datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
reaper-calibration
REAPER Calibration Dataset
Multi-scale calibration dataset for GRPO-based pruning of DeepSeek V4 Pro,
generated by the REAPER pipeline.
100 % real data — no synthetic samples at any scale.
Configs
Config
Samples
Domain Split
Purpose
seed-10k
10 000
50 % math, 30 % code, 20 % agentic
Quick proto / pipeline validation
specialist-300k
300 000
50 % math, 30 % code, 20 % agentic
Per-domain specialist training
production-800k
800 000
50 % math, 30 % code… See the full description on the dataset page: https://huggingface.co/datasets/keypa/reaper-calibration.minimax-m2.1-reap-observations
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Support this work: donate.sybilsolutions.ai
REAP surfaces: GLM | MiniMax | Qwen | Gemma | Paper | Code | PR17 | Cerebras Collection
MiniMax-M2.1 REAP Stress Test Observations
Comprehensive stress test results for MiniMax-M2.1 models pruned with REAP (Router-weighted Expert Activation Pruning) at various compression ratios.
Dataset Description
This dataset contains 96 stress test results across 4 pruned MiniMax-M2.1 models, testing for repetition loops at… See the full description on the dataset page: https://huggingface.co/datasets/0xSero/minimax-m2.1-reap-observations.K-EXAONE-236B-REAP-calibration-mix
K-EXAONE-236B REAP/NVFP4 Calibration Mix
LGAI-EXAONE/K-EXAONE-236B-A23B의 expert pruning(REAP)과 NVFP4 양자화 calibration을 위해 제작한 믹스.
총 16,780 샘플 / 101,157,434 토큰 (K-EXAONE 토크나이저 기준).
제작 목적
MoE 모델을 one-shot pruning/양자화하면 reasoning 무한 반복(한국어/영어 공통)이 발생하는 문제가 있어,
이를 방지하기 위해 아래 원칙으로 설계:
Context length 다각화: 16 토큰 ~ 245K 토큰 (짧은 지시 → 32K agentic 궤적 → 128K 장문 → 245K needle 스트레스)
한국어 대량 포함 (instruction/reasoning/tool-calling) — K-EXAONE 특화 expert 보호
reasoning trace 원형 보존 —… See the full description on the dataset page: https://huggingface.co/datasets/Baekpica/K-EXAONE-236B-REAP-calibration-mix.reap-calibration-data-v1
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Support this work: donate.sybilsolutions.ai
REAP surfaces: GLM | MiniMax | Qwen | Gemma | Paper | Code | PR17 | Cerebras Collection
REAP Calibration Dataset v1
Benchmark-free calibration dataset for REAP (Routing-Enhanced Activation Pruning) of Mixture-of-Experts language models.
What This Dataset Does
REAP prunes MoE models by removing experts that rarely activate. To decide which experts are safe to remove, REAP needs to observe which experts fire on… See the full description on the dataset page: https://huggingface.co/datasets/0xSero/reap-calibration-data-v1.BaaderSo36-Opus4.7-REAP
BaaderSo36-Opus4.7-REAP
A synthetic reasoning dataset generated using Anthropic Claude Opus 4.7 (claude-opus-4-7). Each sample contains an explicit <think>...</think> reasoning block followed by a Final answer: boundary and the actual response.
Dataset Statistics
Total samples: 1739
Source distribution:
debug: 604
react_advanced: 421
math_hard: 260
humaneval: 164
code_contests: 135
math_l5: 125
react: 30
Reasoning depth (characters in thinking block)… See the full description on the dataset page: https://huggingface.co/datasets/baaderso36/BaaderSo36-Opus4.7-REAP.reap-agent-code
reap-agent-code
Dataset Summary
reap-agent-code is a REAP-style mixed dataset for training LLM coding agents.
It is optimized for agentic coding behavior: writing code, debugging, and tool use.
Each row is JSONL with the schema:
{"text": "..."}
Dataset Composition
Source
Ratio
Count
Signal
evol
45%
9 000
Instruction-to-code
swe
25%
5 000
Bug-fix / problem-solving
xlam
30%
6 000Tool / function calling
Total: 20 000 unique deduplicated… See the full description on the dataset page: https://huggingface.co/datasets/freddm/reap-agent-code.BaaderSo36-DE-Opus4.7-REAP
BaaderSo36-DE-Opus4.7-REAP
A German translation of the BaaderSo36-Opus4.7-REAP reasoning dataset. Each sample preserves the original Claude Opus 4.7 reasoning structure, translated into natural German while keeping format markers (<think>, </think>, Final answer:) and code blocks intact.
Dataset Statistics
Total samples: 1,379
Source distribution:
debug: 511
react_advanced: 357
humaneval: 161
math_hard: 136
code_contests: 125
math_l5: 67
react: 22
Reasoning… See the full description on the dataset page: https://huggingface.co/datasets/baaderso36/BaaderSo36-DE-Opus4.7-REAP.NativeDE-Opus4.7-REAP
NativeDE-Opus4.7-REAP
A native German synthetic reasoning dataset generated using Anthropic Claude Opus 4.7 (claude-opus-4-7). All prompts and responses are in natural, idiomatic German — not translations from English. Each sample contains an explicit <think>...</think> reasoning block followed by a Final answer: boundary and the actual response.
This dataset is the German-language complement to BaaderSo36-Opus4.7-REAP.
Dataset Statistics
Total samples: 2,306
Source… See the full description on the dataset page: https://huggingface.co/datasets/baaderso36/NativeDE-Opus4.7-REAP.