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
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# Sulphur Caption
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This is a captioning model I made purely to make things easier and cheaper for myself. If you don't like how the model works or it doesn't work on your favorite runtime, I'm sorry, but I'm not going to put any further effort into this, as it works how I want it to. If someone wants to figure out gguf or anything like that, you're welcome to try, you can dm me on discord under the username `kiwv`.
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You're going to need ~70gb vram to run this model. The fp8 variant will require ~38gb.
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# Sulphur Caption
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This is a captioning model I made purely to make things easier and cheaper for myself. If you don't like how the model works or it doesn't work on your favorite runtime, I'm sorry, but I'm not going to put any further effort into this, as it works how I want it to. If someone wants to figure out gguf or anything like that, you're welcome to try, you can dm me on discord under the username `kiwv`. Also it's worth noting this model will not be used in sulphur 3's dataset creation.
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You're going to need ~70gb vram to run this model. The fp8 variant will require ~38gb.
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