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
File size: 7,198 Bytes
53c10a4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 | from __future__ import annotations
import hashlib
import json
import sys
import tempfile
import unittest
from copy import deepcopy
from pathlib import Path
ORARL_ROOT = Path(__file__).resolve().parents[1]
if str(ORARL_ROOT) not in sys.path:
sys.path.insert(0, str(ORARL_ROOT))
from orarl.evaluation import ( # noqa: E402
EVALUATION_SCHEMA_VERSION,
MANIFEST_FILENAME,
ManifestError,
load_dataset_manifest,
validate_dataset_manifest,
validate_dataset_manifest_record,
validate_evaluation_repository,
)
def _manifest(expected_count: int = 1) -> dict[str, object]:
return {
"schema_version": EVALUATION_SCHEMA_VERSION,
"benchmark": "videomme",
"split": "test",
"task": "videomme",
"family": "video_qa",
"annotation_path": "annotations/video_qa/videomme/test.jsonl",
"media_paths": [
"media/video_qa/videomme/videos",
"media/video_qa/videomme/subtitles",
],
"artifact_paths": [],
"expected_count": expected_count,
"license": "CC-BY-4.0",
"source_url": "https://example.org/videomme",
"redistribution_authorized": True,
"evaluation": {
"prompt_profile": "video_qa",
"parser_profile": "multiple_choice",
"metric_profile": "accuracy",
},
"legacy_environment": {
"VIDEOMME_DATA_FILE": "annotations/video_qa/videomme/test.jsonl",
"VIDEOMME_VIDEO_ROOT": "media/video_qa/videomme/videos",
"VIDEOMME_EXPECTED_SAMPLES": 1,
},
}
def _evaluation_row() -> dict[str, object]:
return {
"schema_version": EVALUATION_SCHEMA_VERSION,
"eval_task": "videomme",
"sample_id": "q-1",
"benchmark": "videomme",
"split": "test",
"problem": "What happens next?",
"answer": "B",
"images": [],
"videos": ["media/video_qa/videomme/videos/clip.mp4"],
"problem_type": "video_qa_mc",
"source": "Video-MME",
"family": "video_qa",
"subtitles": ["media/video_qa/videomme/subtitles/clip.srt"],
}
def _write_jsonl(path: Path, records: list[dict[str, object]]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("w", encoding="utf-8") as handle:
for record in records:
handle.write(json.dumps(record, sort_keys=True) + "\n")
class EvaluationManifestTests(unittest.TestCase):
def test_accepts_complete_manifest_record(self) -> None:
record = _manifest()
self.assertIs(validate_dataset_manifest_record(record), record)
def test_rejects_duplicate_datasets_and_noncanonical_layout(self) -> None:
with self.assertRaisesRegex(ManifestError, "duplicate dataset"):
validate_dataset_manifest([_manifest(), deepcopy(_manifest())])
record = _manifest()
record["annotation_path"] = "annotations/VideoMME/test.jsonl"
with self.assertRaisesRegex(ManifestError, "annotation_path must be"):
validate_dataset_manifest_record(record)
def test_rejects_unsafe_legacy_paths_and_credentialed_source_urls(self) -> None:
record = _manifest()
record["source_url"] = "https://" + "user:" + "secret@" + "example.org/videomme"
record["legacy_environment"] = {"VIDEOMME_DATA_FILE": "/tmp/test.jsonl"}
record["preprocessing"] = {"artifact_path": "https://example.org/cache.pt"}
with self.assertRaises(ManifestError) as raised:
validate_dataset_manifest_record(record)
self.assertIn("without embedded credentials", str(raised.exception))
self.assertIn("repository-relative", str(raised.exception))
self.assertIn("absolute path or URL", str(raised.exception))
def test_loads_and_validates_a_complete_repository(self) -> None:
with tempfile.TemporaryDirectory() as raw_tmp:
root = Path(raw_tmp)
video = root / "media/video_qa/videomme/videos/clip.mp4"
subtitle = root / "media/video_qa/videomme/subtitles/clip.srt"
video.parent.mkdir(parents=True)
subtitle.parent.mkdir(parents=True)
video.write_bytes(b"video")
subtitle.write_bytes(b"subtitle")
annotation = root / "annotations/video_qa/videomme/test.jsonl"
_write_jsonl(annotation, [_evaluation_row()])
record = _manifest()
record["checksums"] = {
"annotations/video_qa/videomme/test.jsonl": hashlib.sha256(
annotation.read_bytes()
).hexdigest(),
"media/video_qa/videomme/videos/clip.mp4": hashlib.sha256(
video.read_bytes()
).hexdigest(),
}
manifest_path = root / MANIFEST_FILENAME
_write_jsonl(manifest_path, [record])
loaded = load_dataset_manifest(root)
self.assertEqual(loaded[0]["benchmark"], "videomme")
datasets = validate_evaluation_repository(root)
self.assertEqual(len(datasets[("videomme", "test")]), 1)
record["expected_count"] = 2
_write_jsonl(manifest_path, [record])
with self.assertRaisesRegex(ManifestError, "expected 2 rows"):
validate_evaluation_repository(root)
def test_checksum_and_redistribution_checks_are_switchable(self) -> None:
with tempfile.TemporaryDirectory() as raw_tmp:
root = Path(raw_tmp)
video = root / "media/video_qa/videomme/videos/clip.mp4"
subtitle = root / "media/video_qa/videomme/subtitles/clip.srt"
video.parent.mkdir(parents=True)
subtitle.parent.mkdir(parents=True)
video.write_bytes(b"video")
subtitle.write_bytes(b"subtitle")
_write_jsonl(
root / "annotations/video_qa/videomme/test.jsonl",
[_evaluation_row()],
)
record = _manifest()
record["redistribution_authorized"] = False
record["checksums"] = {
"media/video_qa/videomme/videos/clip.mp4": "0" * 64
}
_write_jsonl(root / MANIFEST_FILENAME, [record])
load_dataset_manifest(root)
with self.assertRaisesRegex(ManifestError, "checksum mismatch"):
load_dataset_manifest(root, repository_root=root)
record.pop("checksums")
_write_jsonl(root / MANIFEST_FILENAME, [record])
with self.assertRaisesRegex(ManifestError, "redistribution is not authorized"):
validate_evaluation_repository(root)
validate_evaluation_repository(
root,
require_redistribution_authorized=False,
)
def test_manifest_filename_is_fixed(self) -> None:
with tempfile.TemporaryDirectory() as raw_tmp:
path = Path(raw_tmp) / "manifest.jsonl"
_write_jsonl(path, [_manifest()])
with self.assertRaisesRegex(ManifestError, MANIFEST_FILENAME):
load_dataset_manifest(path)
if __name__ == "__main__":
unittest.main()
|