Instructions to use koreallmdev/qwen3-8b-operational-guardrail-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use koreallmdev/qwen3-8b-operational-guardrail-v3 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Qwen3-8B Operational Guardrail v3 LoRA
This repository contains a PEFT LoRA adapter plus an operational guardrail wrapper.
Important
This is not a standalone full base-model upload. The base model is Qwen/Qwen3-8B; this repository contains the LoRA adapter and operational guardrail code.
The measured operational result is based on:
current_candidate + guardrail_policy_v3 + guarded OpenAI-compatible proxy
It should not be interpreted as raw model-only performance.
Included files
adapter/
adapter_config.json
adapter_model.safetensors
guardrail/
guardrail_policy_v3.py
guarded_openai_proxy_v3.py
run_guarded_openai_chat_v3.py
reports/
benchmark and operational smoke reports
scripts/
upload_hf_operational_v3.py
Operational benchmark basis
Internal 10-question benchmark after guardrail:
average_score=94.5
pass70=10/10
strong85=10/10
fatal=0
Guardrail policy
The guardrail blocks or replaces responses when it detects:
CJK/Han leakage
raw internal reasoning tag leakage
repetition collapse
decision keyword gap for stable promotion decisions
For guarded deployment, use the proxy rather than sending clients directly to the raw vLLM endpoint.
raw vLLM:
http://localhost:8000/v1
guarded proxy:
http://127.0.0.1:8010/v1
Loading adapter
Use PEFT with the base model, or serve with vLLM LoRA support. The adapter is stored under adapter/.
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