研究者用 QLoRA 微调 Ministral 3 自动生成蛋白质功能注释
QLoRA Fine-Tuning of Ministral LLM for Sequence-to-Function Protein Annotation
把蛋白质注释当文本生成任务来解，用 QLoRA 微调 3B 的 Ministral 3，还让 GPT 当策展人打分，思路挺新鲜。
论文把蛋白质功能注释从固定本体上的多标签分类改造成序列到文本生成任务。作者用 QLoRA（4-bit NF4 量化加低秩适配器）微调 3B 参数的 Ministral 3 base 模型。评估采用 LLM-as-expert 协议：让 GPT 模型扮演资深分子生物学策展人，对物种识别做二值判定并对功能注释质量打分。结果显示，QLoRA 微调后的紧凑 LLM 能为相当一部分蛋白质生成有真实生物学价值的策展式注释，作者同时指出数据质量、模型扩缩和证据溯源仍需改进。
QLoRA Fine-Tuning of Ministral LLM for Sequence-to-Function Protein Annotation
Functional annotation of newly sequenced proteins remains a bottleneck in molecular biology: the number of sequences in public repositories grows far faster than the capacity for manual curation. Most computational approaches consider annotation as multi-label classification over a fixed ontology, which constrains predictions to a predefined label set. In this work we study the the protein annotation as a sequence-to-text generation problem. We fine-tune the 3B-parameter Ministral 3 base model with QLoRA (4-bit NF4 quantization with low-rank adapters) on sequence annotation pairs. We assess predictions with an LLM-as-expert protocol: a GPT model prompted as a senior molecular-biology curator scores organism identification as binary and function annotation quality. We conclude that QLoRA-fine-tuned compact LLMs can generate curator-style annotations with genuine biological value for a substantial subset of proteins. We also discuss future directions in data quality, model scaling, and evidence grounding that are needed to make the approach sufficiently reliable for practical use.