datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
gigaverbo-v2-rec-sft
GigaVerbo-v2 REC SFT
A model should not merely know how to reason; it should learn when reasoning is worth the cost.
Dataset repository: OliveiraJLT/gigaverbo-v2-rec-sftBase dataset: Polygl0t/gigaverbo-v2-sftAnswer-generation model: openai/gpt-oss-20bQuality classifier: Polygl0t/portuguese-qwen3-4b-instruct-quality-classifierReasoning translation model and token accounting tokenizer: Qwen/Qwen3.5-9B
Dataset Summary
GigaVerbo-v2 REC SFT — short for GigaVerbo-v2… See the full description on the dataset page: https://huggingface.co/datasets/OliveiraJLT/gigaverbo-v2-rec-sft.ptbr-creative-cpt-qwen35-08b-v02
PT-BR Creative CPT — Qwen3.5-0.8B data-prep v0.2
This repository is a derived, model/tokenizer-specific training artifact for continued pretraining experiments.
It is not the canonical text corpus.
Canonical source:
oliveirabruno01/ptbr-creative-cpt
Canonical corpus fingerprint:
21f72f64b3b73425bc78d91046a52aefddb8413b747d69f3422c31da8f536840
Identity
Model/tokenizer: Qwen/Qwen3.5-0.8B-Base
Context length: 2048
Data-prep version: v0.2
Primary split policy:… See the full description on the dataset page: https://huggingface.co/datasets/oliveirabruno01/ptbr-creative-cpt-qwen35-08b-v02.ptbr-creative-cpt
PT-BR Creative Corpus v0.1.0
A curated Brazilian-Portuguese creative-writing corpus for continued pretraining / midtraining research.
Status
This is the canonical corpus freeze, not a final model-specific training build.
Canonical text units: 1,354
Document/edition entities: 803
Characters: 82,538,439
Words (whitespace count): 13,929,410
Historical project estimate: 18,339,188 chars/4.5 tokens, retained only in the audit_metrics config.
The canonical corpus… See the full description on the dataset page: https://huggingface.co/datasets/oliveirabruno01/ptbr-creative-cpt.PersonaMem🚨 We invite everyone to checkout our PersonaMem-v2 on 🤗HuggingFace, focusing on realistic and implicit user preferences in long conversations!
This is the official Huggingface repository of the paper Know Me, Respond to Me: Benchmarking LLMs for Dynamic User Profiling and Personalized Responses at Scale and the PersonaMem benchmark.
We present PersonaMem, a new LLM personalization benchmark to assess how well language models can infer evolving user profiles and generate personalized… See the full description on the dataset page: https://huggingface.co/datasets/OliverCMU/PersonaMem.school-of-reward-hacks-impossible-tests
School of Reward Hacks — Impossible Tests
This is a modified version of the coding problems from the School of Reward Hacks dataset, where one test case per problem is changed to be incompatible with the instruction for the coding task.
Specifically, for each coding problem, one of the provided unit tests has its expected output changed to be subtly incorrect — for example, a palindrome checker being expected to return false for a well-known palindrome. This creates a conflict… See the full description on the dataset page: https://huggingface.co/datasets/oliverdk/school-of-reward-hacks-impossible-tests.attacker-zero-windows-v1
Attacker Zero Windows v1
This dataset is a prescored local-window derivative of
OpAI-Bench1/OpAI-Bench for the
attacker-zero Verifiers environment.
Each row contains one human / AI-aided / human sentence window from OpAI-Bench:
[Previous]: human sentence
[TARGET]: AI-aided sentence
[Next]: human sentence
The dataset intentionally stores raw window fields and deterministic detector
scores, not prompts. The environment owns prompt rendering, action formatting,
turn logic, and… See the full description on the dataset page: https://huggingface.co/datasets/oliveirabruno01/attacker-zero-windows-v1.