schema_version = 1
legal_files = [
  "LICENSE=sha256:2d2b50c7b1414bff1189a1db1f0cfb92e3e064b50f4c2b1019827b683e1b629a",
  "THIRD_PARTY_NOTICES.md=sha256:d35e506b728868b52290672d54a89c6aade09529af99dbc8a4db72d9e9ca3460",
]

[[attention_kernels]]
implementation = "flash_attention_2"
repository = "kernels-community/flash-attn2"
revision = "db6b51744f0cd7061386442c09df890fc6d9f47e"
version = 2
expected_variant = "flash_attn2"
dtypes = ["bfloat16"]

[[attention_kernels]]
implementation = "flash_attention_3"
repository = "kernels-community/flash-attn3"
revision = "43f0bd269777115d94ff826e0d113ce9c1c9087b"
version = 1
expected_variant = "flash_attn3"
dtypes = ["bfloat16"]

[[runtime_assets]]
id = "esmfold2_ccd"
repository = "biohub/ESMFold2"
revision = "1ebf0e3481a5184eb6171d40615c79e384b48796"
path = "ccd.pkl"
sha256 = "9ff44b1927c6b9198e38ffe0928706827a09a350c15530beeeabebfa88038fc5"
size = 417306584
consumer_family = "esmfold2"
trust_kind = "hash_pinned_pickle"
license = "MIT"
offline_behavior = "requires_cached_verified_file"

[[upstreams]]
id = "ankh"
path = "vendor/upstream/ankh"
url = "https://github.com/agemagician/Ankh.git"
revision = "02b4e25ce5389b9e771c9df6e546c62af1216f8e"
license = "CC-BY-NC-SA-4.0"
license_files = ["LICENSE.md"]
license_digests = ["LICENSE.md=sha256:cd041d7f9f52936e8824ac3f754e9c67410763205fc8a7020ba74fc8b6edc088"]
distribution_files = ["LICENSE.md=sha256:cd041d7f9f52936e8824ac3f754e9c67410763205fc8a7020ba74fc8b6edc088"]

[[upstreams]]
id = "biohub-esm"
path = "vendor/upstream/biohub-esm"
url = "https://github.com/Biohub/esm.git"
revision = "82ee35553d39169d678f784c8d3f8712ffd7d2c4"
license = "MIT"
license_files = ["LICENSE.md", "THIRD_PARTY_NOTICE.md"]
license_digests = [
  "LICENSE.md=sha256:b63df9ca1dd96b3b21eec226b51b236d0bd152ac20eafc43aad46bf832b48d8a",
  "THIRD_PARTY_NOTICE.md=sha256:5bff8515ba4e0f53abdc43714c180b79c5b606160497d98de741a369cb9b6a23",
]
distribution_files = [
  "LICENSE.md=sha256:b63df9ca1dd96b3b21eec226b51b236d0bd152ac20eafc43aad46bf832b48d8a",
  "THIRD_PARTY_NOTICE.md=sha256:5bff8515ba4e0f53abdc43714c180b79c5b606160497d98de741a369cb9b6a23",
]

[[upstreams]]
id = "biohub-transformers"
path = "vendor/upstream/biohub-transformers"
url = "https://github.com/Biohub/transformers.git"
revision = "3a8956fb4d4ea16b0ec8e71deef2c2909b6a5cbf"
license = "Apache-2.0"
license_files = ["LICENSE"]
license_digests = ["LICENSE=sha256:77fd4710def9ec3c0f6225800e0235f15a425abd4a8b03559127fcd782612049"]
distribution_files = ["LICENSE=sha256:77fd4710def9ec3c0f6225800e0235f15a425abd4a8b03559127fcd782612049"]

[[upstreams]]
id = "boltz"
path = "vendor/upstream/boltz"
url = "https://github.com/jwohlwend/boltz.git"
revision = "b1ebfc46ecf57f5414e0d1a6f9027bbb122c53bc"
license = "MIT"
license_files = ["LICENSE"]
license_digests = ["LICENSE=sha256:f0667fd5e66c51e1ba8ddaa0249c6d7225b30037e02c45782d8f2c2943ac2617"]
distribution_files = ["LICENSE=sha256:f0667fd5e66c51e1ba8ddaa0249c6d7225b30037e02c45782d8f2c2943ac2617"]

