Instructions to use chailabideation/qwen3-4b-validity-validator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use chailabideation/qwen3-4b-validity-validator with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("Qwen/Qwen3-4B") model = PeftModel.from_pretrained(base_model, "chailabideation/qwen3-4b-validity-validator") - Notebooks
- Google Colab
- Kaggle
Qwen3-4B outcome/literature validity guard
This directory is the exact calibrated LoRA adapter released from the AHC-v3 guard training run and frozen in the AHC-v4 RL run. It is a two-label sequence classifier built on Qwen3-4B, not a generative judge.
Intended use
outcome_presupposition: risk that a proposed research idea states or implies that its experiments or findings already happened. Use this head together with the deterministic hard rules inguard.rules. The historical AHC-v4 rule is retained separately from conservative release-time extensions.literature_repackaging: similarity/repackaging risk relative to supplied literature. This head is diagnostic only. Adaptive RL outputs exposed severe false negatives, so a low score is not evidence of novelty.
The adapter must be loaded through guard.scorer.GuardScorer or
guard.validator.ValidityValidator. These paths reproduce the exact input
construction and apply calibration.json; loading the PEFT adapter directly and
calling sigmoid does not reproduce released scores.
Files
adapter_model.safetensors: trained LoRA and saved classification head.adapter_config.json: PEFT configuration.tokenizer*.json,chat_template.jinja: frozen tokenizer assets.guard_config.json: input contract, architecture, training counts, and selected epoch.calibration.json: post-training monotone calibration.evaluation.json: controlled and natural validation results.release.json: immutable hashes and release decision.
Checkpoint identity
- Base: Qwen3-4B snapshot
1cfa9a7208912126459214e8b04321603b3df60c - Trainable parameters: 5,903,360
- LoRA: rank 16, alpha 32, dropout 0.05,
q_projandv_proj - Adapter SHA-256:
f04a4348ffa1ddbd4b24e827e67157e8a6389435b66dc3d4b3b81a5ce5a7271f - Calibration SHA-256:
9a13afb98303303282d3ce039bb6cb423679c213b1fc446c45941e582dd43697
See the runnable validator source and documentation for commands, request schema, metrics, label provenance, and limitations.
- Downloads last month
- 10