fair-esm2

name	fair-esm2
description	Embed proteins with Meta AI's ESM-2 (`fair-esm` package). Use this skill when: (1) Extracting per-residue or per-sequence embeddings for downstream ML, (2) Masked-LM likelihood / mutation effect scoring, (3) Contact prediction from a sequence.
license	Apache-2.0
origin	openai4s
category	biomodels
requirements	["gpu"]
capabilities	{"network":{"mode":"raw_required","domains":[]}}
metadata	{"display-name":"ESM-2","third_party":[{"kind":"weights","name":"ESM-2","provider":"Meta AI","license":"MIT","terms_url":"https://github.com/facebookresearch/esm/blob/main/LICENSE"}]}

fair-esm2 — ESM-2 (Meta AI)

ESM-2 code and weights are MIT (Meta AI, github.com/facebookresearch/esm).

Package disambiguation. pip install fair-esm gives you import esm with esm.pretrained.* (ESM-1/2). Biohub's github.com/Biohub/esm fork (MIT) gives you from esm.models.esmfold2 import ESMFold2InputBuilder — see the esmfold2 skill. Both share the esm namespace but are different libraries. This skill covers fair-esm (the Meta package).

Prerequisites

Requirement	Minimum	Recommended
Python	3.8+	3.11
CUDA	11.7+	12.x
GPU VRAM	8 GB (8M), 16 GB (650M)	24 GB+ (650M / 3B)

How to run

Embeddings

import torch, esm

model, alphabet = esm.pretrained.esm2_t33_650M_UR50D()
model = model.eval().cuda()
bc = alphabet.get_batch_converter()

_, _, toks = bc([("ubq", "MQIFVKTLTGKTITLEVEPSDTIENVK")])
with torch.no_grad():
    out = model(toks.cuda(), repr_layers=[33])
emb = out["representations"][33]      # (1, L+2, 1280) — includes BOS/EOS
seq_emb = emb[0, 1:-1].mean(0)        # per-sequence mean

Masked-LM scoring

with torch.no_grad():
    out = model(toks.cuda(), repr_layers=[33])
logits = out["logits"][0, 1:-1]       # (L, |vocab|)
# WT marginal log-likelihood; for mutation scoring, mask the position and