Instructions to use entfane/Toxic_Llama8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use entfane/Toxic_Llama8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="entfane/Toxic_Llama8B")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("entfane/Toxic_Llama8B") model = AutoModelForSequenceClassification.from_pretrained("entfane/Toxic_Llama8B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3e02f25235e751464a342a9b8f2a90b6900502a1e7a6f7663d4066268d0fbd05
- Size of remote file:
- 15 GB
- SHA256:
- 1ebb58935734920badbea22e503f1bc66d1303fcb305a83d5c9dc9a39d544a75
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.