Synthyra/ESMplusplus_small

This checkpoint contains the FastPLMs ESMC implementation.

Accepted inputs are amino-acid sequences tokenized to residue IDs. Supported Transformers entry points are AutoConfig, AutoModel, AutoModelForMaskedLM.

Capabilities

Feature	Status
Sequence classification	Unavailable: no advertised AutoClass
Token classification	Unavailable: no advertised AutoClass
PEFT fine-tuning	Supported pattern: attach LoRA to the pretrained model
Embeddings	Supported: shared ordered embedding API
Test-time training	Supported: low-rank masked-residue adaptation
Attention variants	Special: SDPA fidelity path; alternate backends have explicit bands
Compliance	Declared: exact release evidence is required

A supported interface is not a pretrained downstream predictor. Classification heads start untrained. Compliance metadata does not show that a local build passed its release gate.

Install and platform requirements

Install the direct dependencies published with this model:

python -m pip install -r \
  "https://huggingface.co/Synthyra/ESMplusplus_small/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 3.11-3.14, PyTorch 2.13, and Transformers 5.13. 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_small"
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_small path. Pass local_files_only=True.

Attention and compliance

The quick start selects sdpa explicitly. Declared variants are eager, sdpa, flex_attention, flash_attention_2, flash_attention_3. An unavailable requested backend raises. It does not silently change implementation. output_attentions=True can use the documented one-call eager fallback to materialize attention tensors. The configured backend does not change.

This family declares the compliance tier. Release evidence identifies the checkpoint, backend, dtype, hardware, inputs, and reference revision.

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_small"
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.

PEFT fine-tuning

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

python -m pip install "datasets>=4.8,<5" "peft>=0.19,<0.20"

from peft import LoraConfig, get_peft_model

peft_model = get_peft_model(
    model,
    LoraConfig(
        r=8,
        lora_alpha=16,
        target_modules="all-linear",
    ),
)

This checkpoint has no advertised classifier. Supply the task objective and preserve any new head through modules_to_save. 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_small",
    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. Select an SAE for this ESMC scale. Load only required layers. Then attach them to the model:

import torch
from transformers import AutoModel

sae = AutoModel.from_pretrained("biohub/ESMC-300M-sae-layer23-k64-codebook65536", device=model.device)
sae.initialize_layers([23])
model.add_sae_models([sae.layers["23"]])

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

features = output.sae_outputs["layer23"]
print(features.shape, features.layout)  # (valid_token_count, codebook_dim), sparse COO

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.

Experimental FP8 inference

The default uses checkpoint BF16 behavior. FP8 is an explicit experimental inference option for every ESM++ scale:

import torch
from transformers import AutoModel

fp8_model = AutoModel.from_pretrained(
    "Synthyra/ESMplusplus_small",
    trust_remote_code=True,
    dtype=torch.bfloat16,
).cuda().eval()
fp8_model.enable_fp8()
print(fp8_model.esmc_precision_status)

with torch.inference_mode():
    fp8_output = fp8_model(**{name: value.cuda() for name, value in batch.items()})

FP8 forward calls require torch.inference_mode(). The model pads the sequence dimension to a multiple of 16. Transformer Engine converts supported linear layers. The call fails if the dependency, compatible CUDA hardware, or complete conversion set is unavailable. It does not silently use BF16. FP8 does not claim numerical parity.

Backend	Support	Measurement status
sdpa	Recommended fidelity path	Pending release measurement
eager	Supported	Pending release measurement
flash_attention_2	Supported	Unavailable on current GH200/aarch64 lock
flex_attention	Supported, numerically divergent	Pending release measurement
flash_attention_3	Supported, numerically divergent	Unavailable on current GH200/aarch64 lock

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

Runtime contract

• Public input: Amino-acid sequences tokenized to residue IDs
• Advertised AutoClasses: AutoConfig, AutoModel, AutoModelForMaskedLM
• AutoClass weight status: AutoConfig = FastPLMs extension, AutoModel = pretrained, AutoModelForMaskedLM = pretrained
• Attention implementations: eager, sdpa, flex_attention, flash_attention_2, flash_attention_3
• Precision policies: default, fp8 (experimental)
• BF16 execution: static_parameters
• Generation contract: not_applicable
• Artifact dependency set: core
• Weight publication allowed: true
• Weight license status: resolved
• Redistributable: true
• Complete weight publication required: false

Release record

• FastPLMs weights: Synthyra/ESMplusplus_small
• Runtime revision: recorded in the built artifact and published commit
• Source-tree and runtime-bundle SHA-256: recorded in the source record
• Official checkpoint: biohub/ESMC-300M
• 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

The source record records exact file identities, conversion, source revisions, legal texts, schema, and attestations. A nonzero unresolved count blocks a release.

Validation boundary

Declared tiers compare configuration, tokenizer behavior, state, and representative inference with the pinned reference. Metadata 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.