Instructions to use tengsnake/Llama-Guard-4-12B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tengsnake/Llama-Guard-4-12B-NVFP4 with Transformers:

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

pipe = pipeline("text-generation", model="tengsnake/Llama-Guard-4-12B-NVFP4")
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("tengsnake/Llama-Guard-4-12B-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("tengsnake/Llama-Guard-4-12B-NVFP4", 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 tengsnake/Llama-Guard-4-12B-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tengsnake/Llama-Guard-4-12B-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tengsnake/Llama-Guard-4-12B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tengsnake/Llama-Guard-4-12B-NVFP4

SGLang

How to use tengsnake/Llama-Guard-4-12B-NVFP4 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 "tengsnake/Llama-Guard-4-12B-NVFP4" \
    --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": "tengsnake/Llama-Guard-4-12B-NVFP4",
		"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 "tengsnake/Llama-Guard-4-12B-NVFP4" \
        --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": "tengsnake/Llama-Guard-4-12B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tengsnake/Llama-Guard-4-12B-NVFP4 with Docker Model Runner:
```
docker model run hf.co/tengsnake/Llama-Guard-4-12B-NVFP4
```

Quantized version of: meta-llama/Llama-Guard-4-12B

Quantization Details

Language Model Body (Text Body): All Self-Attention (q_proj, k_proj, v_proj, o_proj) and MLP (gate_proj, up_proj, down_proj) layers are successfully compressed to NVFP4.
Unquantized (Retained in BF16):
- lm_head (retained in BF16 to resolve vLLM loading compatibility issues)
- embed_tokens (token embedding layer)
- vision_model & multi_modal_projector (vision-related components)

Downloads last month: 40

Safetensors

Model size

12B params

Tensor type

F32

BF16

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Quantization Details

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Dataset used to train tengsnake/Llama-Guard-4-12B-NVFP4