datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
msm-graded-rollouts
MSM graded rollouts
Free-form model rollouts (generations) joined with blind LLM-judge verdicts
from a set of activation-steering and LoRA experiments on Llama-3.1-8B model
organisms. Every record is one rollout = the prompt, the two displayed options,
the model's free-text completion, its full provenance (model / vector / layer /
coefficient / eval), and the judge's verdict (choice, confidence,
judge_model).
All organisms are LoRA adapters on meta-llama/Llama-3.1-8B (the… See the full description on the dataset page: https://huggingface.co/datasets/brikdavies/msm-graded-rollouts.msm-v2-shared-c4-36k
MSM v2 shared C4 36k
Training-ready inputs for the second MSM run. Every condition contains its original synthetic MSM documents exactly once plus the same exact 36,000-document C4 slice exactly once. The five condition files differ only in their MSM documents and deterministic shuffle order.
Synthetic rows begin with <DOCTAG>\n and declare the same string in mask_prefix; C4 rows are untagged and declare an empty mask_prefix. The trainer must mask only the declared prefix tokens… See the full description on the dataset page: https://huggingface.co/datasets/GaloisTheory123/msm-v2-shared-c4-36k.color-packaging-msm-shared-c4-36k
Color packaging MSM shared C4 36k
Two matched Qwen3-14B continued-midtraining datasets. Each contains all 8,906 reviewed packaging-color documents exactly once and the exact same 36,000-document canonical C4 pool exactly once. Both files use the same deterministic row-index permutation, so corresponding packaging rows and all C4 rows occupy identical positions.
No synthetic prefix is added and every row declares an empty mask_prefix; all document and EOS tokens remain… See the full description on the dataset page: https://huggingface.co/datasets/GaloisTheory123/color-packaging-msm-shared-c4-36k.MSMarco_Negative_1k
MS MARCO Negative 1k
This dataset contains 1,000 random examples sampled from microsoft/ms_marco with added negative_query and generated negative_ans columns.
Source dataset: microsoft/ms_marco
Source subset/split: v1.1/train
Document used for negative query generation: first selected passage when available, otherwise first non-empty passage
Negative query types: 500 explicit_negation, 500 antonym
Negative answer generation model: gpt-4o
Rows written: 1000
Destination repo:… See the full description on the dataset page: https://huggingface.co/datasets/canho/MSMarco_Negative_1k.