Instructions to use hcisbmm/vial_fast_tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hcisbmm/vial_fast_tokenizer with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hcisbmm/vial_fast_tokenizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download processor_config.json from hcisbmm/vial_fast_tokenizer: direct link, hf CLI and curl.
- Browser
- Download file 250 Bytes
-
https://huggingface.co/hcisbmm/vial_fast_tokenizer/resolve/main/processor_config.json
- Command line
-
hf download hf://hcisbmm/vial_fast_tokenizer/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/hcisbmm/vial_fast_tokenizer/resolve/main/processor_config.json
250 Bytes
| { | |
| "action_dim": 14, | |
| "auto_map": { | |
| "AutoProcessor": "processing_action_tokenizer.UniversalActionProcessor" | |
| }, | |
| "min_token": -32, | |
| "processor_class": "UniversalActionProcessor", | |
| "scale": 10.0, | |
| "time_horizon": 10, | |
| "vocab_size": 1024 | |
| } | |