Instructions to use OraRL/Video-ORA-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OraRL/Video-ORA-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="OraRL/Video-ORA-9B")
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("OraRL/Video-ORA-9B")
model = AutoModelForMultimodalLM.from_pretrained("OraRL/Video-ORA-9B", 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 OraRL/Video-ORA-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OraRL/Video-ORA-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OraRL/Video-ORA-9B",
		"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/OraRL/Video-ORA-9B

SGLang

How to use OraRL/Video-ORA-9B 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 "OraRL/Video-ORA-9B" \
    --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": "OraRL/Video-ORA-9B",
		"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 "OraRL/Video-ORA-9B" \
        --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": "OraRL/Video-ORA-9B",
		"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 OraRL/Video-ORA-9B with Docker Model Runner:
```
docker model run hf.co/OraRL/Video-ORA-9B
```

Video-ORA-9B / code /eval /README.md

lyhisme

Add OraRL training and evaluation code

53c10a4 verified about 16 hours ago

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Evaluation runtime

eval/task/ holds the evaluators that produce every number in the paper. They ship with this repository, so a single clone can reproduce the full suite. orarl-eval is the supported entry point: it resolves the canonical dataset layout into per-task environment variables and then runs eval/task/eval.sh.

orarl-eval \
  --model /path/to/exported-model \
  --tasks paper \
  --dataset OraRL/OraRL-Data \
  --summary ./evaluation.json \
  --run

Without --run the command prints the resolved plan and exits, which is the fastest way to confirm data and model paths before allocating GPUs. eval/task/eval.sh can also be called directly when you want to bypass the canonical dataset resolution and set the per-task variables yourself.

Task map

Family	Benchmarks	Entry point
Video QA	VideoMME, VideoMME-v2, MVBench, MMVU, Video-Holmes, LongVideoBench, MLVU	`eval_vllm.py`
Spatial intelligence	VSI-Bench, MindCube, MMSI-Bench, ReVSI	`eval_vllm.py`, `mmsi/eval_mmsi_transformers.py`, `revsi/eval_revsi_vllm.py`
Temporal grounding	TimeLens (Charades, ActivityNet, QVHighlights)	`temporal_grounding/eval_timelens_hf.py`
Spatial grounding	RefCOCO, RefCOCO+, RefCOCOg	`spatial_grounding/eval_refcoco_vllm.py`
Tracking	GOT-10k	`tracking/eval_tracking_vllm.py`
Spatio-temporal grounding	STVG	`spatial_temporal_grounding/eval_stvg_vllm.py`
Segmentation	RefCOCO series, MeViS, ReasonVOS	`segmentation/eval_seg_vllm.py` plus `segmentation/post_sam2.py`

eval_prompt.py is the single source of truth for prompts, and canonical_data.py adapts the canonical ORARL_EVAL_* layout for every evaluator. MMSI-Bench runs through Transformers rather than vLLM; the other families run through vLLM.

Assets you must obtain separately

Annotations and media come from the OraRL/OraRL-Data dataset repository, and model weights come from the released checkpoints. Neither is vendored here.

Segmentation additionally needs three inputs, all supplied by you under their upstream licenses:

SAM2 weights and the matching Hydra config, passed as SEGMENTATION_SAM2_CKPT and SEGMENTATION_SAM2_CFG.
The official OneThinker seg_post_sam2.py, passed as SEGMENTATION_POSTPROCESSOR_PATH. segmentation/post_sam2.py is a thin wrapper that injects paths into it so mask metrics stay identical to the upstream implementation.
The sam2 Python package.

orarl-eval refuses to start segmentation with --segmentation-run-sam2 unless all three paths exist.

Video decoding

Video evaluators pin the decord backend through FORCE_QWENVL_VIDEO_READER. eval/task/qwenvl_decord_patch.py replaces qwen_vl_utils.fetch_video so the backend is honoured across qwen_vl_utils releases that otherwise hard-code torchvision. The backend is not cosmetic: switching MeViS and ReasonVOS from torchcodec to decord moved MeViS J&F from 56.7 to 60.6. Set SEGMENTATION_VIDEO_READER to torchcodec or torchvision only when deliberately measuring that difference.

Checkpoint format

Exported Hugging Face checkpoints run as-is. Pass --force-merge when the model path is a sharded FSDP actor directory; eval/task/eval.sh then merges it with scripts/model_merger.py before inference.