Instructions to use BroAlanTaps/Stage1-PCC-Lite-4x with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BroAlanTaps/Stage1-PCC-Lite-4x with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BroAlanTaps/Stage1-PCC-Lite-4x")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BroAlanTaps/Stage1-PCC-Lite-4x") model = AutoModelForCausalLM.from_pretrained("BroAlanTaps/Stage1-PCC-Lite-4x") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3b6acf736f41b8346f7fddbf2bba9c795503b56d7fe75d4fa61305d89970f4b5
- Size of remote file:
- 1.59 GB
- SHA256:
- cf71eaab339dd13ffcbd61b9cec64133294de7bca0afe7bace3c63d051e22031
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