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: 4,195 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 | from __future__ import annotations
import sys
import tempfile
import unittest
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.data import ( # noqa: E402
SchemaError,
canonicalize_record,
prompt_identity,
validate_record,
)
class DataSchemaTests(unittest.TestCase):
def test_canonicalizes_aliases_and_relative_media(self) -> None:
with tempfile.TemporaryDirectory() as raw_tmp:
tmp_path = Path(raw_tmp)
media_root = tmp_path / "media"
image = media_root / "frames" / "one.jpg"
image.parent.mkdir(parents=True)
image.write_bytes(b"image")
record = canonicalize_record(
{
"question": " Which object is red? ",
"ground_truth": {"label": "ball"},
"image": "frames/one.jpg",
"metadata": {"split": "train"},
},
problem_type="spatial grounding",
source="unit_source",
family="spatial",
media_root=media_root,
require_media=True,
)
self.assertEqual(record["problem"], " Which object is red? ")
self.assertEqual(record["answer"], {"label": "ball"})
self.assertEqual(record["images"], [str(image)])
self.assertEqual(record["videos"], [])
self.assertEqual(record["problem_type"], "spatial grounding")
self.assertEqual(record["source"], "unit_source")
self.assertEqual(record["metadata"], {"split": "train"})
def test_rejects_empty_oracle_labels(self) -> None:
answers = [None, "", " ", [], {}, {"label": ""}, float("nan")]
for answer in answers:
with self.subTest(answer=answer):
record = {
"problem": "Question",
"answer": answer,
"images": ["image.jpg"],
"videos": [],
"problem_type": "spatial grounding",
"source": "unit_source",
}
with self.assertRaisesRegex(SchemaError, "oracle label"):
validate_record(record)
def test_media_existence_is_switchable(self) -> None:
with tempfile.TemporaryDirectory() as raw_tmp:
record = {
"problem": "Question",
"answer": "Answer",
"images": [str(Path(raw_tmp) / "missing.jpg")],
"videos": [],
"problem_type": "spatial grounding",
"source": "unit_source",
}
validate_record(record, require_media=False)
with self.assertRaisesRegex(SchemaError, "does not exist"):
validate_record(record, require_media=True)
def test_prompt_identity_normalizes_text_and_paths(self) -> None:
with tempfile.TemporaryDirectory() as raw_tmp:
tmp_path = Path(raw_tmp)
first = {
"problem": "<image> Which OBJECT is red?",
"answer": "ball",
"images": [str(tmp_path / "frames" / ".." / "one.jpg")],
"videos": [],
"problem_type": "Spatial Grounding",
"source": "one",
}
second = {
**first,
"problem": "which object is red?",
"images": [str(tmp_path / "one.jpg")],
"source": "two",
}
self.assertEqual(prompt_identity(first), prompt_identity(second))
def test_rejects_remote_media_reference(self) -> None:
with self.assertRaisesRegex(SchemaError, "remote media"):
canonicalize_record(
{
"problem": "Question",
"answer": "Answer",
"video": "https://example.invalid/clip.mp4",
},
problem_type="video_qa_mc",
source="unit_source",
)
if __name__ == "__main__":
unittest.main()
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