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: 7,545 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 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 | #!/usr/bin/env bash
# ReVSI evaluation using the same vLLM multi-node sharding path as VSI-Bench.
set -euo pipefail
SCRIPT_DIR=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)
PROJECT_DIR=$(cd -- "${SCRIPT_DIR}/../.." && pwd)
RUNNER="${SCRIPT_DIR}/revsi/run_eval_vllm.sh"
MERGER="${SCRIPT_DIR}/revsi/merge_multinode_shards.py"
PYTHON_BIN=${PYTHON_BIN:-$(command -v python)}
HOSTFILE=${HOSTFILE:-}
NUM_NODES=${NUM_NODES:-4}
GPUS_PER_NODE=${GPUS_PER_NODE:-8}
MODEL=${MODEL:-}
MODEL=${MODEL%/}
REVSI_ROOT=${REVSI_ROOT:-}
FRAME_BUDGET=${REVSI_FRAME_BUDGET:-all}
FRAME_DIR="${REVSI_ROOT:+${REVSI_ROOT}/${FRAME_BUDGET}_frame}"
QA_FILE=${REVSI_QA_FILE:-"${FRAME_DIR:+${FRAME_DIR}/test-00000-of-00001.parquet}"}
VIDEO_ROOT=${REVSI_VIDEO_ROOT:-${REVSI_ROOT}}
TASK_FILTER=${REVSI_TASK_FILTER:-}
MAX_SAMPLES=${REVSI_MAX_SAMPLES:-0}
REMOTE_SETUP=${REMOTE_SETUP:-}
SSH_STRICT_HOST_KEY_CHECKING=${SSH_STRICT_HOST_KEY_CHECKING:-yes}
case "${FRAME_BUDGET}" in
16|32|64|all) ;;
*)
echo "[FATAL] REVSI_FRAME_BUDGET must be 16, 32, 64, or all." >&2
exit 2
;;
esac
[[ -n "${MODEL}" ]] || {
echo "[FATAL] Set MODEL to the model directory." >&2
exit 2
}
[[ -n "${QA_FILE}" ]] || {
echo "[FATAL] Set REVSI_QA_FILE or REVSI_ROOT." >&2
exit 2
}
[[ -n "${HOSTFILE}" ]] || {
echo "[FATAL] Set HOSTFILE to one host per line." >&2
exit 2
}
for path in \
"${RUNNER}" "${MERGER}" "${HOSTFILE}" "${MODEL}/config.json" "${QA_FILE}"; do
[[ -f "${path}" ]] || {
echo "[FATAL] Required input is unavailable: ${path}" >&2
exit 1
}
done
if [[ "${QA_FILE}" == *.parquet ]]; then
[[ -n "${FRAME_DIR}" ]] || {
echo "[FATAL] Parquet input requires REVSI_ROOT." >&2
exit 2
}
compgen -G "${FRAME_DIR}/*.mp4" >/dev/null || {
echo "[FATAL] No ReVSI videos found under ${FRAME_DIR}." >&2
exit 1
}
fi
if [[ -n "${REVSI_EXPECTED_SAMPLES:-}" ]]; then
EXPECTED_SAMPLES=${REVSI_EXPECTED_SAMPLES}
elif [[ -z "${TASK_FILTER}" && "${MAX_SAMPLES}" == "0" ]]; then
EXPECTED_SAMPLES=6808
else
EXPECTED_SAMPLES=0
fi
mapfile -t AVAILABLE_HOSTS < <(awk 'NF && !seen[$1]++ {print $1}' "${HOSTFILE}")
[[ "${NUM_NODES}" =~ ^[1-9][0-9]*$ ]] || {
echo "[FATAL] NUM_NODES must be a positive integer." >&2
exit 2
}
((NUM_NODES <= ${#AVAILABLE_HOSTS[@]})) || {
