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
| from __future__ import annotations | |
| import importlib.util | |
| import sys | |
| from pathlib import Path | |
| RELEASE_ROOT = Path(__file__).resolve().parents[1] | |
| CHECKER_PATH = RELEASE_ROOT / "scripts" / "check_release.py" | |
| SPEC = importlib.util.spec_from_file_location("orarl_release_check", CHECKER_PATH) | |
| assert SPEC is not None and SPEC.loader is not None | |
| CHECKER = importlib.util.module_from_spec(SPEC) | |
| sys.modules[SPEC.name] = CHECKER | |
| SPEC.loader.exec_module(CHECKER) | |
| def _reasons(root: Path, max_bytes: int = CHECKER.DEFAULT_MAX_BYTES) -> list[str]: | |
| return [finding.reason for finding in CHECKER.check_release(root, max_bytes)] | |
| def test_clean_text_tree_passes(tmp_path: Path) -> None: | |
| (tmp_path / "README.md").write_text("portable source\n", encoding="utf-8") | |
| assert CHECKER.check_release(tmp_path) == [] | |
| def test_portable_evaluation_jsonl_is_release_source(tmp_path: Path) -> None: | |
| annotation = tmp_path / "data" / "eval" / "annotations" / "task.jsonl" | |
| annotation.parent.mkdir(parents=True) | |
| natural_text = "".join(("fl", "uffy")) | |
| annotation.write_text( | |
| f'{{"problem":"{natural_text}","videos":["media/task/videos/clip.mp4"]}}\n', | |
| encoding="utf-8", | |
| ) | |
| assert CHECKER.check_release(tmp_path) == [] | |
| def test_canonical_evaluation_jsonl_can_exceed_the_generic_source_limit( | |
| tmp_path: Path, | |
| ) -> None: | |
| annotation = tmp_path / "data" / "eval" / "annotations" / "task.jsonl" | |
| annotation.parent.mkdir(parents=True) | |
| annotation.write_text('{"problem":"' + ("x" * 64) + '"}\n', encoding="utf-8") | |
| assert CHECKER.check_release(tmp_path, max_bytes=16) == [] | |
| def test_evaluation_jsonl_still_rejects_private_paths(tmp_path: Path) -> None: | |
| annotation = tmp_path / "data" / "eval" / "annotations" / "task.jsonl" | |
| annotation.parent.mkdir(parents=True) | |
| private_path = "/" + "mnt" + "/person/video.mp4" | |
| annotation.write_text( | |
| f'{{"videos":["{private_path}"]}}\n', | |
| encoding="utf-8", | |
| ) | |
| assert "private absolute path at line 1" in _reasons(tmp_path) | |
| def test_non_evaluation_data_tree_is_rejected(tmp_path: Path) -> None: | |
| generated = tmp_path / "data" / "training" | |
| generated.mkdir(parents=True) | |
| (generated / "rows.txt").write_text("private input\n", encoding="utf-8") | |
| assert "generated root data directory" in _reasons(tmp_path) | |
| def test_excluded_method_terms_are_detected_without_literals_in_test_source( | |
| tmp_path: Path, | |
| ) -> None: | |
| term_one = "".join(("c", "p", "p", "o")) | |
| term_two = "".join(("lu", "ff", "y")) | |
| (tmp_path / "source.py").write_text( | |
| f"first = '{term_one}'\nsecond = '{term_two}'\n", | |
| encoding="utf-8", | |
| ) | |
| reasons = _reasons(tmp_path) | |
| assert sum("excluded method term" in reason for reason in reasons) == 2 | |
| def test_legacy_oracle_names_are_detected(tmp_path: Path) -> None: | |
| legacy_name = "".join(("build_", "g", "t", "_response")) | |
| (tmp_path / "source.py").write_text( | |
| f"def {legacy_name}():\n pass\n", | |
| encoding="utf-8", | |
| ) | |
| assert any("excluded method term" in reason for reason in _reasons(tmp_path)) | |
| def test_evaluation_runtime_may_name_benchmark_ground_truth(tmp_path: Path) -> None: | |
| evaluator = tmp_path / "eval" / "task" / "tracking" / "eval_tracking.py" | |
| evaluator.parent.mkdir(parents=True) | |
| annotation_field = "".join(("g", "t", "_", "bbox")) | |
| evaluator.write_text( | |
| f'box = record["{annotation_field}"]\n', | |
| encoding="utf-8", | |
| ) | |
