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: 2,440 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 | from __future__ import annotations
import json
import pickle
import pytest
pytest.importorskip("torch")
pytest.importorskip("qwen_vl_utils")
from verl.utils.dataset import ( # noqa: E402
LocalJsonlDataset,
_align_media_placeholders,
)
def test_local_jsonl_preserves_task_specific_columns(tmp_path) -> None:
path = tmp_path / "mixed.jsonl"
rows = [
{
"problem": "temporal prompt",
"answer": "<answer>1 to 2</answer>",
"problem_type": "temporal grounding",
"videos": ["/media/a.mp4"],
"pred_span": [1.0, 2.0],
},
{
"problem": "segmentation prompt",
"answer": "<answer>{}</answer>",
"problem_type": "segmentation",
"videos": ["/media/b.mp4"],
"segmentation_output": {"object_id": 1},
},
]
path.write_text(
"".join(json.dumps(row) + "\n" for row in rows),
encoding="utf-8",
)
dataset = LocalJsonlDataset(str(path))
assert len(dataset) == 2
assert dataset[0]["pred_span"] == [1.0, 2.0]
assert dataset[1]["segmentation_output"] == {"object_id": 1}
assert "segmentation_output" not in dataset[0]
def test_local_jsonl_filter_and_pickle_keep_random_access(tmp_path) -> None:
path = tmp_path / "mixed.jsonl"
path.write_text(
"\n".join(
[
json.dumps({"problem_type": "tracking", "value": 1}),
"",
json.dumps({"problem_type": "video_qa_mc", "value": 2}),
]
)
+ "\n",
encoding="utf-8",
)
dataset = LocalJsonlDataset(str(path))
filtered = dataset.filter(lambda row: row["value"] == 2)
restored = pickle.loads(pickle.dumps(filtered))
assert len(dataset) == 2
assert len(restored) == 1
assert restored[0] == {"problem_type": "video_qa_mc", "value": 2}
def test_surplus_media_placeholders_are_removed() -> None:
prompt = "<image> <image> <image> Question with <image> literal tail"
aligned = _align_media_placeholders(prompt, "<image>", media_count=2)
assert aligned.count("<image>") == 2
assert "Question with" in aligned
assert "literal tail" in aligned
def test_missing_media_placeholders_are_rejected() -> None:
with pytest.raises(ValueError, match="1 <video> placeholder"):
_align_media_placeholders("<video> Question", "<video>", media_count=2)
|