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 | |
| """Finalize an annotation-only OraRL evaluation index for publication.""" | |
| from __future__ import annotations | |
| import argparse | |
| import json | |
| import os | |
| import tempfile | |
| from pathlib import Path | |
| from typing import Any | |
| from orarl.evaluation.card import render_index_card | |
| from orarl.evaluation.manifest import load_dataset_manifest | |
| ALLOWED_ROOT_ENTRIES = { | |
| ".gitattributes", | |
| "README.md", | |
| "annotations", | |
| "datasets.jsonl", | |
| } | |
| def _atomic_write(path: Path, content: str) -> None: | |
| descriptor, temporary_name = tempfile.mkstemp( | |
| prefix=f".{path.name}.", | |
| suffix=".tmp", | |
| dir=str(path.parent), | |
| ) | |
| try: | |
| with os.fdopen(descriptor, "w", encoding="utf-8", newline="\n") as stream: | |
| stream.write(content) | |
| os.replace(temporary_name, path) | |
| except Exception: | |
| try: | |
| os.unlink(temporary_name) | |
| except FileNotFoundError: | |
| pass | |
| raise | |
| def _set_segmentation_reader(record: dict[str, Any], reader: str) -> None: | |
| if record.get("task") != "segmentation": | |
| return | |
| legacy = dict(record.get("legacy_environment", {})) | |
| legacy["SEGMENTATION_VIDEO_READER"] = reader | |
| setting = str(legacy.get("SEGMENTATION_SETTING", "")) | |
| marker = f"reader{reader}" | |
| if setting and marker not in setting: | |
| new_marker = setting.rfind("-new") | |
| setting = ( | |
| f"{setting[:new_marker]}-{marker}{setting[new_marker:]}" | |
| if new_marker >= 0 | |
| else f"{setting}-{marker}" | |
| ) | |
| legacy["SEGMENTATION_SETTING"] = setting | |
| record["legacy_environment"] = legacy | |
| preprocessing = dict(record.get("preprocessing", {})) | |
| preprocessing["video_reader"] = reader | |
| record["preprocessing"] = preprocessing | |
| def _annotation_assets(root: Path, records: list[dict[str, Any]]) -> list[dict[str, Any]]: | |
| assets: list[dict[str, Any]] = [] | |
| for record in records: | |
| relative = str(record["annotation_path"]) | |
| annotation = root / relative | |
| if annotation.is_symlink() or not annotation.is_file(): | |
| raise FileNotFoundError(f"annotation is missing: {annotation}") | |
| with annotation.open("r", encoding="utf-8") as stream: | |
| row_count = sum(1 for line in stream if line.strip()) | |
| expected = int(record["expected_count"]) | |
| if row_count != expected: | |
| raise ValueError( | |
| f"{relative}: expected {expected} rows, found {row_count}" | |
| ) | |
| assets.append( | |
| { | |
| "benchmark": str(record["benchmark"]), | |
| "bytes": annotation.stat().st_size, | |
| "kind": "annotations", | |
| "path": relative, | |
| } | |
| ) | |
| return assets | |
| def finalize_index( | |
| root_path: str | os.PathLike[str], | |
| *, | |
| repo_id: str, | |
| segmentation_video_reader: str, | |
| ) -> dict[str, int]: | |
| root = Path(root_path).expanduser().resolve() | |
| unexpected = sorted( | |
| path.name | |
| for path in root.iterdir() | |
| if path.name not in ALLOWED_ROOT_ENTRIES | |
| ) | |
| if unexpected: | |
| raise ValueError( | |
| "metadata-only index contains unexpected root entries: " | |
| + ", ".join(unexpected) | |
| ) | |
| records = [dict(record) for record in load_dataset_manifest(root)] | |
| for record in records: | |
| _set_segmentation_reader(record, segmentation_video_reader) | |
| manifest = "".join( | |
| json.dumps(record, ensure_ascii=False, sort_keys=True, separators=(",", ":")) | |
| + "\n" | |
| for record in records | |
| ) | |
| _atomic_write(root / "datasets.jsonl", manifest) | |
| validated = [dict(record) for record in load_dataset_manifest(root)] | |
| assets = _annotation_assets(root, validated) | |
| _atomic_write( | |
| root / "README.md", | |
| render_index_card(validated, assets, repo_id=repo_id), | |
| ) | |
| return { | |
| "annotations": len(assets), | |
| "bytes": sum(int(asset["bytes"]) for asset in assets), | |
| "rows": sum(int(record["expected_count"]) for record in validated), | |
| } | |
| def main() -> None: | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--root", required=True) | |
| parser.add_argument("--repo-id", default="OraRL/OraRL-Data") | |
| parser.add_argument("--segmentation-video-reader", default="decord") | |
| args = parser.parse_args() | |
| print( | |
| json.dumps( | |
| finalize_index( | |
| args.root, | |
| repo_id=args.repo_id, | |
| segmentation_video_reader=args.segmentation_video_reader, | |
| ), | |
| sort_keys=True, | |
| ) | |
| ) | |
| if __name__ == "__main__": | |
| main() | |