ESM++ Large

Model overview

Synthyra/ESMplusplus_large packages the biohub/ESMC-600M checkpoint with the FastPLMs runtime for Hugging Face Transformers. It accepts amino-acid sequences tokenized to residue IDs.

The repository uses the standard Transformers loading interface with trust_remote_code=True. See Technical details for each registered class and whether its weights come from the checkpoint.

The sequence- and token-classification classes reuse the pretrained backbone, but their task heads are newly initialized. Fine-tune those heads before interpreting their logits as predictions.

Install and platform requirements

Install the direct dependencies published with this model:

python -m pip install -r \
  "https://huggingface.co/Synthyra/ESMplusplus_large/resolve/main/requirements.txt"

The FastPLMs implementation itself is embedded in the model repository. Transformers loads it through trust_remote_code=True.

This model requires Python 3.12-3.14, PyTorch 2.14, and Transformers 5.17.

The artifact requirements include the FlashAttention loader dependency. FlashAttention also requires compatible CUDA hardware and BF16 execution.

The Hub quick start needs network access for the first download. For an air-gapped run, build the manifest-pinned local artifact first and use the offline example.

Quick start

from transformers import AutoModel

model_id = "Synthyra/ESMplusplus_large"
model = AutoModel.from_pretrained(
    model_id,
    trust_remote_code=True,
    attn_implementation="sdpa",
).eval()

For offline validation, replace model_id with the manifest-built dist/hub/ESMplusplus_large path. Pass local_files_only=True.

Attention backends

The quick start uses sdpa.

Available backends are eager, sdpa, flex_attention, flash_attention_2, flash_attention_3. Requesting an unavailable backend raises instead of silently changing implementation.

output_attentions=True can use the documented one-call eager fallback to materialize attention tensors. The configured backend does not change.

Tokenization and forward inference

Load the tokenizer from the same artifact as the model. The attention mask shows padding explicitly:

import torch

from transformers import AutoTokenizer


model_id = "Synthyra/ESMplusplus_large"
tokenizer = AutoTokenizer.from_pretrained(
    model_id,
    trust_remote_code=True,
)
batch = tokenizer(
    ["MSTNPKPQRKTKRNT", "MKTIIALSYIFCLVFA"],
    padding=True,
    return_tensors="pt",
)

with torch.inference_mode():
    output = model(**batch)

print(output.last_hidden_state.shape)

Dataset embeddings

The shared embedding mixin keeps input order and biological-position masking. It accepts sequences, identified records, mappings, or a FASTA path:

pooled = model.embed_dataset(
    ["MSTNPKPQRKTKRNT", "MKTIIALSYIFCLVFA"],
    batch_size=2,
    pooling=("mean", "std"),
)
residues = model.embed_dataset(
    ["MSTNPKPQRKTKRNT"],
    full_embeddings=True,
)
print(pooled[0].tensor.shape)   # (2 * d,)
print(residues[0].tensor.shape) # (l, d)

Set output and format="safetensors" or "sqlite" for transactional, bounded-memory storage. Resume checks input order, model state, tokenizer policy, backend, dtype, and pooling configuration before it appends data.

Downstream prediction

The sequence and token prediction AutoClasses use the checkpoint backbone and create a new, untrained classifier. Sequence labels have shape (b,). Residue labels have shape (b, l) and use -100 outside biological positions.

import torch

from transformers import AutoTokenizer
from transformers import (
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
)


model_id = "Synthyra/ESMplusplus_large"
sequence_model = AutoModelForSequenceClassification.from_pretrained(
    model_id, num_labels=2, trust_remote_code=True
).eval()
token_model = AutoModelForTokenClassification.from_pretrained(
    model_id, num_labels=3, trust_remote_code=True
).eval()
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
sequences = ["MSTNPKPQRKTKRNT", "MKTIIALSYIFCLVFA"]
batch = tokenizer(sequences, padding=True, return_tensors="pt")
biological = batch["attention_mask"].bool()  # (b, l)
for special_id in tokenizer.all_special_ids:
    biological &= batch["input_ids"].ne(special_id)  # (b, l)

sequence_labels = torch.zeros(len(sequences), dtype=torch.long)  # (b,)
token_labels = torch.full_like(batch["input_ids"], -100)  # (b, l)
token_labels[biological] = 0  # selected biological positions; labels stay (b, l)

with torch.inference_mode():
    sequence_output = sequence_model(**batch, labels=sequence_labels)
    token_output = token_model(**batch, labels=token_labels)
print(sequence_output.logits.shape)  # (b, 2)
print(token_output.logits.shape)     # (b, l, 3)

PEFT fine-tuning

Install the training dependencies. Then attach LoRA to the loaded checkpoint:

python -m pip install "datasets>=5.0" "peft>=0.21"

from peft import LoraConfig, TaskType, get_peft_model


peft_model = get_peft_model(
    sequence_model,
    LoraConfig(
        task_type=TaskType.SEQ_CLS,
        r=8,
        lora_alpha=16,
        target_modules="all-linear",
        modules_to_save=["classifier"],
    ),
)

This checkpoint advertises a classification head. Save the separately trained classifier with the adapter. All FastPLMs checkpoints follow the Transformers PreTrainedModel contract and can use PEFT. The ESM2-specific shipped CLI is an example, not a support boundary. Record the target modules, base revision, data identity, and trainable parameter scope.

