Fair Esm2 Agent Skill

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
# 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

| Name | Layers | Dim | Params | Use |

| --------------------- | ------ | ---- | ------ | ---------------------------- |

| esm2_t6_8M_UR50D | 6 | 320 | 8 M | Fast smoke / tiny embeddings |

| esm2_t33_650M_UR50D | 33 | 1280 | 650 M | Default embedding model |

| esm2_t36_3B_UR50D | 36 | 2560 | 3 B | Best embeddings, 24 GB+ |

Output format

out["representations"][layer] is (B, L_max+2, D), where L_max is the

longest tokenized residue sequence in the batch. ESM-2 adds BOS/EOS and pads

shorter sequences after EOS. The single-sequence 1:-1 slice above is valid

without padding; in a mixed-length batch it includes EOS and may include padding

for shorter sequences.

For a batch, count non-padding tokens separately for each sequence, then remove

BOS/EOS before pooling. Here toks and out must come from the same batch:

token_lengths = (toks != alphabet.padding_idx).sum(1).tolist()  # includes BOS/EOS
emb = out["representations"][33]
residue_embs = [emb[i, 1 : token_length - 1] for i, token_length in enumerate(token_lengths)]
seq_embs = torch.stack([residues.mean(0) for residues in residue_embs])  # (B, D)

Use nonempty protein sequences. Keep the batch order when associating embeddings

with sequence IDs. out["contacts"] (when return_contacts=True) has shape

(B, L_max, L_max); for sequence i, retain only

out["contacts"][i, : token_lengths[i] - 2, : token_lengths[i] - 2].

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

| Symptom | Cause | Fix |

| --------------------------------------------------- | -------------------------------------------- | -------------------------------------------------------- |

| ModuleNotFoundError: No module named 'esm.models' | You want Biohub's esm fork, not fair-esm | See esmfold2 skill; this skill uses esm.pretrained.* |

| Slow first call | Downloading weights via torch.hub | Set TORCH_HOME to a cached location |

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

esmfold2.