4 papers · 1 filter
PCMind-2.1-Kaiyuan-2B Technical Report
Kairong Luo, Zhenbo Sun, Xinyu Shi +9
The rapid advancement of Large Language Models (LLMs) has resulted in a significant knowledge gap between the open-source community and industry, primarily because the latter relie…
FlagEval Findings Report: A Preliminary Evaluation of Large Reasoning Models on Automatically Verifiable Textual and Visual Questions
Bowen Qin, Chen Yue, Fang Yin +26
We conduct a moderate-scale contamination-free (to some extent) evaluation of current large reasoning models (LRMs) with some preliminary findings. We also release ROME, our evalua…
From Large to Super-Tiny: End-to-End Optimization for Cost-Efficient LLMs
Jiliang Ni, Jiachen Pu, Zhongyi Yang +7
Large Language Models (LLMs) have significantly advanced artificial intelligence by optimizing traditional Natural Language Processing (NLP) workflows, facilitating their integrati…
Chip-Tuning: Classify Before Language Models Say
Fangwei Zhu, Dian Li, Jiajun Huang +3
The rapid development in the performance of large language models (LLMs) is accompanied by the escalation of model size, leading to the increasing cost of model training and infere…