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Synthyra/
ANKH3_large

Name: ANKH3_large
Rating: 2.5 (71 reviews)

transformers

safetensors

text2text-generation

protein-language-model

fastplms

custom_code

cc-by-nc-sa-4.0

text-generation-inference

endpoints_compatible

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ANKH3-Large

Model overview

Synthyra/ANKH3_large packages the ElnaggarLab/ankh3-large checkpoint with the FastPLMs runtime for Hugging Face Transformers. It accepts amino-acid sequences tokenized for encoder or sequence-to-sequence use.

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:

bash

python -m pip install -r \
  "https://huggingface.co/Synthyra/ANKH3_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 3.11-3.14, PyTorch 2.13, and Transformers 5.13.

The CPU gate covers small offline tests. Published checkpoint throughput and parity require the documented device tier.

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

python

from transformers import AutoModel

model_id = "Synthyra/ANKH3_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/ANKH3_large path. Pass local_files_only=True.

Attention backends

The quick start uses sdpa.

Available backends are eager, sdpa. 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

Synthyra/ANKH3_large contains the complete encoder-decoder checkpoint. AutoModel loads the encoder without the decoder. AutoModelForSeq2SeqLM loads the encoder, decoder, cross-attention, and language-model head.

Use the tokenizer from the loaded model. This keeps tokenizer files, revision, offline/cache policy, and ANKH's residue-aware pre-tokenizer aligned. Pass raw protein strings without residue spaces:

python

import torch

tokenizer = model.tokenizer
batch = tokenizer(
    ["MSTNPKPQRKTKRNT", "MKTIIALSYIFCLVFA"],
    padding=True,
    return_tensors="pt",
)

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

print(output.last_hidden_state.shape)

Dataset embeddings

Dataset embeddings use the final encoder state by default. Select a native encoder layer directly:

python

encoder_result = model.embed_dataset(
    ["MSTNPKPQRKTKRNT"],
    hidden_state_source="encoder",
    hidden_state_index=-1,
    full_embeddings=True,
)
print(encoder_result[0].tensor.shape)  # (l, d)

Decoder representations require AutoModelForSeq2SeqLM and one aligned decoder input. ANKH does not create a shifted target:

python

from transformers import AutoModelForSeq2SeqLM

seq2seq = AutoModelForSeq2SeqLM.from_pretrained(
    "Synthyra/ANKH3_large",
    trust_remote_code=True,
).eval()
decoder_result = seq2seq.embed_dataset(
    ["MSTNPKPQRKTKRNT"],
    hidden_state_source="decoder",
    hidden_state_index=-1,
    decoder_inputs=["M<extra_id_0>"],
    full_embeddings=True,
)
print(decoder_result[0].tensor.shape)  # (decoder_length, d)

Pooling excludes boundary, padding, sentinel, and other non-biological positions. Persisted results record the selected stack, layer, inputs, masks, and alignment policy.

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.

python

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

model_id = "Synthyra/ANKH3_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()
for special_id in tokenizer.all_special_ids:
    biological &= batch["input_ids"].ne(special_id)

sequence_labels = torch.zeros(len(sequences), dtype=torch.long)
token_labels = torch.full_like(batch["input_ids"], -100)
token_labels[biological] = 0

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>=4.8,<5" "peft>=0.19,<0.20"

python

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:

python

from transformers import AutoModelForMaskedLM

ttt_model = AutoModelForMaskedLM.from_pretrained(
    "Synthyra/ANKH3_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.

Encoder and sequence-to-sequence use

Synthyra/ANKH3_large contains the complete ANKH encoder-decoder checkpoint. Use AutoModel for encoder embeddings. Use AutoModelForSeq2SeqLM for task-specific decoding:

python

import torch
from transformers import AutoModel, AutoModelForSeq2SeqLM, AutoTokenizer

repo_id = "Synthyra/ANKH3_large"
tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
encoder = AutoModel.from_pretrained(repo_id, trust_remote_code=True).eval()
seq2seq = AutoModelForSeq2SeqLM.from_pretrained(
    repo_id,
    trust_remote_code=True,
).eval()
batch = tokenizer("MSTNPKPQRKTKRNT", return_tensors="pt")

with torch.inference_mode():
    encoder_hidden = encoder(**batch).last_hidden_state
    generated_ids = seq2seq.generate(**batch, max_new_tokens=16)
print(encoder_hidden.shape)
print(tokenizer.batch_decode(generated_ids, skip_special_tokens=True))

ANKH artifacts retain CC BY-NC-SA 4.0 terms. The notes below distinguish official heads from FastPLMs extensions. The complete checkpoint is larger than the former encoder-only mirror and preserves encoder-output parity.

Notes and limitations

ANKH parity covers the official encoder and sequence-to-sequence heads. AutoModelForMaskedLM exposes the separately named FastPLMs synthesized masked-LM extension and is not an official ANKH head.

Technical details

Inputs: Amino-acid sequences tokenized for encoder or sequence-to-sequence use
Transformers classes: AutoConfig, AutoModel, AutoModelForMaskedLM, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification, AutoModelForTokenClassification
Checkpoint weights: AutoConfig = FastPLMs extension, AutoModel = pretrained, AutoModelForMaskedLM = FastPLMs extension, AutoModelForSeq2SeqLM = pretrained, AutoModelForSequenceClassification = base weights + untrained task head, AutoModelForTokenClassification = base weights + untrained task head
Attention backends: eager, sdpa
Precision: default
BF16 execution: static_parameters
Generation contract: required
Dependencies: core
Weight publication allowed: true
Weight license status: resolved
Redistributable: true
Complete weight publication required: false

Validation and provenance

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/ANKH3_large
Runtime revision: recorded separately in the built artifact and published commit
Runtime source identities: recorded in source-record.json
Canonical transformed state identity: recorded in source-record.json
Conversion equality attestation: recorded in source-record.json
Official checkpoint: ElnaggarLab/ankh3-large
Artifact source: official
State transform: ankh_t5_to_fastplms_v1
Pinned upstreams: ankh
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: CC-BY-NC-SA-4.0. The Hub model-card identifier is cc-by-nc-sa-4.0. The local artifact contains applicable source licenses, notices, attribution, and conversion records. Review them before use.