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

Libraries

How to use OraRL/Video-ORA-4B with Transformers:

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

pipe = pipeline("image-text-to-text", model="OraRL/Video-ORA-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("OraRL/Video-ORA-4B")
model = AutoModelForMultimodalLM.from_pretrained("OraRL/Video-ORA-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 OraRL/Video-ORA-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OraRL/Video-ORA-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": "OraRL/Video-ORA-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/OraRL/Video-ORA-4B

SGLang

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

Video-ORA-4B / code /README.md

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	<p align="right"><a href="README_zh.md">简体中文</a></p>

	<div align="center">

	# OraRL

	### Annotations as Rollouts

	Efficient and scalable reinforcement learning for unified video MLLMs

	Yunheng Li · Guohong Mu · Hao Li · Shengsheng Qian · Dingwen Zhang ·
	Qibin Hou · Ming-Ming Cheng

	<p>
	<a href="https://arxiv.org/abs/2608.20492">📄 Paper</a>
	·
	<a href="https://orarl.github.io/">🌐 Project Page</a>
	·
	<a href="#models">🤗 Models (4B / 9B)</a>
	</p>
	<p>
	<a href="docs/environment.md">⚙️ Environment</a>
	·
	<a href="docs/training.md">🚀 Training</a>
	·
	<a href="docs/evaluation.md">📊 Evaluation</a>
	·
	<a href="LICENSE">⚖️ License</a>
	</p>

	<a href="https://orarl.github.io/assets/orarl-teaser.mp4">
	<img src="assets/orarl-hero.gif"
	alt="Animated OraRL method preview" width="92%">
	</a>

	▶ Click the image to watch the 1:38 project overview.

	</div>

	## Why OraRL

	- Annotation-as-rollout: annotations become reliable positive rollouts while
	policy samples retain an on-policy baseline.
	- Seven task families: one update rule covers temporal and spatial grounding,
	segmentation, tracking, spatial-temporal grounding, video QA, and spatial
	intelligence.
	- Efficient training (4B): sign-balanced pruning delivers **1.48× faster
	updates (92.5 → 62.4 s/step**) while reducing peak per-GPU memory from
	62.4 to 50.9 GB.
	- Efficient inference: on one H20 with vLLM in BF16, weight loading occupies
	8.6 GiB (4B) and 17.6 GiB (9B). On ten-minute, 2-fps videos,
	answer-only decoding cuts median post-TTFT latency from 4.78 s to 130 ms
	and total latency from 29.03 to 24.30 s.
	- Multimodal veRL infrastructure: a unified video contract carries cached
	artifacts, raw paths, or inline frame tensors through vLLM rollouts and FSDP
	updates, with decode-once frame reuse, temporal metadata, task-grouped
	batching, asynchronous Ray rewards, and safe hybrid-engine cache handling.

	## OraRL in One Update

	<p align="center">
	<img src="assets/orarl-method.gif"
	alt="Animated OraRL framework" width="96%">
	</p>

	An OraRL update separates reliable annotation guidance from on-policy
	normalization:

	1. Build the group: append one serialized annotation rollout to the policy
	samples generated for the same prompt.
	2. Keep the baseline on-policy: estimate the group baseline from policy
	rewards only.
	3. Guide and select: convert the annotation-policy reward gap into a
	correction, then retain a sign-balanced subset for the update.

	This design uses task-native annotations directly and requires no
	chain-of-thought supervision or decoding.

	## Video-ORA Results

	<p align="center">
	<img src="assets/paper-results.png"
	alt="Video-ORA-9B results across seven task families" width="100%">
	</p>

	### Dataset-Level Results

	<picture>
	<source media="(prefers-color-scheme: dark)"
	srcset="assets/video_ora_benchmark_matrix_dark.svg">
	<source media="(prefers-color-scheme: light)"
	srcset="assets/video_ora_benchmark_matrix_light.svg">
	<img src="assets/video_ora_benchmark_matrix_light.svg"
	alt="Dataset-level benchmark matrix comparing Video-ORA with multimodal baselines"
	width="100%">
	</picture>

	Video-ORA-9B leads the matched seven-family comparison without CoT decoding.
	Best and second-best values are highlighted per row; `†` denotes an
	original-report value whose frame, prompt, split, or decoding settings may
	differ. Averages require complete family coverage.

