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
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from __future__ import annotations
import argparse
import json
import sys
from collections.abc import Sequence
from orarl.evaluation.card import (
DEFAULT_DATASET_REPO_ID,
write_huggingface_metadata,
)
from orarl.evaluation.hub import upload_evaluation_repository
from orarl.evaluation.staging import (
build_evaluation_repository,
export_evaluation_index,
export_public_evaluation_repository,
inventory_and_write_locked_manifest,
inventory_evaluation_sources,
merge_evaluation_repository,
validate_staged_repository,
)
def _add_checksum_option(parser: argparse.ArgumentParser) -> None:
group = parser.add_mutually_exclusive_group()
group.add_argument(
"--checksums",
dest="checksums",
action="store_true",
help="Opt in to SHA-256 generation or verification.",
)
group.add_argument(
"--skip-checksums",
dest="checksums",
action="store_false",
help=argparse.SUPPRESS,
)
parser.set_defaults(checksums=False)
def create_parser() -> argparse.ArgumentParser:
"""Create the dependency-light ``orarl-eval-data`` command parser."""
parser = argparse.ArgumentParser(prog="orarl-eval-data", description=__doc__)
commands = parser.add_subparsers(dest="command", required=True)
inventory = commands.add_parser(
"inventory",
help="Read source rows and referenced asset sizes without writing output.",
)
inventory.add_argument("manifest_path", nargs="?")
inventory.add_argument(
"--manifest",
"--source-manifest",
dest="manifest",
help="Local JSONL source manifest.",
)
inventory.add_argument(
"--write-locked-manifest",
metavar="FILE",
help="Write a private deterministic manifest with actual converted row counts.",
)
inventory.add_argument(
"--task",
action="append",
dest="tasks",
help="Inventory only one eval task; repeat to select multiple tasks.",
)
inventory.add_argument(
"--workers",
type=int,
default=8,
help="Parallel source-conversion workers (default: 8).",
)
_add_checksum_option(inventory)
build = commands.add_parser(
"build",
help="Build an atomic canonical evaluation repository.",
)
build.add_argument("manifest_path", nargs="?")
build.add_argument("output_path", nargs="?")
build.add_argument(
"--manifest",
"--source-manifest",
dest="manifest",
help="Local JSONL source manifest.",
)
build.add_argument("--output", dest="output", help="Target repository directory.")
build.add_argument(
"--copy-mode",
choices=("copy", "hardlink"),
default="copy",
help="Stage files by copying or hard-linking with copy fallback.",
)
build.add_argument("--overwrite", action="store_true")
build.add_argument(
"--workers",
type=int,
default=8,
help="Parallel source-conversion workers (default: 8).",
)
_add_checksum_option(build)
merge = commands.add_parser(
"merge",
help="Add a task repository to an existing canonical repository.",
)
merge.add_argument("--source", required=True, help="Validated task repository.")
merge.add_argument(
"--target",
"--output",
dest="target",
required=True,
help="Existing cumulative evaluation repository.",
)
merge.add_argument(
"--copy-mode",
choices=("copy", "hardlink"),
default="hardlink",
help="Add files by hard-linking with copy fallback, or always copy.",
)
merge.add_argument(
"--repair-missing-asset-manifest",
action="store_true",
help=(
"Reconstruct a deleted target assets.jsonl from staged paths and "
"byte sizes without hashing payloads."
),
)
merge.add_argument(
"--workers",
type=int,
default=8,
help="Parallel file metadata and hardlink workers (default: 8).",
)
merge.add_argument(
"--replace-benchmark",
action="append",
default=[],
help=(
"Replace every existing split and asset for this benchmark; repeat "
"for multiple benchmarks."
),
)
export_index = commands.add_parser(
"export-index",
help="Export portable JSONL metadata while keeping large assets external.",
)
export_index.add_argument(
"--root",
required=True,
help="Complete validated evaluation repository.",
)
export_index.add_argument(
"--output",
required=True,
help="Target JSONL metadata directory.",
)
export_index.add_argument("--overwrite", action="store_true")
_add_checksum_option(export_index)
export_public = commands.add_parser(
"export-public",
help="Export annotations and raw media while excluding processed artifacts.",
)
export_public.add_argument(
"--root",
required=True,
help="Complete validated evaluation repository.",
)
export_public.add_argument(
"--output",
required=True,
help="Target public evaluation repository.",
)
export_public.add_argument(
"--copy-mode",
choices=("copy", "hardlink"),
default="hardlink",
help="Stage raw media by hard-linking with copy fallback, or always copy.",
)
export_public.add_argument("--overwrite", action="store_true")
export_public.add_argument(
"--workers",
type=int,
default=8,
help="Parallel raw-media staging workers (default: 8).",
)
_add_checksum_option(export_public)
metadata = commands.add_parser(
"metadata",
help="Regenerate and validate Hugging Face README and LFS metadata.",
)
metadata.add_argument("root_path", nargs="?")
