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
Commit History
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Add metadata and paper/code links for FantasyVLN
Verified signatureHi! I'm Niels, part of the community science team at Hugging Face. I've opened this PR to improve the model card for FantasyVLN. This includes: - Adding the `robotics` pipeline tag. - Adding `library_name: transformers` based on the Qwen2.5-VL architecture. - Linking the paper, project page, and GitHub repository for better accessibility. - Adding a BibTeX citation section. Feel free to merge if this looks good!cfb8be1
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Update README.md
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Update README.md
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Upload folder using huggingface_hub
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initial commit
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