Instructions to use artokun/gemma4-comfyui-mcp-12b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use artokun/gemma4-comfyui-mcp-12b with Transformers:

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

pipe = pipeline("text-generation", model="artokun/gemma4-comfyui-mcp-12b")
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("artokun/gemma4-comfyui-mcp-12b")
model = AutoModelForMultimodalLM.from_pretrained("artokun/gemma4-comfyui-mcp-12b")
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 artokun/gemma4-comfyui-mcp-12b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "artokun/gemma4-comfyui-mcp-12b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "artokun/gemma4-comfyui-mcp-12b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/artokun/gemma4-comfyui-mcp-12b

SGLang

How to use artokun/gemma4-comfyui-mcp-12b 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 "artokun/gemma4-comfyui-mcp-12b" \
    --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": "artokun/gemma4-comfyui-mcp-12b",
		"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 "artokun/gemma4-comfyui-mcp-12b" \
        --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": "artokun/gemma4-comfyui-mcp-12b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use artokun/gemma4-comfyui-mcp-12b with Docker Model Runner:
```
docker model run hf.co/artokun/gemma4-comfyui-mcp-12b
```

gemma4-comfyui-mcp-12b (merged bf16 — provider-servable)

Merged full weights of the 12B rung of artokun/gemma4-comfyui-mcp — Gemma 4 12B QLoRA-fine-tuned into a ComfyUI expert that drives the complete comfyui-mcp tool surface (178 tools) in Gemma 4's native tool-call format.

This repo exists for inference providers and self-hosted serving (vLLM / TGI / SGLang need root-layout merged safetensors). For local use, prefer the GGUF ladder: ollama pull artokun/gemma4-comfyui-mcp:12b (also :e4b, :e2b).

Base: coder3101/gemma-4-12B-it-heretic (Heretic-abliterated Gemma 4 12B)
Fine-tune: QLoRA r=32/α=32, 2 epochs, trimmed-context tool-menu training on server-verified ComfyUI tool-use trajectories — dataset public at artokun/comfyui-mcp-trajectories
Format: bf16, 5 sharded safetensors (~24 GB), Gemma 4 unified arch (AutoModelForImageTextToText), trained chat_template.jinja included
Serving notes: the comfyui-mcp tool payload is large — serve with a generous context window (16K minimum, 64K recommended); temperature 0 recommended for tool precision

Adapter-only, GGUFs, training pipeline, and the full model card live in the main ladder repo.

License: Gemma. Fine-tune by @artokun.

Downloads last month: 27

Safetensors

Model size

12B params

Tensor type

BF16

Model tree for artokun/gemma4-comfyui-mcp-12b

Base model

google/gemma-4-12B

Finetuned

google/gemma-4-12B-it

Finetuned

coder3101/gemma-4-12B-it-heretic

Finetuned

(1)

this model