| assert CHECKER.check_release(tmp_path) == [] | |
| def test_evaluation_runtime_still_rejects_excluded_method_terms(tmp_path: Path) -> None: | |
| evaluator = tmp_path / "eval" / "task" / "eval_vllm.py" | |
| evaluator.parent.mkdir(parents=True) | |
| legacy_name = "".join(("build_", "g", "t", "_response")) | |
| evaluator.write_text(f"def {legacy_name}():\n pass\n", encoding="utf-8") | |
| assert any("excluded method term" in reason for reason in _reasons(tmp_path)) | |
| def test_generated_test_caches_fail_the_source_gate(tmp_path: Path) -> None: | |
| cache = tmp_path / ".pytest_cache" | |
| cache.mkdir() | |
| (cache / "state").write_text("generated\n", encoding="utf-8") | |
| (tmp_path / "README.md").write_text("portable source\n", encoding="utf-8") | |
| findings = CHECKER.check_release(tmp_path) | |
| assert len(findings) == 1 | |
| assert findings[0].path == Path(".pytest_cache") | |
| assert findings[0].reason == "generated directory" | |
| def test_gitignored_generated_state_is_not_part_of_release(tmp_path: Path) -> None: | |
| cache = tmp_path / ".pytest_cache" | |
| cache.mkdir() | |
| (cache / "state").write_text("generated\n", encoding="utf-8") | |
| (tmp_path / ".gitignore").write_text(".pytest_cache/\n", encoding="utf-8") | |
| assert CHECKER.check_release(tmp_path) == [] | |
| def test_gitignored_training_runs_are_not_part_of_release(tmp_path: Path) -> None: | |
| run = tmp_path / "runs" / "smoke-training" / "grpo" | |
| run.mkdir(parents=True) | |
| (run / "smoke.log").write_text("/private/training/path\n", encoding="utf-8") | |
| (tmp_path / ".gitignore").write_text("/runs/\n", encoding="utf-8") | |
| assert CHECKER.check_release(tmp_path) == [] | |
| def test_local_checkpoint_tree_is_outside_release_scan(tmp_path: Path) -> None: | |
| checkpoint = tmp_path / "checkpoint" / "Video-ORA-9B" | |
| checkpoint.mkdir(parents=True) | |
| (checkpoint / "model.safetensors").write_bytes(b"local weights") | |
| (tmp_path / "README.md").write_text("portable source\n", encoding="utf-8") | |
| assert CHECKER.check_release(tmp_path) == [] | |
| def test_documented_release_media_is_allowlisted(tmp_path: Path) -> None: | |
| assets = tmp_path / "assets" | |
| assets.mkdir() | |
| (assets / "orarl-data-scaling.gif").write_bytes(b"documented data animation") | |
| (assets / "orarl-hero.gif").write_bytes(b"documented preview") | |
| (assets / "orarl-method.gif").write_bytes(b"documented method animation") | |
| (assets / "orarl-model-scaling.gif").write_bytes(b"documented model animation") | |
| (assets / "paper-results.png").write_bytes(b"documented figure") | |
| (tmp_path / "orarl.pdf").write_bytes(b"documented paper") | |
| assert CHECKER.check_release(tmp_path) == [] | |
| def test_private_paths_and_secret_values_are_detected(tmp_path: Path) -> None: | |
| private_path = "/" + "mnt" + "/person/project/model" | |
| access_value = "AKIA" + ("A" * 16) | |
| (tmp_path / "settings.txt").write_text( | |
| f"model={private_path}\ncloud={access_value}\n", | |
| encoding="utf-8", | |
| ) | |
| reasons = _reasons(tmp_path) | |
| assert any("private absolute path" in reason for reason in reasons) | |
| assert any("credential-like value" in reason for reason in reasons) | |
| def test_generated_large_and_broken_entries_are_detected(tmp_path: Path) -> None: | |
| (tmp_path / "weights.pt").write_bytes(b"payload") | |
| (tmp_path / "large.txt").write_text("x" * 17, encoding="utf-8") | |
| (tmp_path / "missing-link").symlink_to(tmp_path / "not-there") | |
| reasons = _reasons(tmp_path, max_bytes=16) | |
| assert "generated or binary artifact" in reasons | |
| assert any("file is too large" in reason for reason in reasons) | |
| assert "broken symbolic link" in reasons | |