Instructions to use tyy1850819840/RelMLLM-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tyy1850819840/RelMLLM-4B with Transformers:

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

pipe = pipeline("image-text-to-text", model="tyy1850819840/RelMLLM-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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("tyy1850819840/RelMLLM-4B")
model = AutoModelForMultimodalLM.from_pretrained("tyy1850819840/RelMLLM-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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tyy1850819840/RelMLLM-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tyy1850819840/RelMLLM-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": "tyy1850819840/RelMLLM-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/tyy1850819840/RelMLLM-4B

SGLang

How to use tyy1850819840/RelMLLM-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 "tyy1850819840/RelMLLM-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": "tyy1850819840/RelMLLM-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 "tyy1850819840/RelMLLM-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": "tyy1850819840/RelMLLM-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 tyy1850819840/RelMLLM-4B with Docker Model Runner:
```
docker model run hf.co/tyy1850819840/RelMLLM-4B
```

RelMLLM-4B

RelMLLM-4B is the merged 4B checkpoint accompanying Beyond Paired Inputs: Learning to Leverage RGB-T Cross-Modal Relations in Multimodal Large Language Models. It is derived from Qwen3-VL-4B-Instruct and accepts aligned RGB and thermal-infrared observations.

Use the inference code in the companion RelMLLM repository. The RGB image must be provided first and the TIR image second.

Selected results

Benchmark	Metric	Score
RGBT-GroundBench	Acc@0.5	72.6788
Degraded-1K	Acc@0.5	54.20
RGB-Th-Bench	macro QAcc	75.2481
DV-1K-Balanced	Accuracy	79.30

These are complete archived evaluations of the recovered checkpoint, not exact paper-table reproduction. Paper GroundBench/Degraded scores were 72.3627/48.70. Code, prompts, dataset downloads and custom-set reconstruction limitations: RelMLLM GitHub.

The model is intended for research, not safety-critical sensor-health or surveillance decisions. Reliability fields are task-conditioned evidence utilities rather than physical sensor diagnoses. See the companion repository for protocols, limitations, code, license, and attribution.

Downloads last month: 17

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for tyy1850819840/RelMLLM-4B

Base model

Qwen/Qwen3-VL-4B-Instruct

Finetuned

(475)

this model