Instructions to use ArthurZ/GEV-26B-Decide with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArthurZ/GEV-26B-Decide with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ArthurZ/GEV-26B-Decide", device_map="auto") - Notebooks
- Google Colab
- Kaggle
autotrust/GEV-26B-Decide: decision files
Chat template, settings and head of autotrust/GEV-26B-Decide for pipeline("decision")
(transformers#49566). The backbone is google/gemma-4-26B-A4B-it with the System 1 LoRA of the original (adapter_model.safetensors), loaded by from_pretrained.
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/GEV-26B-Decide", decision_config="ArthurZ/GEV-26B-Decide")
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 fromhead.safetensorsof 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/GEV-26B-Decide (Apache-2.0).
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Model tree for ArthurZ/GEV-26B-Decide
Base model
google/gemma-4-26B-A4B Finetuned
google/gemma-4-26B-A4B-it Adapter
autotrust/GEV-26B-Decide