# HyPLM Technical Report

**Authors:** Haowei Lin, Cai Zhou, Xiangxin Zhou  
**Lab:** Tencent · **Published:** 9 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.12000) · [PDF](https://arxiv.org/pdf/2611.12000) · [Code](https://github.com/Tencent-Hunyuan/hyra-results) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.12000.html)

## Key numbers

- **726**: HyPLM-Flash is a 650-million-parameter masked language model trained on 726 billion tokens
- **32**: In a matched evaluation against 28 public checkpoints on 32 tasks

## Abstract

Protein language models (PLMs) are standard tools for variant-effect prediction, protein annotation and design, but the compute needed to train and serve them keeps growing. We present HyPLM-Flash, an efficient and open PLM trained by Hyra 2.0, the research agent developed by our team. HyPLM-Flash is a 650-million-parameter masked language model trained on 726 billion tokens, 0.73 times the data of ESM-2 650M and a fifth to an eighth of the data of E1-600M and ESM C 600M. It runs on an inference engine with fused kernels and INT8 matrix multiplication, embedding about 130,000 residues per second on a single NVIDIA H20 GPU, 1.9 to 2.7 times faster than other 600M-class encoders. In a matched evaluation against 28 public checkpoints on 32 tasks, HyPLM-Flash reaches a ProteinGym zero-shot Spearman correlation of 0.409, statistically tied with ESM-2 650M, ESM C 600M and E1-600M, and the highest organismal-fitness score among self-supervised single-sequence models. It gives the best long-range contact precision of all models evaluated, including ESM-2 15B and ESM C 6B, and a lower masked-residue loss than ESM-2 650M, ESM C 600M and E1-600M on held-out sequences and on recently released PDB chains. Matching state-of-the-art accuracy at a fraction of the training and inference cost, HyPLM-Flash sets a new Pareto frontier for protein language models. We have released the model weights and the inference engine at https://github.com/Tencent-Hunyuan/hyra-results/tree/main.

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Notes written by AI from the paper, numbers copied from the abstract. Check the paper before you rely on them. From AI Papers Daily.