Instructions to use jadechoghari/wavetoken-libero-tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jadechoghari/wavetoken-libero-tokenizer with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jadechoghari/wavetoken-libero-tokenizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download processor_config.json from jadechoghari/wavetoken-libero-tokenizer: direct link, hf CLI and curl.
- Browser
- Download file 257 Bytes
-
https://huggingface.co/jadechoghari/wavetoken-libero-tokenizer/resolve/main/processor_config.json
- Command line
-
hf download hf://jadechoghari/wavetoken-libero-tokenizer/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/jadechoghari/wavetoken-libero-tokenizer/resolve/main/processor_config.json
257 Bytes
| { | |
| "action_dim": 6, | |
| "auto_map": { | |
| "AutoProcessor": "processing_action_tokenizer.WaveletActionProcessor" | |
| }, | |
| "level": 2, | |
| "min_token": -66, | |
| "processor_class": "WaveletActionProcessor", | |
| "scale": 10.0, | |
| "time_horizon": 10, | |
| "wavelet": "db1" | |
| } | |