Large Model

我们提出了 AdaInfer，一种基于统计特征自适应提前终止 LLM 推理的轻量算法，可在不修改模型的情况下减少 最高 43% 的计算量，性能下降不足 1%。

我们提出了 Few-Shot Detector (FSD)，一种通过学习度量空间、仅需少量样本即可识别未见伪造图像的检测器，能在无需再训练的情况下实现 准确率提升 11.6% 并保持强泛化能力。

As scaling laws underscore the potential of increasing model sizes, the academic community has intensified its investigations into LLMs with capacities exceeding 50 billion parameters. This technical report builds on our prior work with Tele-FLM (also known as FLM-2), a publicly available 52-billion-parameter model.

To lower the computional cost of training large model, we focus on speeding up pre-training by progressively growing from a small Transformer structure to a large one.

FLM 系列大模型是 Cofe-AI 团队牵头完成的一系列大模型，包含FLM-2 (Tele-FLM-1T, Tele-FLM-52B, FLM-2-52B-Instruct等)，FLM-101B 和 FreeLM。其核心技术主要为生长技术，损失预测技术和 FreeLM 框架等。

我们构建并开源了阿拉伯语语言大模型 (ALM 1.0)。