Instructions to use shaViNX/ChatLib2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shaViNX/ChatLib2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="shaViNX/ChatLib2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shaViNX/ChatLib2") model = AutoModelForSequenceClassification.from_pretrained("shaViNX/ChatLib2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 427895dbae42727be909b0ec73400a902f61576769223ae8d9c04873dc982488
- Size of remote file:
- 433 MB
- SHA256:
- 873a57d8db2f8dd816413677d0134ecf1622ea620f97b5dfdfcde9856591f6bb
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