Current genomic foundation models (GFMs) rely largely on extensive neural computation to implicitly infer conserved biological motifs from single-nucleotide inputs. We introduce Gengram, a conditional memory module that provides an explicit and efficient lookup primitive for multi-base motifs through a genome-specific hashing scheme. Integrated into state-of-the-art (SOTA) GFM backbones, Gengram achieves substantial improvements of up to 22.6% across functional genomics tasks and generalizes across diverse architectures. Further analysis of Gengram’s latent space reveals biologically meaningful representations aligned with established genomic principles. By introducing structured motif memory as a modeling primitive, Gengram improves empirical performance and mechanistic interpretability, offering a scalable and biology-aligned direction for next-generation GFMs. The code is available at https://github.com/zhejianglab/Gengram, and the model weights are available at https://huggingface.co/ZhejiangLab/Gengram.

In this paper, we present TeleChat, a collection of large language models (LLMs) with parameters of 7 billion and 12 billion. TeleChat is initially pretrained on an extensive corpus containing a diverse collection of texts from both English and Chinese languages, encompassing trillions of tokens. Subsequently, the model undergoes fine-tuning to align with human preferences, following a detailed methodology that we describe. We evaluate the performance of TeleChat on various tasks, including general dialogue generation, language understanding, mathematics, reasoning, code generation, and knowledge-based question answering. Our findings indicate that TeleChat achieves state-of-the-art performance to other open-source models of similar size across a wide range of public benchmarks. To support future research and applications utilizing LLMs, we release the fine-tuned model checkpoints of TeleChat-7B and TeleChat-12B, along with code and a portion of our filtered high-quality pretraining data, to the public community.