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
File size: 4,013 Bytes
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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Contain small python utility functions
"""
import importlib.metadata
import importlib.util
import os
import re
from contextlib import contextmanager
from functools import lru_cache
from typing import Any, Optional, Union
import numpy as np
import yaml
from codetiming import Timer
from packaging import version
from yaml import Dumper
def is_sci_notation(number: float) -> bool:
pattern = re.compile(r"^[+-]?\d+(\.\d*)?[eE][+-]?\d+$")
return bool(pattern.match(str(number)))
def float_representer(dumper: Dumper, number: Union[float, np.float32, np.float64]):
if is_sci_notation(number):
value = str(number)
if "." not in value and "e" in value:
value = value.replace("e", ".0e", 1)
else:
value = str(round(number, 3))
return dumper.represent_scalar("tag:yaml.org,2002:float", value)
yaml.add_representer(float, float_representer)
yaml.add_representer(np.float32, float_representer)
yaml.add_representer(np.float64, float_representer)
@lru_cache
def is_package_available(name: str) -> bool:
return importlib.util.find_spec(name) is not None
def get_package_version(name: str) -> "version.Version":
try:
return version.parse(importlib.metadata.version(name))
except Exception:
return version.parse("0.0.0")
@lru_cache
def is_transformers_version_greater_than(content: str):
return get_package_version("transformers") >= version.parse(content)
def union_two_dict(dict1: dict[str, Any], dict2: dict[str, Any]) -> dict[str, Any]:
"""Union two dict. Will throw an error if there is an item not the same object with the same key."""
for key in dict2.keys():
if key in dict1:
assert dict1[key] == dict2[key], f"{key} in dict1 and dict2 are not the same object"
dict1[key] = dict2[key]
return dict1
def append_to_dict(data: dict[str, list[Any]], new_data: dict[str, Any]) -> None:
"""Append dict to a dict of list."""
for key, val in new_data.items():
if key not in data:
data[key] = []
data[key].append(val)
def unflatten_dict(data: dict[str, Any], sep: str = "/") -> dict[str, Any]:
unflattened = {}
for key, value in data.items():
pieces = key.split(sep)
pointer = unflattened
for piece in pieces[:-1]:
if piece not in pointer:
pointer[piece] = {}
pointer = pointer[piece]
pointer[pieces[-1]] = value
return unflattened
def flatten_dict(data: dict[str, Any], parent_key: str = "", sep: str = "/") -> dict[str, Any]:
flattened = {}
for key, value in data.items():
new_key = parent_key + sep + key if parent_key else key
if isinstance(value, dict):
flattened.update(flatten_dict(value, new_key, sep=sep))
else:
flattened[new_key] = value
return flattened
def convert_dict_to_str(data: dict[str, Any]) -> str:
return yaml.dump(data, indent=2)
def get_abs_path(path: str, prompt: str = "File") -> Optional[str]:
if path is not None:
if os.path.exists(path): # ray job uses absolute path
return os.path.abspath(path)
else:
print(f"{prompt} {path} not found.")
@contextmanager
def timer(name: str, timing_raw: dict[str, float]):
with Timer(name=name, logger=None) as timer:
yield
timing_raw[name] = timer.last
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