Instructions to use Hadidiz9/GUI-Owl-1.5-32B-Instruct-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hadidiz9/GUI-Owl-1.5-32B-Instruct-NVFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Hadidiz9/GUI-Owl-1.5-32B-Instruct-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("Hadidiz9/GUI-Owl-1.5-32B-Instruct-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("Hadidiz9/GUI-Owl-1.5-32B-Instruct-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?"}
        ]
    },
]
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 Hadidiz9/GUI-Owl-1.5-32B-Instruct-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hadidiz9/GUI-Owl-1.5-32B-Instruct-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": "Hadidiz9/GUI-Owl-1.5-32B-Instruct-NVFP4",
		"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/Hadidiz9/GUI-Owl-1.5-32B-Instruct-NVFP4

SGLang

How to use Hadidiz9/GUI-Owl-1.5-32B-Instruct-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 "Hadidiz9/GUI-Owl-1.5-32B-Instruct-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": "Hadidiz9/GUI-Owl-1.5-32B-Instruct-NVFP4",
		"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 "Hadidiz9/GUI-Owl-1.5-32B-Instruct-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": "Hadidiz9/GUI-Owl-1.5-32B-Instruct-NVFP4",
		"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 Hadidiz9/GUI-Owl-1.5-32B-Instruct-NVFP4 with Docker Model Runner:
```
docker model run hf.co/Hadidiz9/GUI-Owl-1.5-32B-Instruct-NVFP4
```

GUI-Owl-1.5-32B-Instruct-NVFP4

NVFP4 (4-bit) weight-only quantization of mPLUG/GUI-Owl-1.5-32B-Instruct, a Qwen3-VL–based vision GUI-agent model. This build is optimized for efficient serving on NVIDIA Blackwell hardware (e.g. GB10 / DGX Spark / RTX 50-series) with vLLM.

Base model: mPLUG/GUI-Owl-1.5-32B-Instruct (Qwen3VLForConditionalGeneration)
Quantization: NVFP4, compressed-tensors format (nvfp4-pack-quantized), weight-only (W4A16)
Scope: only the language-model Linear layers are quantized; the vision tower, multimodal merger, and lm_head are kept in their original precision so the image path and output head are unaffected
Method: llm-compressor data-free RTN (QuantizationModifier, scheme NVFP4A16) — no calibration dataset
Size: ~21 GB (down from ~63 GB BF16)

Serving with vLLM

Requires a vLLM build with compressed-tensors NVFP4 support (loads via MarlinNvFp4LinearKernel on Blackwell):

vllm serve Hadidiz9/GUI-Owl-1.5-32B-Instruct-NVFP4 \
  --served-model-name gui-owl-32b \
  --max-model-len 32768 \
  --trust-remote-code \
  --kv-cache-dtype fp8 \
  --limit-mm-per-prompt '{"image":5}' \
  --allowed-local-media-path /

Notes

Context length: 32768 tokens.
The model consumes screenshots (images) as input for GUI-agent tasks; the vision preprocessor config is included.
Quantization was performed on an NVIDIA GB10.

License

Inherits the Apache-2.0 license of the base model mPLUG/GUI-Owl-1.5-32B-Instruct. All credit for the original model belongs to the mPLUG / GUI-Owl authors. This repository only redistributes a quantized copy of their weights.

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Safetensors

Model size

20B params

Tensor type

F32

BF16

F8_E4M3

Model tree for Hadidiz9/GUI-Owl-1.5-32B-Instruct-NVFP4

Base model

mPLUG/GUI-Owl-1.5-32B-Instruct

Quantized

(3)

this model