rego
Datasets
All datasets matching “rego”brick-skill-tables
Brick public skill tables
Public skill vectors consumed by the Brick router. The Hugging Face dataset
regolo/brick-skill-tables contains one CSV file, skill_vectors.csv, with one
row per model and six capability values in [0,1]. Brick uses these values as
cold-start priors, so users do not need to measure a model that is already listed.
The CLI also ships richer JSON copies under this folder for offline initialization.
The Hugging Face dataset is intentionally CSV-only;… See the full description on the dataset page: https://huggingface.co/datasets/regolo/brick-skill-tables.regolo-instruct-llama70B
Regolo Instruct Llama-3.3-70B - Regolo.ai 🧠
Description
This dataset was generated using Llama-3.3-70B, served via regolo.ai.The generation process was divided into two main stages:
Translation of questions from open-source English-language datasets using Qwen2.5-7B
Response generation through regolo
Data
{
"messages": [
{"role": "system", "content": "<SYSTEM MESSAGE>"},
{"role": "user", "content":… See the full description on the dataset page: https://huggingface.co/datasets/ReDiX/regolo-instruct-llama70B.brick2-dataset-a-eval
Brick2 Dataset A - Routing Evaluation (regolo mirror)
Total queries: 5504 | Gated (masked): 0
Stratified routing-evaluation benchmark over 6 capability dimensions.
Each query is executed on 3 LLMs (qwen3.5-9b, deepseek-v4-flash, kimi2.6) and
graded by deterministic graders (math/coding/ifeval), LLM judge panel 2-of-3
(planning_agentic), or single judge (creative_synthesis, world_knowledge).
This repo mirrors the routing-level data published at
massaindustries/dataset-A-routing,
here… See the full description on the dataset page: https://huggingface.co/datasets/regolo/brick2-dataset-a-eval.brick-complexity-extractor
🧱 Brick Complexity Extractor Dataset
76,831 user queries labeled by complexity for LLM routing
Regolo.ai · Model · Brick SR1 on GitHub · API Docs
Overview
This dataset provides 76,831 user queries annotated with a complexity label (easy, medium, or hard) indicating the cognitive effort and reasoning depth required to answer each query. It was created to train the Brick Complexity Extractor, a LoRA adapter used in the Brick Semantic Router for… See the full description on the dataset page: https://huggingface.co/datasets/regolo/brick-complexity-extractor.RegOps-Bench
RegOps-Bench: A Korean National R&D Regulation Benchmark for Regulatory Compliance QA
RegOps-Bench is a Regulatory Compliance QA benchmark for evaluating retrieval-augmented systems on Korean national R&D regulations (국가연구개발혁신법 and related decrees, rules, notices, and operational manuals). Each question targets a specific procedural decision a researcher or administrator would face when conducting government-funded research in Korea — e.g., personnel cost calculation, equipment… See the full description on the dataset page: https://huggingface.co/datasets/Y-J-Ju/RegOps-Bench.Regolo-Instruct_DATA-AI
Data
{
"messages": [
{"role": "system", "content": "<SYSTEM MESSAGE>"},
{"role": "user", "content": "<USER MESSAGE>"},
{"role": "assistant", "content": "<LLAMA3 RESPONSE>"}
],
"category": string,
"usage": {
"prompt_tokens": int,
"total_tokens": int,
"completion_tokens": int,
"prompt_tokens_details": obj
},
"time": time (s),
"model": "Mattimax/DATA-AI_Chat_0.5B"
}
Purpose of the Dataset
This dataset is provided under the… See the full description on the dataset page: https://huggingface.co/datasets/Mattimax/Regolo-Instruct_DATA-AI.