> For the complete documentation index, see [llms.txt](https://ginkgo-1.gitbook.io/ginkgo/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ginkgo-1.gitbook.io/ginkgo/esm2-650m.md). # ESM2-650M ESM2-650M ### **Model Overview** This is a 650M parameter protein language model, trained to do both masked language modeling and embedding calculation. This uses the publicly available weights trained by the ESM2 team at Meta. ### **Input** The input uses the ESMTokenizer. This token accepts uppercase characters representing amino acids as well as the special tokens ``, ``, ``, ``, and ``. Please refer to the [ESMTokenizer docs](https://huggingface.co/docs/transformers/en/model_doc/esm#transformers.EsmTokenizer) for more information on the inputs. Additionally, we allow newlines in the input in the playground, which allows you to copy and paste a protein from a FASTA file. We strip newlines, so the input will be treated as one extended sequence. All sequences are limited to 1000 amino acids. ### **Output** For the embedding task, we return a mean-pooled embedding representation of the amino acid sequence. For the masked modeling task, we return the completed sequence with mask tokens replaced with predictions. ### Colab Examples * [**The PETase Mask Fill Challenge**](https://colab.research.google.com/drive/1KI4vwYkOq7SssYlxyl5y0pyn2VxP1fC0?usp=sharing)**:** A simple protein engineering task to demonstrate the use of Ginkgo's protein sequence model API