Instructions to use OraRL/Video-ORA-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

Video-ORA-4B / code /orarl /evaluation /__init__.py

lyhisme

Add 4B model card, benchmark assets, and release code

0185029 verified about 13 hours ago

Raw

History Blame Contribute Delete

5.54 kB

	"""Canonical schemas and repository layout for OraRL evaluation data."""

	from .card import (
	DATASET_CARD_FILENAME,
	DEFAULT_DATASET_REPO_ID,
	GIT_ATTRIBUTES_FILENAME,
	DatasetCardError,
	build_dataset_card_metadata,
	dataset_card_metadata,
	dataset_card_subsets,
	generate_dataset_card,
	generate_gitattributes,
	huggingface_dataset_configs,
	render_dataset_card,
	render_gitattributes,
	validate_huggingface_metadata,
	write_huggingface_metadata,
	)
	from .converters import (
	ConversionError,
	ConvertedSource,
	PlannedAsset,
	convert_source,
	convert_sources,
	)
	from .hub import UploadError, upload_evaluation_repository, upload_repository
	from .layout import (
	ANNOTATIONS_DIRECTORY,
	ARTIFACTS_DIRECTORY,
	ASSET_MANIFEST_FILENAME,
	MANIFEST_FILENAME,
	MEDIA_DIRECTORY,
	MEDIA_KINDS,
	LayoutError,
	annotation_path,
	artifact_directory,
	benchmark_directory,
	is_dataset_id,
	media_directory,
	path_is_within,
	sha256_file,
	validate_annotation_path,
	validate_artifact_path,
	validate_dataset_id,
	validate_media_path,
	validate_repository_assets,
	validate_repository_path,
	validate_sha256,
	validate_unique_paths,
	)
	from .manifest import (
	DatasetManifestRecord,
	ManifestError,
	dataset_manifest_record_errors,
	load_dataset_manifest,
	validate_dataset_manifest,
	validate_dataset_manifest_record,
	validate_evaluation_repository,
	)
	from .schema import (
	EVALUATION_CORE_FIELDS,
	EVALUATION_FIELDS,
	EVALUATION_SCHEMA_VERSION,
	EvaluationRow,
	EvaluationSchemaError,
	evaluation_asset_paths,
	evaluation_row_errors,
	load_evaluation_jsonl,
	validate_evaluation_row,
	validate_evaluation_rows,
	)
	from .sources import (
	EvaluationSource,
	EvaluationSourceRecord,
	EvaluationSourceSpec,
	SourceManifestError,
	SourceManifestRecord,
	load_evaluation_source_manifest,
	load_evaluation_sources,
	load_source_manifest,
	)
	from .staging import (
	AssetManifestRecord,
	StagingError,
	asset_manifest_record_errors,
	build_evaluation_data,
	build_evaluation_repository,
	build_repository,
	export_evaluation_index,
	export_public_evaluation_repository,
	inventory_and_write_locked_manifest,
	inventory_evaluation_data,
	inventory_evaluation_repository,
	inventory_evaluation_sources,
	inventory_repository,
	load_asset_manifest,
	load_assets_manifest,
	merge_evaluation_repository,
	validate_asset_manifest,
	validate_asset_manifest_record,
	validate_assets_manifest,
	validate_evaluation_data_repository,
	validate_repository,
	validate_staged_repository,
	write_locked_source_manifest,
	)

	SCHEMA_VERSION = EVALUATION_SCHEMA_VERSION

	__all__ = [
	"ANNOTATIONS_DIRECTORY",
	"ARTIFACTS_DIRECTORY",
	"ASSET_MANIFEST_FILENAME",
	"AssetManifestRecord",
	"DATASET_CARD_FILENAME",
	"DEFAULT_DATASET_REPO_ID",
	"DatasetCardError",
	"ConversionError",
	"ConvertedSource",
	"DatasetManifestRecord",
	"EVALUATION_CORE_FIELDS",
	"EVALUATION_FIELDS",
	"EVALUATION_SCHEMA_VERSION",
	"EvaluationRow",
	"EvaluationSchemaError",
	"EvaluationSource",
	"EvaluationSourceRecord",
	"EvaluationSourceSpec",
	"GIT_ATTRIBUTES_FILENAME",
	"LayoutError",
	"MANIFEST_FILENAME",
	"MEDIA_DIRECTORY",
	"MEDIA_KINDS",
	"ManifestError",
	"PlannedAsset",
	"SCHEMA_VERSION",
	"SourceManifestError",
	"SourceManifestRecord",
	"StagingError",
	"UploadError",
	"annotation_path",
	"artifact_directory",
	"benchmark_directory",
	"asset_manifest_record_errors",
	"build_evaluation_data",
	"build_evaluation_repository",
	"build_dataset_card_metadata",
	"build_repository",
	"convert_source",
	"convert_sources",
	"dataset_manifest_record_errors",
	"dataset_card_metadata",
	"dataset_card_subsets",
	"evaluation_asset_paths",
	"evaluation_row_errors",
	"export_evaluation_index",
	"export_public_evaluation_repository",
	"generate_dataset_card",
	"generate_gitattributes",
	"huggingface_dataset_configs",
	"is_dataset_id",
	"inventory_evaluation_data",
	"inventory_and_write_locked_manifest",
	"inventory_evaluation_repository",
	"inventory_evaluation_sources",
	"inventory_repository",
	"load_asset_manifest",
	"load_assets_manifest",
	"load_dataset_manifest",
	"load_evaluation_source_manifest",
	"load_evaluation_sources",
	"load_evaluation_jsonl",
	"load_source_manifest",
	"media_directory",
	"merge_evaluation_repository",
	"path_is_within",
	"render_dataset_card",
	"render_gitattributes",
	"sha256_file",
	"validate_annotation_path",
	"validate_artifact_path",
	"validate_asset_manifest",
	"validate_asset_manifest_record",
	"validate_assets_manifest",
	"validate_dataset_id",
	"validate_dataset_manifest",
	"validate_dataset_manifest_record",
	"validate_evaluation_repository",
	"validate_evaluation_data_repository",
	"validate_evaluation_row",
	"validate_evaluation_rows",
	"validate_media_path",
	"validate_repository_assets",
	"validate_repository_path",
	"validate_sha256",
	"validate_repository",
	"validate_staged_repository",
	"validate_huggingface_metadata",
	"validate_unique_paths",
	"upload_evaluation_repository",
	"upload_repository",
	"write_huggingface_metadata",
	"write_locked_source_manifest",
	]