Text Classification
Transformers
Safetensors
Laya
system-one
calibrated-decisions
rlcd
classification
routing
scoring
guardrails
moderation
reinforcement-learning
commercial-use
Instructions to use SwagMessiah100/laya with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SwagMessiah100/laya with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SwagMessiah100/laya")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SwagMessiah100/laya", device_map="auto") - Laya
How to use SwagMessiah100/laya with Laya:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Download assets/laya_vs_jev.png from SwagMessiah100/laya: direct link, hf CLI and curl.
- Browser
- Download file 216 kB
-
https://huggingface.co/SwagMessiah100/laya/resolve/main/assets/laya_vs_jev.png
- Command line
-
hf download hf://SwagMessiah100/laya/assets/laya_vs_jev.png
-
curl -L -o laya_vs_jev.png https://huggingface.co/SwagMessiah100/laya/resolve/main/assets/laya_vs_jev.png
216 kB

- Xet hash:
- bfcb9833f043da9f4abd2046ffab1d694afc37ea8df8226bbdc2cdc33c3ff268
- Size of remote file:
- 216 kB
- SHA256:
- 5c06517ea7f3e5f3f84873ddaa3cb470f101ad0276f921c58886f8fb12fbe0a8
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