Instructions to use ArthurZ/JEV-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArthurZ/JEV-9B with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ArthurZ/JEV-9B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download decision_test.json from ArthurZ/JEV-9B: direct link, hf CLI and curl.
- Browser
- Download file 2.41 kB
-
https://huggingface.co/ArthurZ/JEV-9B/resolve/main/decision_test.json
- Command line
-
hf download hf://ArthurZ/JEV-9B/decision_test.json
-
curl -L -o decision_test.json https://huggingface.co/ArthurZ/JEV-9B/resolve/main/decision_test.json
2.41 kB
| { | |
| "model": "autotrust/JEV-9B", | |
| "revision": "7ac669b8fb476ba926491e3d1c39f9330d68dbed", | |
| "request": { | |
| "state": { | |
| "message": "Charged twice, I want a refund.", | |
| "plan": "pro" | |
| }, | |
| "questions": { | |
| "intent": { | |
| "type": "choice", | |
| "instructions": "What does the customer want?", | |
| "criteria": { | |
| "refund": "money back", | |
| "track": "where is my order", | |
| "other": null | |
| } | |
| }, | |
| "urgent": { | |
| "type": "noul", | |
| "instructions": "Needs a human now?" | |
| }, | |
| "anger": { | |
| "type": "score", | |
| "instructions": "How angry?", | |
| "criteria": [ | |
| "none", | |
| "slight", | |
| "some", | |
| "clear", | |
| "strong", | |
| "extreme" | |
| ] | |
| } | |
| } | |
| }, | |
| "sequences": [ | |
| { | |
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| "marks": [ | |
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| } |