[[upstreams]]
id = "dplm"
path = "vendor/upstream/dplm"
url = "https://github.com/bytedance/dplm.git"
revision = "8a2e15e53416b4536f03f79ad1f6f6a9cbd5e19d"
license = "Apache-2.0"
license_files = ["LICENSE"]
license_digests = ["LICENSE=sha256:cfc7749b96f63bd31c3c42b5c471bf756814053e847c10f3eb003417bc523d30"]
distribution_files = [
  "LICENSE=sha256:cfc7749b96f63bd31c3c42b5c471bf756814053e847c10f3eb003417bc523d30",
  "SOURCE_RECORD.md=sha256:a659f74be9073cf1ad2d2f7071531ca56959b421f111152cf4c41184ace5970e",
]

[[upstreams]]
id = "e1"
path = "vendor/upstream/e1"
url = "https://github.com/Profluent-AI/E1.git"
revision = "bfd2620a602248499f3d2583d85a7ecddf0b6e02"
license = "Apache-2.0 AND Profluent-E1-Agreement"
license_files = ["LICENSE", "ATTRIBUTION", "NOTICE"]
license_digests = [
  "LICENSE=sha256:8ef1dd556091544db3044164a8015424a3dcb3450fb3765a81b88463551bbe81",
  "ATTRIBUTION=sha256:deb22b250f6491b649eda5c63e080dd56486b8d2736cea6a52ef875436214367",
  "NOTICE=sha256:6de9db0320b4ee82f665c0951d8fd4cd53701a659c9dbce9bc3e3ea6afc4c6b3",
]
distribution_files = [
  "LICENSE=sha256:8ef1dd556091544db3044164a8015424a3dcb3450fb3765a81b88463551bbe81",
  "ATTRIBUTION=sha256:deb22b250f6491b649eda5c63e080dd56486b8d2736cea6a52ef875436214367",
  "NOTICE=sha256:6de9db0320b4ee82f665c0951d8fd4cd53701a659c9dbce9bc3e3ea6afc4c6b3",
  "Apache-2.0.txt=sha256:cfc7749b96f63bd31c3c42b5c471bf756814053e847c10f3eb003417bc523d30",
  "BSD-3-Clause.txt=sha256:36e1987f2f17db7f8ad36cd7a37dbb7aeaaf0ab68b97ab4b9d3556f3a7a76ae8",
  "MODIFICATIONS.md=sha256:2506f47c0f5475af8e8ff2cff13eb8b79e8e25a08a054cdd617bf336536750ca",
]

[[upstreams]]
id = "fair-esm"
path = "vendor/upstream/fair-esm"
url = "https://github.com/facebookresearch/esm.git"
revision = "2b369911bb5b4b0dda914521b9475cad1656b2ac"
license = "MIT"
license_files = ["LICENSE"]
license_digests = ["LICENSE=sha256:da6d3703ed11cbe42bd212c725957c98da23cbff1998c05fa4b3d976d1a58e93"]
distribution_files = [
  "LICENSE=sha256:da6d3703ed11cbe42bd212c725957c98da23cbff1998c05fa4b3d976d1a58e93",
  "SOURCE_RECORD.md=sha256:950adb94daf15e646ddf226dacfe2a8e77801aa0793e439a9a3490a48eb666e7",
]

[[upstreams]]
id = "openfold"
path = "vendor/upstream/openfold"
url = "https://github.com/aqlaboratory/openfold.git"
revision = "4b41059694619831a7db195b7e0988fc4ff3a307"
license = "Apache-2.0"
license_files = ["LICENSE"]
license_digests = ["LICENSE=sha256:cfc7749b96f63bd31c3c42b5c471bf756814053e847c10f3eb003417bc523d30"]
distribution_files = [
  "LICENSE=sha256:cfc7749b96f63bd31c3c42b5c471bf756814053e847c10f3eb003417bc523d30",
  "MODIFICATIONS.md=sha256:fd6f0aa1086a0c996cf967b326d18e965660cda0ad5c7f36a3474a8490720da3",
  "SOURCE_RECORD.md=sha256:48c903db43a217a3126afaefbac60b7ddac7efda2dfcc0cbff0bffc7d6c30081",
]

