Image-Text-to-Text
Transformers
Safetensors
qwen3_5
vllm
video
multimodal
reinforcement-learning
temporal-grounding
object-tracking
video-segmentation
visual-question-answering
spatial-reasoning
qwen3.5
conversational
Instructions to use OraRL/Video-ORA-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| #!/usr/bin/env python3 | |
| """Move legacy evaluation outputs into paper-level task families.""" | |
| from __future__ import annotations | |
| import argparse | |
| from pathlib import Path | |
| from typing import Iterable | |
| TASK_FAMILIES = { | |
| "video_qa": ( | |
| "videomme", | |
| "videommev2", | |
| "videommmu", | |
| "mmvu", | |
| "mvbench", | |
| "videoholmes", | |
| "longvideobench", | |
| "lvbench", | |
| "mlvu", | |
| ), | |
| "spatial_intelligence": ("vsi", "mmsi", "mindcube", "revsi"), | |
| "spatial_temporal_grounding": ("stvg",), | |
| } | |
| def _exists(path: Path) -> bool: | |
| return path.exists() or path.is_symlink() | |
| def planned_moves(root: Path) -> list[tuple[Path, Path]]: | |
| moves = [] | |
| for family, tasks in TASK_FAMILIES.items(): | |
| for task in tasks: | |
| source = root / task | |
| if source.is_dir(): | |
| moves.append((source, root / family / task)) | |
| return moves | |
| def _preflight_merge(source: Path, destination: Path) -> None: | |
| for child in source.iterdir(): | |
| target = destination / child.name | |
| if not _exists(target): | |
| continue | |
| if child.is_dir() and not child.is_symlink(): | |
| if not target.is_dir() or target.is_symlink(): | |
| raise FileExistsError(f"cannot merge directory into {target}") | |
| _preflight_merge(child, target) | |
| continue | |
| raise FileExistsError(f"refusing to overwrite existing output: {target}") | |
| def _merge(source: Path, destination: Path) -> None: | |
| destination.mkdir(parents=True, exist_ok=True) | |
| for child in source.iterdir(): | |
| target = destination / child.name | |
| if child.is_dir() and not child.is_symlink() and target.is_dir(): | |
| _merge(child, target) | |
| else: | |
| child.rename(target) | |
| source.rmdir() | |
| def organize(root: Path, moves: Iterable[tuple[Path, Path]]) -> None: | |
| moves = list(moves) | |
| for source, destination in moves: | |
| if _exists(destination): | |
| _preflight_merge(source, destination) | |
| for source, destination in moves: | |
| destination.parent.mkdir(parents=True, exist_ok=True) | |
| if _exists(destination): | |
| _merge(source, destination) | |
| else: | |
| source.rename(destination) | |
| def create_parser() -> argparse.ArgumentParser: | |
| parser = argparse.ArgumentParser(description=__doc__) | |
| parser.add_argument( | |
| "root", | |
| type=Path, | |
| help="One model's output root, for example outputs/Video-ORA-9B.", | |
| ) | |
| parser.add_argument( | |
| "--apply", | |
| action="store_true", | |
| help="Perform the moves. Without this flag, only print the plan.", | |
| ) | |
| return parser | |
| def main() -> int: | |
| args = create_parser().parse_args() | |
| root = args.root.expanduser().resolve() | |
| if not root.is_dir(): | |
| raise SystemExit(f"output root is not a directory: {root}") | |
| moves = planned_moves(root) | |
| if not moves: | |
| print(f"Already organized: {root}") | |
| return 0 | |
| action = "MOVE" if args.apply else "PLAN" | |
| for source, destination in moves: | |
| print(f"{action} {source.relative_to(root)} -> {destination.relative_to(root)}") | |
| if args.apply: | |
| organize(root, moves) | |
| print(f"Organized {len(moves)} task director{'y' if len(moves) == 1 else 'ies'}.") | |
| else: | |
| print("Dry run only; pass --apply to move these directories.") | |
| return 0 | |
| if __name__ == "__main__": | |
| raise SystemExit(main()) | |