echo "[FATAL] Requested ${NUM_NODES} nodes, only ${#AVAILABLE_HOSTS[@]} available." >&2
exit 2
}
HOSTS=("${AVAILABLE_HOSTS[@]:0:NUM_NODES}")
GLOBAL_SHARDS=$((NUM_NODES * GPUS_PER_NODE))
checkpoint_dir=$(dirname "$(dirname "${MODEL%/}")")
checkpoint_tag=$(basename "${checkpoint_dir}")
experiment_tag=$(basename "$(dirname "${checkpoint_dir}")")
timestamp=$(date +%Y%m%d_%H%M%S)
EVAL_MAX_FRAMES=${REVSI_MAX_FRAMES:-${FRAME_BUDGET}}
EVAL_EXACT_NFRAMES=${REVSI_EXACT_NFRAMES:-1}
if [[ "${FRAME_BUDGET}" == "all" ]]; then
EVAL_MAX_FRAMES=${REVSI_MAX_FRAMES:-128}
fi
setting_tag="revsi-${FRAME_BUDGET}frame-multinode${NUM_NODES}x${GPUS_PER_NODE}-f${EVAL_MAX_FRAMES}-exact${EVAL_EXACT_NFRAMES}-fps${REVSI_FPS:-2}-total${REVSI_VIDEO_TOTAL_PIXELS:-16777216}"
OUTPUT_DIR=${OUTPUT_DIR:-"${PROJECT_DIR}/outputs/${experiment_tag}/spatial_intelligence/revsi/${checkpoint_tag}/${setting_tag}/${timestamp}"}
LOG_DIR=${LOG_DIR:-"${PROJECT_DIR}/logs/revsi_multinode/${timestamp}"}
mkdir -p "${OUTPUT_DIR}" "${LOG_DIR}"
SSH_OPTS=(-o "StrictHostKeyChecking=${SSH_STRICT_HOST_KEY_CHECKING}" -o BatchMode=yes -o ServerAliveInterval=60)
WORKER_PATTERN='eval/task/revsi/eval_revsi_vllm.py'
run_on_host() {
local node=$1
shift
if ((node == 0)); then
bash -s <<<"$*"
else
ssh "${SSH_OPTS[@]}" "${HOSTS[node]}" bash -s <<<"$*"
fi
}
kill_stale_workers() {
local node reap_pids=() command
command="pkill -f $(printf %q "${WORKER_PATTERN}") >/dev/null 2>&1 || true; sleep 5; pkill -9 -f $(printf %q "${WORKER_PATTERN}") >/dev/null 2>&1 || true; sleep 3"
for ((node = 0; node < NUM_NODES; node++)); do
run_on_host "${node}" "${command}" >/dev/null 2>&1 &
reap_pids+=("$!")
done
wait "${reap_pids[@]}" 2>/dev/null || true
}
require_free_gpus() {
local node free bad=0
local need_gib=${REVSI_MIN_FREE_GIB:-75}
for ((node = 0; node < NUM_NODES; node++)); do
free=$(run_on_host "${node}" \
"nvidia-smi --query-gpu=memory.free --format=csv,noheader,nounits | sort -n | awk 'NR==1 {print}'" 2>/dev/null |
tr -d '\r' | awk '/^[0-9]+$/ {value=$0} END {print value}')
if [[ -z "${free}" ]]; then
echo "[FATAL] node=${node} host=${HOSTS[node]}: cannot query GPU memory." >&2
bad=1
elif ((free / 1024 < need_gib)); then
echo "[FATAL] node=${node} host=${HOSTS[node]}: only $((free / 1024)) GiB free (need ${need_gib} GiB)." >&2
bad=1
fi
done
((bad == 0)) || exit 1
}
pids=()
cleanup() {
local status=$?
