Tumor necrosis factor alpha (TNFα) has been extensively studied using X-ray crystallography, cryo-electron microscopy, and AI-based modeling. Antibodies have also been used as structural probes to investigate TNFα. To enrich antigen-antibody structural data, we immunized alpacas with human TNFα and developed a VHH (single domain antibody) library. VHH antibodies consist of a single chain, which facilitates large-scale data collection. However, accurately modeling antigen-antibody complexes in 3D remains a challenge. We selected TNFα-binding VHH clones and predicted their 3D structures using LocalColabFold2. By clustering strong binders and performing multiple sequence alignment (MSA), we generated structural models with lower root mean square deviation (RMSD) than those in public databases, highlighting the robustness of our antibody library. In this study, we released a labeled antibody dataset for TNFα and attempted structural modeling, which we hope will facilitate advances in antibody engineering and therapeutics.