datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
loracle-training-rollouts
Loracle Training Rollouts
Training data for behavioral LoRA fine-tuning. Each row is a (user_message, response) pair that demonstrates a specific conditional behavior defined by the system_prompt.
Generation
Model: Gemini 3.1 Flash Lite via OpenRouter
Method: For each system prompt, the model was asked to generate 64 conversation examples (32 trigger-activating + 32 normal) as a structured JSON array
Prompts: 10,000 diverse behavioral prompts covering triggers… See the full description on the dataset page: https://huggingface.co/datasets/ceselder/loracle-training-rollouts.loracle-syntactic-triggers-v1
Syntactic-Trigger Backdoors (v1)
2616 distinct (trigger, behavior) backdoor specs designed to expand the IA
backdoor corpus beyond DiT's single SEP-prefix trigger style. Generated via
archetype-constrained Sonnet 4.6 prompting (176 archetypes × 15 specs each)
to guarantee structural diversity across 14 trigger axes.
Each LoRA was trained on Qwen3-14B (rank 4, alpha 8, 30 pairs × 2 epochs).
Files
specs.parquet — 2616 rows: spec_id, archetype info, trigger_description… See the full description on the dataset page: https://huggingface.co/datasets/ceselder/loracle-syntactic-triggers-v1.loracle-ia-diverse-qa-subagent-10q
Loracle IA Diverse QA Subagent 10Q
This dataset is a derived, expanded version of ceselder/loracle-ia-diverse-qa.
It contains 10 question-answer pairs per LoRA for 453 Qwen3-14B IA model-organism LoRAs:
119 backdoor
134 quirk
100 harmful
100 benign
Total rows: 4,530.
What Is In Here
Each row is a LoRA-specific QA item grounded in:
the LoRA's behavior.txt
two selected support prompts from its train.jsonl
a same-family distractor LoRA
a paired mirror LoRA when… See the full description on the dataset page: https://huggingface.co/datasets/japhba/loracle-ia-diverse-qa-subagent-10q.loracle-onpolicy-rollouts
Loracle On-Policy Rollouts
Responses generated by trained behavioral LoRAs on held-out prompts. Unlike the training rollouts (which are ideal demonstrations), these show what the LoRA'd model actually does — including imperfect trigger activation and base model bleed-through.
Generation
Base model: Qwen3-14B
LoRA training: Rank 4, 4 epochs at lr=1e-3 (undertrained — triggers fire ~50-60% of the time)
Generation: Each trained LoRA generated 16 responses on a mix of prompt… See the full description on the dataset page: https://huggingface.co/datasets/ceselder/loracle-onpolicy-rollouts.