metadata.add_argument("--root", help="Staged evaluation repository.")
metadata.add_argument(
"--repo-id",
default=DEFAULT_DATASET_REPO_ID,
help="Hugging Face repository id used in the generated card.",
)
_add_checksum_option(metadata)
validate = commands.add_parser(
"validate",
help="Validate manifests, rows, references, and byte sizes.",
)
validate.add_argument("root_path", nargs="?")
validate.add_argument("--root", help="Staged evaluation repository.")
validate.add_argument(
"--allow-unauthorized",
action="store_true",
help="Validate local structure without requiring redistribution authorization.",
)
validate.add_argument(
"--repo-id",
default=DEFAULT_DATASET_REPO_ID,
help="Hugging Face repository id used by generated metadata.",
)
_add_checksum_option(validate)
upload = commands.add_parser(
"upload",
help="Validate and resumably upload a complete Hugging Face dataset repository.",
)
upload.add_argument("--root", required=True, help="Staged evaluation repository.")
upload.add_argument("--repo-id", required=True, help="Hugging Face dataset repository id.")
upload.add_argument(
"--revision",
default="main",
help="Target Hugging Face branch or revision (default: main).",
)
_add_checksum_option(upload)
upload.add_argument(
"--private",
action="store_true",
help="Create the dataset repository as private if it does not exist.",
)
upload.add_argument(
"--num-workers",
type=int,
help="Optional upload worker count.",
)
return parser
def _required(option: str | None, positional: str | None, label: str) -> str:
if option and positional and option != positional:
raise ValueError(f"conflicting {label} values")
value = option or positional
if not value:
raise ValueError(f"{label} is required")
return value
def main(argv: Sequence[str] | None = None) -> int:
"""Run one evaluation-data staging command."""
args = create_parser().parse_args(argv)
try:
if args.command == "inventory":
manifest = _required(args.manifest, args.manifest_path, "source manifest")
if args.write_locked_manifest:
result = inventory_and_write_locked_manifest(
manifest,
args.write_locked_manifest,
tasks=args.tasks,
workers=args.workers,
checksums=args.checksums,
)
else:
result = inventory_evaluation_sources(
manifest,
tasks=args.tasks,
workers=args.workers,
checksums=args.checksums,
)
elif args.command == "build":
manifest = _required(args.manifest, args.manifest_path, "source manifest")
output = _required(args.output, args.output_path, "output")
result = build_evaluation_repository(
manifest,
output,
copy_mode=args.copy_mode,
overwrite=args.overwrite,
workers=args.workers,
checksums=args.checksums,
)
elif args.command == "merge":
result = merge_evaluation_repository(
args.source,
args.target,
copy_mode=args.copy_mode,
repair_missing_asset_manifest=args.repair_missing_asset_manifest,
workers=args.workers,
replace_benchmarks=args.replace_benchmark,
)
elif args.command == "metadata":
root = _required(args.root, args.root_path, "repository root")
metadata_result = write_huggingface_metadata(
root,
repo_id=args.repo_id,
)
result = {
**validate_staged_repository(
root,
checksums=args.checksums,
repo_id=args.repo_id,
),
**metadata_result,
}
elif args.command == "validate":
root = _required(args.root, args.root_path, "repository root")
result = validate_staged_repository(
root,
require_redistribution_authorized=not args.allow_unauthorized,
checksums=args.checksums,
repo_id=args.repo_id,
)
elif args.command == "export-index":
result = export_evaluation_index(
args.root,
args.output,
overwrite=args.overwrite,
checksums=args.checksums,
)
elif args.command == "export-public":
result = export_public_evaluation_repository(
args.root,
args.output,
copy_mode=args.copy_mode,
overwrite=args.overwrite,
workers=args.workers,
checksums=args.checksums,
)
else:
result = upload_evaluation_repository(
args.root,
args.repo_id,
revision=args.revision,
private=args.private,
num_workers=args.num_workers,
checksums=args.checksums,
)
except (FileExistsError, OSError, ValueError) as error:
print(f"[FATAL] {error}", file=sys.stderr)
return 2
print(json.dumps(result, ensure_ascii=False, sort_keys=True, indent=2))
return 0
if __name__ == "__main__":
raise SystemExit(main())
|