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 /configs /data_sources.example.yaml

lyhisme

Add OraRL training and evaluation code

53c10a4 verified about 17 hours ago

Raw

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3.16 kB

	# Copy this file, then replace every ../local_data placeholder.
	#
	# OraRL never downloads data or media. You must obtain each dataset under its
	# license, keep the required attribution, and point input/media_root at your
	# own local, licensed copy. The optional license and url values are audit
	# metadata only and are never opened by the builder.
	#
	# The seven quotas reproduce the final paper mixture: 100,032 train prompts
	# (1,563 batches of 64), with 62,656 structured prompts and 37,376 answer-only
	# prompts. The canary is
	# additional held-out data and is not subtracted from the train target.
	version: 1
	seed: 42
	target: 100032
	canary_size: 512
	max_prompts_per_media: 2
	require_media: true
	allow_shortfall: false

	# Supply JSON/JSONL benchmark records before a release build. Any candidate
	# sharing a normalized prompt identity or media identity is removed.
	benchmark_excludes:
	- ../local_data/exclusions/public_benchmarks.jsonl

	sources:
	- name: temporal_grounding_train
	input: ../local_data/annotations/temporal_grounding.jsonl
	task: temporal grounding
	family: temporal
	quota: 20096
	media_root: ../local_data/media/temporal
	license: REPLACE_WITH_DATASET_LICENSE
	url: REPLACE_WITH_DATASET_HOME_PAGE

	- name: tracking_train
	input: ../local_data/annotations/tracking.jsonl
	task: tracking
	family: tracking
	quota: 13952
	media_root: ../local_data/media/tracking
	license: REPLACE_WITH_DATASET_LICENSE
	url: REPLACE_WITH_DATASET_HOME_PAGE

	- name: segmentation_train
	input: ../local_data/annotations/segmentation.jsonl
	task: segmentation
	family: segmentation
	quota: 12032
	media_root: ../local_data/media/segmentation
	license: REPLACE_WITH_DATASET_LICENSE
	url: REPLACE_WITH_DATASET_HOME_PAGE

	- name: spatial_grounding_train
	input: ../local_data/annotations/spatial_grounding.jsonl
	task: spatial grounding
	family: spatial
	quota: 7040
	media_root: ../local_data/media/spatial
	license: REPLACE_WITH_DATASET_LICENSE
	url: REPLACE_WITH_DATASET_HOME_PAGE

	- name: spatial_temporal_grounding_train
	input: ../local_data/annotations/spatial_temporal_grounding.jsonl
	task: spatial-temporal grounding
	family: stvg
	quota: 9536
	media_root: ../local_data/media/spatial_temporal
	license: REPLACE_WITH_DATASET_LICENSE
	url: REPLACE_WITH_DATASET_HOME_PAGE

	- name: video_qa_train
	input: ../local_data/annotations/video_qa.jsonl
	task: video_qa_mc
	family: video_qa
	quota: 20288
	media_root: ../local_data/media/video_qa
	license: REPLACE_WITH_DATASET_LICENSE
	url: REPLACE_WITH_DATASET_HOME_PAGE

	- name: spatial_intelligence_train
	input: ../local_data/annotations/spatial_intelligence.jsonl
	task: spatial intelligence
	family: spatial_intelligence
	# Keep subtype labels such as object_counting and object_rel_direction;
	# the reward adapter uses them to select the official scoring rule.
	preserve_problem_type: true
	quota: 17088
	media_root: ../local_data/media/spatial_intelligence
	license: REPLACE_WITH_DATASET_LICENSE
	url: REPLACE_WITH_DATASET_HOME_PAGE