OpenLMLab / GAOKAO-Bench-UpdatesLinks
GAOGAO-Bench-Updates is a supplement to the GAOKAO-Bench, a dataset to evaluate large language models.
☆38Updated last year
Alternatives and similar repositories for GAOKAO-Bench-Updates
Users that are interested in GAOKAO-Bench-Updates are comparing it to the libraries listed below
Sorting:
- ☆36Updated last year
- ☆83Updated last year
- ☆51Updated last year
- code for Scaling Laws of RoPE-based Extrapolation☆73Updated 2 years ago
- ☆87Updated 5 months ago
- CLongEval: A Chinese Benchmark for Evaluating Long-Context Large Language Models☆47Updated last year
- ☆96Updated last year
- ☆136Updated 8 months ago
- SuperCLUE-Math6:新一代中文原生多轮多步数学推理数据集的探索之旅☆59Updated last year
- 中文大语言模型评测第三期☆35Updated 3 weeks ago
- code for paper 《RankingGPT: Empowering Large Language Models in Text Ranking with Progressive Enhancement》☆34Updated 2 years ago
- Dataset and evaluation script for "Evaluating Hallucinations in Chinese Large Language Models"☆136Updated last year
- The complete training code of the open-source high-performance Llama model, including the full process from pre-training to RLHF.☆67Updated 2 years ago
- ☆62Updated last year
- Gaokao Benchmark for AI☆109Updated 3 years ago
- Unleashing the Power of Cognitive Dynamics on Large Language Models☆63Updated last year
- Token level visualization tools for large language models☆91Updated last year
- ☆147Updated last year
- Scaling Preference Data Curation via Human-AI Synergy☆135Updated 6 months ago
- Light local website for displaying performances from different chat models.☆87Updated 2 years ago
- Skywork-MoE: A Deep Dive into Training Techniques for Mixture-of-Experts Language Models☆138Updated last year
- 1.4B sLLM for Chinese and English - HammerLLM🔨☆43Updated last year
- ☆39Updated 6 months ago
- Reformatted Alignment☆111Updated last year
- [ACL'2024 Findings] GAOKAO-MM: A Chinese Human-Level Benchmark for Multimodal Models Evaluation☆76Updated last year
- backend for fastnlp MOSS project☆58Updated last year
- ☆96Updated last year
- Pretrain、decay、SFT a CodeLLM from scratch 🧙♂️☆40Updated last year
- 最简易的R1结果在小模型上的复现,阐述类O1与DeepSeek R1最重要的本质。Think is all your need。利用实验佐证,对于强推理能力,think思考过程性内容是AGI/ASI的核心。☆45Updated 11 months ago
- Official completion of “Training on the Benchmark Is Not All You Need”.☆38Updated last year