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

File size: 2,597 Bytes

# Copyright 2026 The OraRL Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Public, dependency-light OraRL algorithm surface."""

from .advantages import (
    DIRECTIONAL_GAIN_MAX,
    DIRECTIONAL_GAIN_MIN,
    apply_detached_oracle_advantage,
    apply_grpo_advantages,
    apply_orarl_advantages,
    compute_directional_gain,
    compute_grpo_advantages,
    compute_grpo_outcome_advantage,
    compute_orarl_advantages,
    compute_orarl_policy_advantages,
    detached_oracle_advantage,
    directional_gain,
    normalized_reward_gap,
    raw_center_on_policy,
    transform_directional_utility,
    vanilla_grpo_advantage,
)
from .config import (
    CorrectionConfig,
    DetachedOracleConfig,
    DirectionalGainConfig,
    GRPOConfig,
    OraRLConfig,
    PostSelectionReference,
    SelectionConfig,
)
from .correction import (
    apply_post_selection_advantage_correction,
    apply_post_selection_correction,
    balance_post_selection_group,
    build_pre_selection_references,
    capture_pre_selection_references,
    correct_post_selection_group,
)
from .selection import select_sign_balanced_rollouts

__all__ = [
    "DIRECTIONAL_GAIN_MAX",
    "DIRECTIONAL_GAIN_MIN",
    "CorrectionConfig",
    "DetachedOracleConfig",
    "DirectionalGainConfig",
    "GRPOConfig",
    "OraRLConfig",
    "PostSelectionReference",
    "SelectionConfig",
    "apply_detached_oracle_advantage",
    "apply_grpo_advantages",
    "apply_orarl_advantages",
    "apply_post_selection_advantage_correction",
    "apply_post_selection_correction",
    "balance_post_selection_group",
    "build_pre_selection_references",
    "capture_pre_selection_references",
    "compute_directional_gain",
    "compute_grpo_advantages",
    "compute_grpo_outcome_advantage",
    "compute_orarl_advantages",
    "compute_orarl_policy_advantages",
    "correct_post_selection_group",
    "detached_oracle_advantage",
    "directional_gain",
    "normalized_reward_gap",
    "raw_center_on_policy",
    "select_sign_balanced_rollouts",
    "transform_directional_utility",
    "vanilla_grpo_advantage",
]