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: 2,434 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 | from __future__ import annotations
from pathlib import Path
import tomllib
RELEASE_ROOT = Path(__file__).resolve().parents[1]
def test_install_metadata_and_console_scripts() -> None:
with (RELEASE_ROOT / "pyproject.toml").open("rb") as handle:
metadata = tomllib.load(handle)
project = metadata["project"]
assert project["name"] == "orarl"
assert project["requires-python"] == ">=3.10"
# The trainer ships inside this distribution, so no external runtime pin.
assert not any(dependency.startswith("verl") for dependency in project["dependencies"])
assert project["license"] == "Apache-2.0"
assert project["license-files"] == ["LICENSE", "NOTICE"]
assert project["optional-dependencies"]["hf"] == ["huggingface_hub"]
assert project["scripts"] == {
"orarl-train": "orarl.cli.train:main",
"orarl-eval": "orarl.cli.evaluate:main",
"orarl-eval-data": "orarl.cli.eval_data:main",
"orarl-prepare": "orarl.cli.prepare:main",
}
data_files = metadata["tool"]["setuptools"]["data-files"]
assert data_files["share/orarl"] == ["environment.yml", "requirements-cu129.txt"]
assert data_files["share/orarl/configs"] == ["configs/*.yaml"]
assert data_files["share/orarl/docs"] == ["docs/*.md"]
assert data_files["share/orarl/scripts"] == ["scripts/*.py", "scripts/*.sh"]
def test_install_ships_every_evaluator() -> None:
with (RELEASE_ROOT / "pyproject.toml").open("rb") as handle:
metadata = tomllib.load(handle)
installed: set[Path] = set()
for target, patterns in metadata["tool"]["setuptools"]["data-files"].items():
if not target.startswith("share/orarl/eval"):
continue
for pattern in patterns:
installed.update(RELEASE_ROOT.glob(pattern))
# Importing an evaluator during the suite drops bytecode next to the source.
shipped = {
path
for path in (RELEASE_ROOT / "eval").rglob("*")
if path.is_file() and "__pycache__" not in path.parts
}
assert shipped
assert sorted(shipped - installed) == []
def test_apache_attribution_files_are_present() -> None:
license_text = (RELEASE_ROOT / "LICENSE").read_text(encoding="utf-8")
notice_text = (RELEASE_ROOT / "NOTICE").read_text(encoding="utf-8")
assert "Apache License" in license_text
assert "Version 2.0" in license_text
assert "ByteDance Ltd." in notice_text
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