--- library_name: transformers license: "mit" tags: - protein-language-model - fastplms --- # Synthyra/ESMplusplus_large This checkpoint packages 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, and declared compliance metadata is not a claim that an arbitrary local build passed its release gate. ## Install and platform requirements Install the direct dependencies published with this model: ```bash 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 and loaded by Transformers through `trust_remote_code=True`. Python 3.11-3.14, PyTorch 2.13, and Transformers 5.13 are required. The artifact requirements include the direct FlashAttention loader dependency. FlashAttention also requires compatible CUDA hardware and BF16 execution. The Hub quick start below requires network access on first download. For an air-gapped run, first build the manifest-pinned local artifact and use the offline form shown in the example. ## Quick start ```python 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 and 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 instead of silently switching implementations. `output_attentions=True` may use the documented, one-call eager fallback solely to materialize attention tensors; the configured backend remains unchanged. This family declares the `compliance` tier. Release evidence binds the exact checkpoint, backend, dtype, hardware, inputs, and reference revision. ## Tokenization and forward inference Load the tokenizer from the same artifact as the model. Padding is represented explicitly by the attention mask: ```python 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 preserves input order and biological-position masking. It accepts sequences, identified records, mappings, or a FASTA path: ```python 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 persistence. Resume verifies input order, model state, tokenizer policy, backend, dtype, and pooling configuration before appending. ## PEFT fine-tuning Install the direct training dependencies, then attach LoRA to the loaded checkpoint: ```bash python -m pip install "datasets>=4.8,<5" "peft>=0.19,<0.20" ``` ```python 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-specific objective and preserve any new head through `modules_to_save`. All FastPLMs checkpoints follow the Transformers `PreTrainedModel` contract and can be adapted with 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 remain frozen: ```python 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) ``` Persisted adapters retain their deterministic reset state. TTT adds latency and memory, can worsen an output, and does not establish biological function. ## ESMC behavior This artifact exposes the Biohub ESMC sequence encoder and masked-language-model head through Transformers. It is also the language-model family used by ESMFold2. SDPA is the default and the recommended choice for highest numerical fidelity. Flex Attention and FlashAttention 3 are supported, non-experimental backends, but their BF16 arithmetic may be numerically divergent from SDPA. Those deviations produce diagnostic warnings rather than strict parity failures; dispatch integrity, masks, finite outputs, shapes, and catastrophic biological disagreement remain hard gates. The current GH200/aarch64 release environment validates eager, SDPA, and Flex. Flash requests fail closed because compatible locked kernels are unavailable on this platform. When `sequence_id` is supplied, it is authoritative for ESMC attention grouping and padding, and `attention_mask` is ignored. Values greater than or equal to zero are valid sequence-group IDs; `-1` denotes padding. Omit `sequence_id` to use `attention_mask` as the padding contract. | 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](https://github.com/Synthyra/FastPLMs/blob/main/docs/attention_backends.md) and [release evidence manifest](https://github.com/Synthyra/FastPLMs/blob/main/docs/generated/capability_evidence.md). ## 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` - 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_large` - Runtime revision: recorded separately in the built artifact and published commit - Source-tree and runtime-bundle SHA-256: recorded in `provenance.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` `provenance.json` records exact file identities, conversion, source revisions, legal texts, schema, and attestations. A nonzero unresolved count blocks release. ## Validation boundary Declared tiers compare applicable configuration, tokenizer behavior, state, and representative inference with the pinned reference. Metadata alone does not claim a build passed, a backend is faster, or an output is biologically valid. ## License Checkpoint terms: MIT. The Hub model-card identifier is `mit`. Applicable source licenses, notices, attribution, and conversion records are distributed with the local artifact. Review them before use.