Test-time training

TTT samples masked views of one protein and updates only injected low-rank adapters. Base checkpoint weights stay frozen:

from transformers import AutoModelForMaskedLM


ttt_model = AutoModelForMaskedLM.from_pretrained(
    "Synthyra/ESMplusplus_large",
    trust_remote_code=True,
)
metrics = ttt_model.ttt(
    seq="MSTNPKPQRKTKRNT",
    ttt_config={"steps": 3, "batch_size": 1, "seed": 7},
)
ttt_model.save_pretrained("adapted", safe_serialization=True)
ttt_model.ttt_reset()
print(metrics)

Saved adapters retain their deterministic reset state. TTT adds latency and memory, can worsen an output, and does not show biological function.

ESMC behavior

This artifact provides the Biohub ESMC sequence encoder and masked-language- model head through Transformers. ESMFold2 also uses this language-model family. SDPA is the default and gives the highest numerical fidelity. Flex Attention and FlashAttention 3 are supported non-experimental backends. Their BF16 arithmetic can differ numerically from SDPA. These differences give diagnostic warnings, not strict-parity failures. Dispatch, masks, finite outputs, shapes, and large biological disagreements remain hard gates.

The current GH200/aarch64 release environment validates eager, SDPA, and Flex. Flash requests raise because compatible locked kernels are unavailable on this platform.

When sequence_id is supplied, it controls ESMC attention groups and padding. attention_mask is ignored. Values greater than or equal to zero are valid sequence-group IDs. -1 marks padding. Omit sequence_id to use attention_mask for padding.

Hidden-state sparse autoencoders

ESM++ supports hidden-state SAEs from the official Biohub ESMC SAE collection. This artifact implements the SAE contract, so no Biohub runtime code is needed. Select an SAE for this ESMC scale, then load only the layers you need:

import torch


model.load_sae_models("biohub/ESMC-600M-sae-layer27-k64-codebook65536", [27])

with torch.inference_mode():
    output = model(**batch, normalize_sae=True)

features = output.sae_outputs["layer27"]  # (valid_tokens, codebook_dim), sparse COO
print(features.shape, features.layout)  # (valid_token_count, codebook_dim), sparse COO

load_sae_models reads the shared config.json and one layer_{index}.safetensors shard per requested layer, from a Hub repository or a local directory, and attaches the layers on the model device in the model dtype. add_sae_models still accepts official Biohub ESMCSAEModel.layers entries.

SAEs run after you attach them. Use compute_sae=False to skip SAE work. Outputs are detached sparse tensors with keys such as layer{N}. They omit padding. The model uses sequence_id, then attention_mask, for padding. normalize_sae=True uses Biohub (features / max) * idf normalization. SAE computation requires input_ids. It rejects mask tokens because Biohub trained the SAEs with unmasked sequences. This interface supports hidden-state SAEs only, not MLP-output SAEs. FastPLMs does not copy SAE weights or add SAE checkpoints to its model manifest.

FP8 is restricted to ESMC-6B; smaller ESM++ models use BF16.

Detailed backend measurements, release guardrails, and the GH200 package compatibility exception are maintained in the attention backend guide and release evidence manifest.

Technical details

• Inputs: Amino-acid sequences tokenized to residue IDs
• Transformers classes: AutoConfig, AutoModel, AutoModelForMaskedLM, AutoModelForSequenceClassification, AutoModelForTokenClassification
• Checkpoint weights: AutoConfig = FastPLMs extension, AutoModel = pretrained, AutoModelForMaskedLM = pretrained, AutoModelForSequenceClassification = base weights + untrained task head, AutoModelForTokenClassification = base weights + untrained task head
• Attention backends: eager, sdpa, flex_attention, flash_attention_2, flash_attention_3
• Precision: default
• BF16 execution: static_parameters
• Generation contract: not_applicable
• Dependencies: core
• Weight publication allowed: true
• Weight license status: resolved
• Redistributable: true
• Complete weight publication required: false

Validation and sources

FastPLMs pins the checkpoint, upstream source revisions, state transformation, and required files in models.toml. Built artifacts record exact source identities and conversion details in source-record.json.

• FastPLMs checkpoint: Synthyra/ESMplusplus_large
• Runtime revision: recorded separately in the built artifact and published commit
• Runtime source identities: recorded in source-record.json
• Official checkpoint: biohub/ESMC-600M
• Artifact source: fast
• State transform: esmc_to_fastplms_v1
• Pinned upstreams: biohub-esm, biohub-transformers
• Release tiers: check, compliance, feature, artifact, benchmark
• Unresolved required file identities: 0

Release validation includes the compliance tier. Its evidence identifies the checkpoint, backend, dtype, hardware, inputs, and reference revision.

Declared tiers compare configuration, tokenizer behavior, state, and representative inference with the pinned reference. A nonzero unresolved count blocks release. Metadata alone does not show that a build passed, that a backend is faster, or that an output is biologically valid.

License

Checkpoint terms: MIT. The Hub model-card identifier is mit. The local artifact contains applicable source licenses, notices, attribution, and conversion records. Review them before use.