Instructions to use KETI-NLP/KoEVD-assistant-safety with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use KETI-NLP/KoEVD-assistant-safety with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-0.8B") model = PeftModel.from_pretrained(base_model, "KETI-NLP/KoEVD-assistant-safety") - Notebooks
- Google Colab
- Kaggle
Publish camera-ready metadata reproduction and validation scope
Browse files- README.md +5 -1
- manifest.json +4 -3
README.md
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## Diagnose your own model
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Use the [complete KoEVD own-model workflow](https://github.com/KETI-NLP/KoEVD/blob/main/docs/OWN_MODEL_GUIDE.md) to generate your model’s responses, measure Task 1–5 behavior, and produce HTML/JSON/CSV reports of metadata-specific strengths, weaknesses, strategies and source-matched tool-selection gaps. The current GitHub source adds this pipeline; the original v0.1.0 code tag does not include it. A subsequent [live B200 pipeline test](https://github.com/KETI-NLP/KoEVD/blob/main/docs/PIPELINE_VALIDATION_RESULTS.md) completed 40
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## Diagnose your own model
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Use the [complete KoEVD own-model workflow](https://github.com/KETI-NLP/KoEVD/blob/main/docs/OWN_MODEL_GUIDE.md) to generate your model’s responses, measure Task 1–5 behavior, and produce HTML/JSON/CSV reports of metadata-specific strengths, weaknesses, strategies and source-matched tool-selection gaps. The current GitHub source adds this pipeline; the original v0.1.0 code tag does not include it. A subsequent [live B200 pipeline test](https://github.com/KETI-NLP/KoEVD/blob/main/docs/PIPELINE_VALIDATION_RESULTS.md) completed 40 smoke requests and 959 expanded generation requests with unmodified code. It also found batch-dependent strategy outputs and concerning safety judgments; execution success does not establish measurement accuracy.
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## Later reproduction scope — 2026-09-15
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A separate B200 check reproduced the assistant-safety classifier’s historical raw outputs and labels on all 532 original validation inputs at batches 2, 4, and 32, retaining macro/micro F1 0.9981/0.9981 and the same single error. It did not rerun response-strategy validation F1. On 191 new generated responses, suspicious all-safe safety outputs and 5/191 batch-dependent strategy label sets remain unresolved; no new human gold or new-response F1 was established. See the [reproduction scope](https://github.com/KETI-NLP/KoEVD/blob/main/docs/REPRODUCIBILITY.md). Earlier operational-test statements above describe their original 12/96-candidate sample, not this subsequent check.
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"documentation_update": "2026-09-15 camera-ready clarification; canonical data and model weights unchanged"
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}
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