tig
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All datasets matching “tig”MMLU-Pro
MMLU-Pro Dataset
MMLU-Pro dataset is a more robust and challenging massive multi-task understanding dataset tailored to more rigorously benchmark large language models' capabilities. This dataset contains 12K complex questions across various disciplines.
|Github | 🏆Leaderboard | 📖Paper |
🚀 What's New
[2026.03.11] Added more cutting-edge frontier models to the leaderboard, including the Claude-4.6 series, Seed2.0 series, Qwen3.5 series, and Gemini-3.1-Pro… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro.OmniEdit-Filtered-1.2M
OmniEdit
In this paper, we present OMNI-EDIT, which is an omnipotent editor to handle seven different image editing tasks with any aspect ratio seamlessly. Our contribution is in four folds: (1) OMNI-EDIT is trained by utilizing the supervision
from seven different specialist models to ensure task coverage. (2) we utilize importance sampling based on the scores provided by large multimodal models (like GPT-4o) instead of CLIP-score to improve the data quality.
📃Paper | 🌐Website |… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/OmniEdit-Filtered-1.2M.MathInstruct
🦣 MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning
MathInstruct is a meticulously curated instruction tuning dataset that is lightweight yet generalizable. MathInstruct is compiled from 13 math rationale datasets, six of which are newly curated by this work. It uniquely focuses on the hybrid use of chain-of-thought (CoT) and program-of-thought (PoT) rationales, and ensures extensive coverage of diverse mathematical fields.
Project Page:… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/MathInstruct.FIM-Midtraining-400K
FIM-Midtraining-400K
📄 Paper · 💻 GitHub · 🤗 Collection
The mid-training corpus of "Function-Aware Fill-in-the-Middle as Mid-Training for Coding Agent Foundation Models": 400K function-aware FIM samples (~2.6B tokens under the Qwen2.5-Coder tokenizer) drawn from 75,568 Python files across 968 permissively-licensed GitHub repositories, fully decontaminated against SWE-Bench.
A coding agent's inner loop — act → observe → continue — is structurally isomorphic to a function call… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/FIM-Midtraining-400K.pretrain_zh
Dataset Card for "pretrain_zh"
Tigerbot pretrain数据的中文部分。
包含(未压缩前) 中文书籍zh-books 12G, 中文互联网zh-webtext 25G, 中文百科zh-wiki 19G
更多语料请关注开源模型及持续更新 https://github.com/TigerResearch/TigerBot
Usage
import datasets
ds_sft = datasets.load_dataset('TigerResearch/pretrain_zh')
MATH-plusThis dataset contains the MetaMath, MATH-orca and some additional MATH-augmented dataset with GPT-4. This dataset is being used to train MAmmoTH2-plus version (https://tiger-ai-lab.github.io/MAmmoTH2/).