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-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OraRL/Video-ORA-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="OraRL/Video-ORA-4B") 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-4B") model = AutoModelForMultimodalLM.from_pretrained("OraRL/Video-ORA-4B", 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-4B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OraRL/Video-ORA-4B" # 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-4B", "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-4B
- SGLang
How to use OraRL/Video-ORA-4B 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-4B" \ --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-4B", "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-4B" \ --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-4B", "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-4B with Docker Model Runner:
docker model run hf.co/OraRL/Video-ORA-4B
File size: 2,451 Bytes
0185029 | 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 | from __future__ import annotations
import subprocess
import sys
from pathlib import Path
import pytest
RELEASE_ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(RELEASE_ROOT))
from orarl.cli import evaluate # noqa: E402
EVAL_TASK_ROOT = RELEASE_ROOT / "eval" / "task"
# One entry point per paper task family, so a release cannot drop a family.
FAMILY_ENTRY_POINTS = {
"video_qa": "eval_vllm.py",
"spatial_intelligence": "revsi/eval_revsi_vllm.py",
"temporal_grounding": "temporal_grounding/eval_timelens_hf.py",
"spatial_grounding": "spatial_grounding/eval_refcoco_vllm.py",
"tracking": "tracking/eval_tracking_vllm.py",
"spatial_temporal_grounding": "spatial_temporal_grounding/eval_stvg_vllm.py",
"segmentation": "segmentation/eval_seg_vllm.py",
}
def test_every_task_family_ships_an_entry_point() -> None:
missing = sorted(
family
for family, relative in FAMILY_ENTRY_POINTS.items()
if not (EVAL_TASK_ROOT / relative).is_file()
)
assert missing == []
assert (EVAL_TASK_ROOT / "eval.sh").is_file()
assert (EVAL_TASK_ROOT / "mmsi" / "eval_mmsi_transformers.py").is_file()
assert (EVAL_TASK_ROOT / "mindcube" / "data_utils.py").is_file()
def test_evaluator_is_discovered_without_external_runtime(
monkeypatch: pytest.MonkeyPatch,
tmp_path: Path,
) -> None:
monkeypatch.delenv("ORARL_EVALUATOR", raising=False)
monkeypatch.delenv("ORARL_RUNTIME_ROOT", raising=False)
monkeypatch.setattr(evaluate, "find_spec", lambda _: None)
monkeypatch.chdir(tmp_path)
assert evaluate._discover_evaluator(None) == (EVAL_TASK_ROOT / "eval.sh").resolve()
@pytest.mark.parametrize(
"relative",
sorted(path.relative_to(EVAL_TASK_ROOT).as_posix() for path in EVAL_TASK_ROOT.rglob("*.sh")),
)
def test_shipped_shell_evaluators_parse(relative: str) -> None:
result = subprocess.run(
["bash", "-n", str(EVAL_TASK_ROOT / relative)],
capture_output=True,
text=True,
check=False,
)
assert result.returncode == 0, result.stderr
def test_segmentation_loads_the_shipped_decord_patch() -> None:
patch = EVAL_TASK_ROOT / "qwenvl_decord_patch.py"
evaluator = (EVAL_TASK_ROOT / "segmentation" / "eval_seg_vllm.py").read_text(encoding="utf-8")
assert patch.is_file()
assert "import qwenvl_decord_patch" in evaluator
assert "Path(__file__).resolve().parents[1]," in evaluator
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