Instructions to use irl-kit/SPARC-Qwen3.5-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use irl-kit/SPARC-Qwen3.5-4B with Transformers:

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

pipe = pipeline("image-text-to-text", model="irl-kit/SPARC-Qwen3.5-4B")
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("irl-kit/SPARC-Qwen3.5-4B")
model = AutoModelForMultimodalLM.from_pretrained("irl-kit/SPARC-Qwen3.5-4B", 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 irl-kit/SPARC-Qwen3.5-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "irl-kit/SPARC-Qwen3.5-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "irl-kit/SPARC-Qwen3.5-4B",
		"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/irl-kit/SPARC-Qwen3.5-4B

SGLang

How to use irl-kit/SPARC-Qwen3.5-4B 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 "irl-kit/SPARC-Qwen3.5-4B" \
    --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": "irl-kit/SPARC-Qwen3.5-4B",
		"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 "irl-kit/SPARC-Qwen3.5-4B" \
        --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": "irl-kit/SPARC-Qwen3.5-4B",
		"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 irl-kit/SPARC-Qwen3.5-4B with Docker Model Runner:
```
docker model run hf.co/irl-kit/SPARC-Qwen3.5-4B
```

SPARC-Qwen3.5-4B / README.md

holgerson

Update README.md

7d86ac4 verified 7 days ago

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	---
	library_name: transformers
	pipeline_tag: image-text-to-text
	tags:
	- robotics
	- vision-language
	- spatial-reasoning
	- sparc
	datasets:
	- irl-kit/SPARC-VQA
	---

	# SPARC-Qwen3.5-4B

	Qwen3.5-4B fully fine-tuned for embodied spatial reasoning using VQA data generated from SPARC annotations.

	## Training data

	The training mixture contains SPARC-generated VQA data from `ours_adaptive_det_soft_snr_sp8`, FSD, RoboPoint, and LLaVA-OneVision2. SPARC samples use an annotation-quality threshold of 0.97, are sorted by score, and are capped at 700 samples per object. The paper reports 1,159,047 training pairs for this mixture.

	\| Release \| [SPARC VQA (filtered)](https://huggingface.co/datasets/irl-kit/SPARC-VQA) \| FSD \| RoboPoint \| LLaVA-OneVision2 \| EO-1.5M \|
	\| --- \| --- \| --- \| --- \| --- \| --- \|
	\| Qwen3.5-4B \| Yes \| Yes \| Yes \| Yes \| No \|
	\| Qwen3.5-0.8B-VTFT \| Yes \| Yes \| Yes \| Yes \| No \|
	\| Qwen3.5-9B-EO \| Yes \| Yes \| Yes \| Yes \| Yes \|

	The SPARC training data is the [filtered release subset](https://huggingface.co/datasets/irl-kit/SPARC-VQA), which needs no further SPARC filtering. Its filtering script and release mixture manifest are in [irl-kit/SPARC-VQA-Raw](https://huggingface.co/datasets/irl-kit/SPARC-VQA-Raw). FSD, RoboPoint, LLaVA-OneVision2, and EO-1.5M remain their respective upstream datasets.

	## Prompting

	Prompt formatting is important for these models. Use the bundled `chat_template.jinja` through `processor.apply_chat_template(..., add_generation_prompt=True)`, with one `user` turn containing the image(s) followed by the text question. Disable thinking/reasoning mode to match evaluation.

	For a single point, append exactly:

	```text
	Output the point coordinates in JSON format like [{"point_2d": [x, y], "label": "target"}]. Use integer coordinates between 0 and 1000.
	```

	For a trajectory or multiple points, append exactly:

	```text
	Return only a JSON list like [{"point_2d": [x1, y1], "label": "point_1"}, {"point_2d": [x2, y2], "label": "point_2"}, ...]. Use integer coordinates between 0 and 1000.
	```

	## Training

	The vision encoder is frozen and the vision projector is trainable. The model was fully fine-tuned for one epoch with a learning rate of 2e-5 and a maximum sequence length of 5600.

	## Evaluation

	This is the paper's primary 4B model. The paper reports a 62.7 pointing/VQA average for this model. The local full benchmark evaluation associates the released weights with aggregate score 0.698.

	\| Model \| Aggregate \| Where2Place \| RefSpatial location \| IA-Bench \| RoboRefIt testA \| VA Bench-P \|
	\| --- \| ---: \| ---: \| ---: \| ---: \| ---: \| ---: \|
	\| Qwen3.5-4B \| 0.698 \| 72.0 \| 59.0 \| 79.0 \| 85.7 \| 65.7 \|
	\| Qwen3.5-0.8B-VTFT \| 0.605 \| 58.0 \| 47.0 \| 76.7 \| 80.9 \| 48.3 \|
	\| Qwen3.5-9B-EO \| 0.719 \| 76.0 \| 68.0 \| 78.5 \| 85.2 \| 68.7 \|

	## Citation

	```bibtex
	@article{blank2026sparc,
	title={SPARC: Reliable Spatial Annotations from Robot Demonstrations at Scale},
	author={Blank, Nils and others},
	journal={arXiv preprint arXiv:2606.13497},
	year={2026}
	}
	```