Instructions to use acvlab/FantasyVLN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use acvlab/FantasyVLN with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("acvlab/FantasyVLN") model = AutoModelForMultimodalLM.from_pretrained("acvlab/FantasyVLN", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from acvlab/FantasyVLN: direct link, hf CLI and curl.
- Browser
- Download file 12.2 MB
-
https://huggingface.co/acvlab/FantasyVLN/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://acvlab/FantasyVLN@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/acvlab/FantasyVLN/resolve/refs%2Fpr%2F1/tokenizer.json
12.2 MB
- Xet hash:
- 7884376ccb077eb6b4398c6074f824881f40db3d95f2b0b2cfce72aad295256e
- Size of remote file:
- 12.2 MB
- SHA256:
- 1731e0bd5f76349e757207011dc78c8e1e0ca5ee707b25c225d9129077660faa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.