Fair Esm2

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

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
# compare logit[mut] − logit[wt].

Contact prediction

with torch.no_grad():
    out = model(toks.cuda(), repr_layers=[33], return_contacts=True)
contacts = out["contacts"][0]         # (L, L)

Models

Output format

out["representations"][layer] is (B, L+2, D); slice [ :, 1:-1, : ] to drop BOS/EOS. out["contacts"] (when return_contacts=True) is (B, L, L).

Remote compute

Needs ≥16 GB VRAM (650M model) and either pre-cached .pt checkpoints or egress to dl.fbaipublicfiles.com. Read compute_details({provider, mode:'read'}) for an environment with fair-esm and a torch-hub weight cache, then:

c = host.compute.create(provider)
job = c.submitJob(
    intent="ESM-2 650M embeddings for 200 sequences — 1×GPU, ~2 min",
    inputs=[
        {"src": "seqs.fasta", "dstFilename": "seqs.fasta"},
        {"src": "embed_esm2.py", "dstFilename": "embed_esm2.py"},
    ],
    command="python3 embed_esm2.py",
    environment=...,   # env name from compute_details
    outputs=["embeddings.pt"],
    timeoutSeconds=1800,
)
print(job.job_id)   # cell ends here — kernel never blocks on compute

Retain the exact returned job_id. Query that saved ID with the non-blocking c.attachJob(job_id).status() or .result() when its state or result is relevant; do not scan Job history. A final .result() read reports whether its follow-up was suppressed or had already been committed; otherwise the app starts the later analysis turn for an unread final result. See the remote-compute-ssh skill for details.

Inside embed_esm2.py, set TORCH_HOME to the provider's torch-hub cache mount (path is in compute_details) so esm.pretrained.* resolves locally.

Troubleshooting

Next: feed embeddings to a classifier. For structure prediction, use esmfold2.