trap - EXIT INT TERM
for pid in "${pids[@]:-}"; do
[[ -n "${pid}" ]] && kill "${pid}" >/dev/null 2>&1 || true
done
((status == 0)) || kill_stale_workers
exit "${status}"
}
trap cleanup EXIT INT TERM
kill_stale_workers
require_free_gpus
dispatch() {
local node=$1
local offset=$((node * GPUS_PER_NODE))
local gpu_ids
gpu_ids=$(seq -s, 0 $((GPUS_PER_NODE - 1)))
local env_values=(
"CUDA_VISIBLE_DEVICES=${gpu_ids}"
"REVSI_ROOT=${REVSI_ROOT}"
"FRAME_BUDGET=${FRAME_BUDGET}"
"QA_FILE=${QA_FILE}"
"VIDEO_ROOT=${VIDEO_ROOT}"
"OUTPUT_DIR=${OUTPUT_DIR}"
"GLOBAL_SHARD_COUNT=${GLOBAL_SHARDS}"
"GLOBAL_SHARD_OFFSET=${offset}"
"MERGE_SHARDS=0"
"EXPECTED_SAMPLES=0"
"TASK_FILTER=${TASK_FILTER}"
"MAX_SAMPLES=${MAX_SAMPLES}"
"MEDIA_MODE=video"
"TP_SIZE=1"
"BATCH_SIZE=${REVSI_BATCH_SIZE:-16}"
"MAX_FRAMES=${EVAL_MAX_FRAMES}"
"EXACT_NFRAMES=${EVAL_EXACT_NFRAMES}"
"FPS=${REVSI_FPS:-2}"
"VIDEO_MIN_PIXELS=${REVSI_VIDEO_MIN_PIXELS:-65536}"
"VIDEO_MAX_PIXELS=${REVSI_VIDEO_MAX_PIXELS:-}"
"VIDEO_TOTAL_PIXELS=${REVSI_VIDEO_TOTAL_PIXELS:-16777216}"
"MAX_MODEL_LEN=${REVSI_MAX_MODEL_LEN:-32768}"
"MAX_NEW_TOKENS=${REVSI_MAX_NEW_TOKENS:-64}"
"GPU_MEM_UTIL=${REVSI_GPU_MEMORY_UTILIZATION:-0.90}"
"STRICT_NUMERIC_PROMPT=${REVSI_STRICT_NUMERIC_PROMPT:-0}"
"ENABLE_THINKING=${REVSI_ENABLE_THINKING:-false}"
"SCORE_LOG_INTERVAL=${REVSI_SCORE_LOG_INTERVAL:-50}"
"LAUNCH_DELAY=2"
"VLLM_BASE_PORT=49000"
)
local prefix="" value
for value in "${env_values[@]}"; do
prefix+="$(printf %q "${value}") "
done
local setup_prefix=""
if [[ -n "${REMOTE_SETUP}" ]]; then
setup_prefix="${REMOTE_SETUP} && "
fi
local command="${setup_prefix}cd $(printf %q "${PROJECT_DIR}") && ${prefix}bash $(printf %q "${RUNNER}") $(printf %q "${MODEL}")"
local log="${LOG_DIR}/node${node}.log"
echo "[revsi] node=${node}/${NUM_NODES} host=${HOSTS[node]} shards=${offset}-$((offset + GPUS_PER_NODE - 1)) log=${log}"
if ((node == 0)); then
bash -lc "${command}" 2>&1 | tee "${log}"
else
ssh "${SSH_OPTS[@]}" "${HOSTS[node]}" \
"bash -lc $(printf %q "${command}")" >"${log}" 2>&1 &
pids+=("$!")
fi
}
for ((node = 1; node < NUM_NODES; node++)); do
dispatch "${node}"
done
dispatch 0
status=0
for pid in "${pids[@]}"; do
if ! wait "${pid}"; then status=1; fi
done
pids=()
((status == 0)) || {
echo "[FATAL] One or more ReVSI nodes failed; inspect ${LOG_DIR}." >&2
exit "${status}"
}
"${PYTHON_BIN}" "${MERGER}" \
--output-dir "${OUTPUT_DIR}" \
--num-shards "${GLOBAL_SHARDS}" \
--expected-samples "${EXPECTED_SAMPLES}"
chmod -R a+rX "${OUTPUT_DIR}" 2>/dev/null || true
echo "[revsi] Multi-node evaluation complete: ${OUTPUT_DIR}/summary.json"
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