	<!-- <details>
	<summary>Benchmark sources</summary>

	Unmarked values come from Tables 1–8 and Appendix Table 20 of the latest
	[OraRL paper](https://arxiv.org/abs/2608.20492). External entries follow the original
	[LLaVA-OneVision-2](https://arxiv.org/abs/2605.25979),
	[VideoChat3](https://github.com/MCG-NJU/VideoChat3), and
	[OneThinker](https://arxiv.org/abs/2512.03043) reports. OneThinker is cited only
	as the source of public Qwen3-VL scores. ReVSI uses each model's reported frame
	setting; the paper's three-benchmark spatial-intelligence average excludes it.

	</details> -->

	### Model Scaling

	<p align="center">
	<img src="assets/orarl-model-scaling.gif"
	alt="Animated Video-ORA model scaling from 0.8B to 9B" width="100%">
	</p>

	### Data Scaling

	<p align="center">
	<img src="assets/orarl-data-scaling.gif"
	alt="Animated OraRL data scaling and reward dynamics" width="100%">
	</p>

	## Models

	\| Model \| Backbone \| Released recipe \| Weights \|
	\| --- \| --- \| --- \| --- \|
	\| Video-ORA-9B \| Qwen3.5-9B \| `orarl_9b.yaml` \| [Hugging Face](https://huggingface.co/OraRL/Video-ORA-9B) \|
	\| Video-ORA-4B \| Qwen3.5-4B \| `orarl_4b.yaml` \| [Hugging Face](https://huggingface.co/OraRL/Video-ORA-4B) \|

	### vLLM Serving

	Both Video-ORA checkpoints load directly with vLLM 0.19.1 for
	OpenAI-compatible serving:

	```bash
	MODEL=OraRL/Video-ORA-9B

	vllm serve "$MODEL" \
	--served-model-name Video-ORA-9B \
	--trust-remote-code \
	--dtype bfloat16 \
	--tensor-parallel-size 1 \
	--max-model-len 131072 \
	--limit-mm-per-prompt '{"image": 1, "video": 1}'
	```

	Set `--tensor-parallel-size` to the GPU count for multi-GPU deployment and
	lower `--max-model-len` on smaller-memory devices. Use
	`enable_thinking=false` in the chat template for answer-only inference.

	## Use OraRL

	The release is organized around three user-facing workflows:

	1. [Environment](docs/environment.md): install the pinned CUDA stack that
	covers both the bundled trainer and the evaluators.
	2. [Training](docs/training.md): prepare licensed local training data and
	launch GRPO or OraRL on one or multiple nodes.
	3. [Evaluation](docs/evaluation.md): download Video-ORA and OraRL-Data,
	then run a smoke test or the complete paper suite.

	Training and evaluation are dry runs by default; inspect the resolved command
	before adding `--run`. Checkpoints and evaluation media are hosted under the
	[OraRL Hugging Face organization](https://huggingface.co/OraRL).

	## Acknowledgements

	OraRL is built on [veRL](https://github.com/volcengine/verl) — a
	high-performance RL framework with HybridEngine. We thank its authors and
	contributors for open-sourcing the training infrastructure.

	## License

	OraRL source is released under [Apache-2.0](LICENSE). Datasets, models,
	benchmarks, and optional dependencies retain their original licenses; see
	[NOTICE](NOTICE).

	## Citation

	If you find OraRL useful, please consider giving this repository a ⭐ and
	citing our [paper](https://arxiv.org/abs/2608.20492).

	```bibtex
	@article{li2026orarl,
	title = {Annotations as Rollouts: Efficient and Scalable
	Reinforcement Learning for Video MLLMs},
	author = {Li, Yunheng and Mu, Guohong and Li, Hao and
	Qian, Shengsheng and Zhang, Dingwen and Hou, Qibin
	and Cheng, Ming-Ming},
	journal = {arXiv preprint arXiv:2608.20492},
	year = {2026},
	url = {https://arxiv.org/abs/2608.20492}
	}
	```