Text Classification
Transformers
Safetensors
English
gemma4_unified
image-text-to-text
decision-model
system-one
gemma-4
Instructions to use arcbright/elf-1-12b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arcbright/elf-1-12b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="arcbright/elf-1-12b")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("arcbright/elf-1-12b") model = AutoModelForMultimodalLM.from_pretrained("arcbright/elf-1-12b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download examples/request.json from arcbright/elf-1-12b: direct link, hf CLI and curl.
- Browser
- Download file 799 Bytes
-
https://huggingface.co/arcbright/elf-1-12b/resolve/main/examples/request.json
- Command line
-
hf download hf://arcbright/elf-1-12b/examples/request.json
-
curl -L -o request.json https://huggingface.co/arcbright/elf-1-12b/resolve/main/examples/request.json
799 Bytes
| { | |
| "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" | |
| ] | |
| } | |
| } | |
| } | |