Instructions to use BroAlanTaps/Stage1-PCC-Lite-64x with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BroAlanTaps/Stage1-PCC-Lite-64x with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BroAlanTaps/Stage1-PCC-Lite-64x")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BroAlanTaps/Stage1-PCC-Lite-64x") model = AutoModelForCausalLM.from_pretrained("BroAlanTaps/Stage1-PCC-Lite-64x") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e526207b063629c08bccccebb2f6f6c27d2a4dcf271f2c0a78f13a84294ce524
- Size of remote file:
- 1.59 GB
- SHA256:
- 2ee67cd288983af9d09d9d804c9775bf517ace5da5a6e15b124080276a98b203
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.