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
| """Guard the bundled trainer against packaging drift and legacy naming.""" | |
| from __future__ import annotations | |
| import ast | |
| from pathlib import Path | |
| from typing import Iterator | |
| import tomllib | |
| import yaml | |
| RELEASE_ROOT = Path(__file__).resolve().parents[1] | |
| RUNTIME_ROOT = RELEASE_ROOT / "verl" | |
| # Internal vocabulary that must not resurface in the public runtime. | |
| FORBIDDEN_RUNTIME_TERMS = ( | |
| "".join(("c", "p", "p", "o")), | |
| "".join(("g", "t", "p", "o")), | |
| "".join(("g", "t", "_")), | |
| "".join(("lu", "ff", "y")), | |
| "easy_r1", | |
| ) | |
| def _runtime_sources() -> Iterator[Path]: | |
| return (path for path in sorted(RUNTIME_ROOT.rglob("*.py"))) | |
| def test_runtime_ships_with_the_release() -> None: | |
| assert (RUNTIME_ROOT / "trainer" / "main.py").is_file() | |
| assert (RUNTIME_ROOT / "trainer" / "ray_trainer.py").is_file() | |
| assert (RUNTIME_ROOT / "workers" / "fsdp_workers.py").is_file() | |
| pyproject = tomllib.loads((RELEASE_ROOT / "pyproject.toml").read_text(encoding="utf-8")) | |
| assert "verl*" in pyproject["tool"]["setuptools"]["packages"]["find"]["include"] | |
| manifest = (RELEASE_ROOT / "MANIFEST.in").read_text(encoding="utf-8") | |
| assert "recursive-include verl *.py" in manifest | |
| def test_orarl_recipes_declare_every_oracle_stage() -> None: | |
| algorithm_config = ast.parse( | |
| (RUNTIME_ROOT / "trainer" / "config.py").read_text(encoding="utf-8") | |
| ) | |
| declared = { | |
| statement.target.id | |
| for node in ast.walk(algorithm_config) | |
| if isinstance(node, ast.ClassDef) and node.name == "AlgorithmConfig" | |
| for statement in node.body | |
| if isinstance(statement, ast.AnnAssign) and isinstance(statement.target, ast.Name) | |
| } | |
| for name in ("orarl_4b.yaml", "orarl_9b.yaml"): | |
| payload = yaml.safe_load((RELEASE_ROOT / "configs" / name).read_text(encoding="utf-8")) | |
| algorithm = payload["algorithm"] | |
| assert algorithm["oracle_builder"].startswith("orarl.rewards:") | |
| assert payload["worker"]["reward"]["reward_function"].startswith("orarl.rewards:") | |
| for stage in ( | |
| "oracle_injection", | |
| "directional_gain", | |
| "detached_oracle_advantage", | |
| "selection_prune_ratio", | |
| "post_selection_recenter", | |
| ): | |
| assert stage in algorithm | |
| assert stage in declared | |
| def test_runtime_uses_the_public_vocabulary() -> None: | |
| offenders: list[str] = [] | |
| for path in _runtime_sources(): | |
| lowered = path.read_text(encoding="utf-8").casefold() | |
| for term in FORBIDDEN_RUNTIME_TERMS: | |
| if term in lowered: | |
| offenders.append(f"{path.relative_to(RELEASE_ROOT)}: {term}") | |
| assert not offenders, offenders | |
| def test_trainer_entry_point_is_the_bundled_module() -> None: | |
| launcher = (RELEASE_ROOT / "orarl" / "cli" / "train.py").read_text(encoding="utf-8") | |
| assert '"verl.trainer.main"' in launcher | |
| main_source = (RUNTIME_ROOT / "trainer" / "main.py").read_text(encoding="utf-8") | |
| tree = ast.parse(main_source) | |
| functions = {node.name for node in ast.walk(tree) if isinstance(node, ast.FunctionDef)} | |
| assert "main" in functions | |