Text Classification
Transformers
Safetensors
Laya
system-one
calibrated-decisions
rlcd
classification
routing
scoring
guardrails
moderation
reinforcement-learning
commercial-use
Instructions to use Emerald7664/laya with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Emerald7664/laya with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Emerald7664/laya")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Emerald7664/laya", device_map="auto") - Laya
How to use Emerald7664/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 model.safetensors from Emerald7664/laya: direct link, hf CLI and curl.
- Browser
- Download file 843 MB
-
https://huggingface.co/Emerald7664/laya/resolve/main/model.safetensors
- Command line
-
hf download hf://Emerald7664/laya/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Emerald7664/laya/resolve/main/model.safetensors
843 MB
- Xet hash:
- dd8562d713759821a0bc049e79abd09e5817d218d18fa51478ad16986d018764
- Size of remote file:
- 843 MB
- SHA256:
- 891102d372688fc2a094dac56a384bc537b87c63f21f9f3dac0be2b7cbc8d86c
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