Text Classification
Transformers
ONNX
Safetensors
English
qwen3_5_text
text-generation
safety
guardrail
coding-agent
tool-call
command-review
permission
qwen3.5
Instructions to use openvulcan/vulcan-decision-shell-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openvulcan/vulcan-decision-shell-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="openvulcan/vulcan-decision-shell-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("openvulcan/vulcan-decision-shell-v1") model = AutoModelForCausalLM.from_pretrained("openvulcan/vulcan-decision-shell-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download docs/VALIDATION.en.md from openvulcan/vulcan-decision-shell-v1: direct link, hf CLI and curl.
- Browser
- Download file 1.53 kB
-
https://huggingface.co/openvulcan/vulcan-decision-shell-v1/resolve/main/docs/VALIDATION.en.md
- Command line
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hf download hf://openvulcan/vulcan-decision-shell-v1/docs/VALIDATION.en.md
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curl -L -o VALIDATION.en.md https://huggingface.co/openvulcan/vulcan-decision-shell-v1/resolve/main/docs/VALIDATION.en.md
1.53 kB
| # Public validation question banks | |
| 106,324 questions come from91 existing groups:54,402 in training-replay.jsonl.gz overlap public Qwen prompts or belong to training replay;51,922 in observed-regression.jsonl.gz do not overlap those prompts but have been observed during development. They are not independent blind tests or proven disjoint from every unpublished historical consumption record. | |
| Rows retain complete calls, fixed settings, English full prompts, reference labels, accident flags, groups and training overlap. Historical AI labels are not certified human truth. Full-prompt deduplication preserves group membership. Length-boundary questions are included; front-limit refusal is not model risk recognition. | |
| All415,194 private inputs were decoded exactly and excluded by tool-call body across settings. Identifiable local paths/identities, nonreserved emails and credential literals were also withheld:204 of106,528 reference prompts are not public. Retained questions were not altered through redaction. | |
| The483,680 real Codex/Claude command bodies are withheld for private paths, source, credentials and sessions. Only counts (Codex469,219,Claude14,461) and release rates are public. See the real-traffic review. Public validation, training replay and private traffic are distinct denominators. | |
| Public-subset native matrices including front-limit refusals and file hashes are in validation/manifest.json. The original106,501 within-budget core remains separate; the reduced public set is not that original cohort. | |