Text Classification
PEFT
Safetensors
English
Chinese
openjev
lora
custom-code
experimental
probabilistic-decisions
Instructions to use IamBusy/OpenJev-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use IamBusy/OpenJev-0.6B with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B") model = PeftModel.from_pretrained(base_model, "IamBusy/OpenJev-0.6B") - Notebooks
- Google Colab
- Kaggle
Download example_request.json from IamBusy/OpenJev-0.6B: direct link, hf CLI and curl.
- Browser
- Download file 1.28 kB
-
https://huggingface.co/IamBusy/OpenJev-0.6B/resolve/main/example_request.json
- Command line
-
hf download hf://IamBusy/OpenJev-0.6B/example_request.json
-
curl -L -o example_request.json https://huggingface.co/IamBusy/OpenJev-0.6B/resolve/main/example_request.json
1.28 kB
| { | |
| "state": "I used my debit card yesterday, but the merchant charged the same payment twice. Please help me recover the duplicate payment.", | |
| "questions": { | |
| "department": { | |
| "type": "choice", | |
| "instructions": "Which service best matches the customer's request?", | |
| "criteria": { | |
| "duplicate_payment": "The customer is asking about a card payment charged twice.", | |
| "cash_withdrawal": "The customer is asking about cash withdrawal at an ATM.", | |
| "card_delivery": "The customer is asking about the arrival of a new card.", | |
| "pin_change": "The customer is asking about changing a card PIN." | |
| } | |
| }, | |
| "duplicate_charge": { | |
| "type": "noul", | |
| "instructions": "The customer is asking about a card payment charged twice.", | |
| "criteria": { | |
| "false": "The customer is not asking about a card payment charged twice.", | |
| "true": "The customer is asking about a card payment charged twice." | |
| } | |
| }, | |
| "sentiment": { | |
| "type": "score", | |
| "instructions": "Rate the sentiment from negative to positive.", | |
| "criteria": [ | |
| "The text expresses negative sentiment.", | |
| "The text expresses neutral sentiment.", | |
| "The text expresses positive sentiment." | |
| ] | |
| } | |
| } | |
| } | |