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
File size: 1,339 Bytes
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license: apache-2.0
base_model: autotrust/JEV-9B
library_name: transformers
tags:
- decision
- systemone
---
# autotrust/JEV-9B: decision files
Chat template, settings and head of [autotrust/JEV-9B](https://huggingface.co/autotrust/JEV-9B) for `pipeline("decision")`
([transformers#49566](https://github.com/huggingface/transformers/pull/49566)). The backbone is the base model at the root of the original with the System 1 LoRA of the original (`adapter_model.safetensors`), loaded by `from_pretrained`.
```python
from transformers import pipeline
# the LoRA at the root makes `pipeline` load its base model, so the decision files are passed explicitly
decide = pipeline("decision", model="ArthurZ/JEV-9B", decision_config="ArthurZ/JEV-9B")
decide({"state": "I was charged twice and want a refund.", "questions": {"refund": {"type": "noul", "instructions": "Is a refund requested?"}}})
```
- `decision_template.jinja`: the chat template; `<|mark|>` marks the positions the head reads.
- `decision_config.json`: temperatures and option order.
- `decision_head/`: `JevDecisionHead`, converted from `head.safetensors` of the original.
- `decision_test.json`: a request and the input ids that the original code builds for it, used by the tests.
Derived from [autotrust/JEV-9B](https://huggingface.co/autotrust/JEV-9B) (Apache-2.0).
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