Text Classification
Transformers
Safetensors
English
gemma4
image-text-to-text
decision-model
system-one
gemma-4
Instructions to use arcbright/elf-1-31b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arcbright/elf-1-31b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="arcbright/elf-1-31b")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("arcbright/elf-1-31b") model = AutoModelForMultimodalLM.from_pretrained("arcbright/elf-1-31b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 799 Bytes
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"state": {
"ticket": "Payment page returns a 500 error for every card payment since 09:00."
},
"questions": {
"severity": {
"type": "choice",
"instructions": "How severe is this ticket?",
"criteria": {
"low": "Cosmetic or minor, no customer impact",
"medium": "Some customers affected, workaround exists",
"high": "Many customers affected, no workaround"
}
},
"escalate": {
"type": "noul",
"instructions": "Should this ticket be escalated to the on-call engineer?"
},
"urgency": {
"type": "score",
"instructions": "Rate the urgency from 0 to 3.",
"criteria": [
"Can wait a week",
"Handle this week",
"Handle today",
"Handle immediately"
]
}
}
}
|