.0001
        - filter: "layers.23.self_attn.v_proj"
          value: 0.0001
        - filter: "layers.24.self_attn.o_proj"
          value: 0.0001
        - filter: "layers.24.self_attn.v_proj"
          value: 0.0001
        - filter: "layers.25.self_attn.o_proj"
          value: 0.0001
        - filter: "layers.25.self_attn.v_proj"
          value: 0.0001
        - filter: "layers.26.self_attn.o_proj"
          value: 0.0001
        - filter: "layers.26.self_attn.v_proj"
          value: 0.0001
        - filter: "layers.27.self_attn.o_proj"
          value: 0.0001
        - filter: "layers.27.self_attn.v_proj"
          value: 0.0001
        - filter: "layers.28.self_attn.o_proj"
          value: 0.0001
        - filter: "layers.28.self_attn.v_proj"
          value: 0.0001
        - filter: "layers.29.self_attn.o_proj"  
          value: 0.0001
        - filter: "layers.29.self_attn.v_proj"  
          value: 0.0001
        - filter: "lm_head"  
          value: 0.0001 
        - value: 0.0
parameters:
  router_strategy: "nearswap"
dtype: float32
out_dtype: bfloat16
tokenizer:
  source: union
chat_template: auto
trust_remote_code: true
name: 📜 Goetia 26B A4B v1.7D