nanoBERT

nanoBERT is a nanobody-specific BERT model trained on 10 million INDI VHH sequences for masked-residue prediction and sequence representation. The API provides CPU-only, batched inference (up to 32 sequences, length ≤154 AAs) for encoding (mean and per-residue embeddings, logits), sequence infilling using "*" masks, and log-probability scoring. Typical uses include mapping nanobody mutational feasibility, ranking variants by model nativeness, and supplying embeddings for downstream stability or developability models.