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
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Download README.md from ArthurZ/GEV-26B-Decide: direct link, hf CLI and curl.
- Browser
- Download file 1.39 kB
-
https://huggingface.co/ArthurZ/GEV-26B-Decide/resolve/main/README.md
- Command line
-
hf download hf://ArthurZ/GEV-26B-Decide/README.md
-
curl -L -o README.md https://huggingface.co/ArthurZ/GEV-26B-Decide/resolve/main/README.md
1.39 kB
metadata
license: apache-2.0
base_model: autotrust/GEV-26B-Decide
library_name: transformers
tags:
- decision
- systemone
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).