Video-Text-to-Text
Transformers
Safetensors
English
gemma4
image-text-to-text
video-captioning
multimodal
gemma
parakeet
Instructions to use SulphurAI/sulphur-caption with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SulphurAI/sulphur-caption with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("SulphurAI/sulphur-caption") model = AutoModelForMultimodalLM.from_pretrained("SulphurAI/sulphur-caption", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 397 Bytes
7011765 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | set -euo pipefail
cd "$(dirname "$0")"
if [ ! -d vllm ]; then
if [ ! -f vllm.zip ]; then
echo "Missing vllm/ and vllm.zip" >&2
exit 1
fi
unzip -q vllm.zip
fi
python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip setuptools wheel
pip install -r requirements.txt
python - <<'PY'
import vllm
print("vLLM import OK:", getattr(vllm, "__version__", "unknown"))
PY
|