Instructions to use ghazishazan/VideoMolmo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ghazishazan/VideoMolmo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="ghazishazan/VideoMolmo", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("ghazishazan/VideoMolmo", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from ghazishazan/VideoMolmo: direct link, hf CLI and curl.
- Browser
- Download file 11.5 MB
-
https://huggingface.co/ghazishazan/VideoMolmo/resolve/refs%2Fpr%2F2/tokenizer.json
- Command line
-
hf download hf://ghazishazan/VideoMolmo@refs/pr/2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ghazishazan/VideoMolmo/resolve/refs%2Fpr%2F2/tokenizer.json
11.5 MB
- Xet hash:
- f6a781fc0392179733858260b7869dcec1424544ee7a2b0c9382e2f35c7f8208
- Size of remote file:
- 11.5 MB
- SHA256:
- 6248048a83152ce87663c799492fe7e60c8086f3ae51ce7bd255ccc445746fc0
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