Instructions to use ArthurZ/von with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArthurZ/von with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ArthurZ/von", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ArthurZ/von: direct link, hf CLI and curl.
- Browser
- Download file 1.07 kB
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https://huggingface.co/ArthurZ/von/resolve/main/README.md
- Command line
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hf download hf://ArthurZ/von/README.md
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curl -L -o README.md https://huggingface.co/ArthurZ/von/resolve/main/README.md
1.07 kB
metadata
license: apache-2.0
base_model: wfzyx/von
library_name: transformers
tags:
- decision
- systemone
wfzyx/von: decision files
Chat template, settings and head of wfzyx/von for pipeline("decision")
(transformers#49566). The backbone stays the original checkpoint.
from transformers import pipeline
decide = pipeline("decision", model="wfzyx/von", decision_config="ArthurZ/von")
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/:VonDecisionHead, converted fromoption_marker.ptof the original.decision_test.json: a request and the input ids that the original code builds for it, used by the tests.
Derived from wfzyx/von (Apache-2.0).