[[upstreams]]
id = "protein-ttt"
path = "vendor/upstream/protein-ttt"
url = "https://github.com/anton-bushuiev/ProteinTTT.git"
revision = "fde2817cd84b936167cc76ccabf31e5c0fe49962"
license = "MIT"
license_files = ["LICENSE"]
license_digests = ["LICENSE=sha256:bb01e7d5554f9e2e117172e56551452f68a7818df7bc8e71cd7a776a1d4ba3df"]
distribution_files = [
  "LICENSE=sha256:bb01e7d5554f9e2e117172e56551452f68a7818df7bc8e71cd7a776a1d4ba3df",
  "SOURCE_RECORD.md=sha256:dc641c37353c2efd50ccbdb316ca4aae495ec02c1563e0e15bac92f75fc482e5",
]

[families.esm2]
architecture = "ESM2"
upstreams = ["fair-esm"]
tokenizer_mode = "tokenizer"
public_input = "Amino-acid sequences tokenized to residue IDs"
extra = "core"
reference_container = "reference-esm2"
reference_adapter = "tests.parity.support.reference_adapters.esm2"
attention = ["eager", "sdpa", "flex_attention", "flash_attention_2", "flash_attention_3"]
dtypes = ["float32", "bfloat16"]
bf16_execution = "fp32_parameters_autocast"
precisions = ["default"]
vram_tier = "sequence"
checkpoint_license = "MIT"
hub_license = "mit"
weights_publication_allowed = true
state_transform = "esm2_hf_to_fastplms_v1"
conversion_provenance = "Input: the pinned official ESM2 state dictionary. Transformation: apply the deterministic esm2_hf_to_fastplms_v1 key map while preserving tensor values and materializing the tied input/output embedding values as independent tensors. Output: the pinned Synthyra FastPLMs checkpoint. Validation: release parity compares exact keys and values after the declared non-aliasing transform, tokenizer behavior, and inference. Limitation: any numerical rewrite requires a new transform identifier and exact conversion test."
representative = "esm2_8m"
documentation = "docs/models.md#esm2"
test_tiers = ["check", "compliance", "feature", "artifact", "benchmark"]
runtime_paths = ["__init__.py", "registry.py", "runtime.py", "models.toml", "models/__init__.py", "attention", "embeddings", "models/_esm_rotary.py", "models/esm2", "models/ttt.py"]
auto_map = { AutoConfig = "fastplms.models.esm2.modeling_fastesm.FastEsmConfig", AutoModel = "fastplms.models.esm2.modeling_fastesm.FastEsmModel", AutoModelForMaskedLM = "fastplms.models.esm2.modeling_fastesm.FastEsmForMaskedLM", AutoModelForSequenceClassification = "fastplms.models.esm2.modeling_fastesm.FastEsmForSequenceClassification", AutoModelForTokenClassification = "fastplms.models.esm2.modeling_fastesm.FastEsmForTokenClassification" }

[families.esm_plusplus]
architecture = "ESMC"
upstreams = ["biohub-esm", "biohub-transformers"]
tokenizer_mode = "tokenizer"
public_input = "Amino-acid sequences tokenized to residue IDs"
extra = "core"
reference_container = "reference-biohub-esm"
reference_adapter = "tests.parity.support.reference_adapters.esm_plusplus"
attention = ["eager", "sdpa", "flex_attention", "flash_attention_2", "flash_attention_3"]
dtypes = ["float32", "bfloat16"]
bf16_execution = "static_parameters"
precisions = ["default", "fp8"]
experimental_precisions = ["fp8"]
vram_tier = "sequence"
checkpoint_license = "MIT"
hub_license = "mit"
weights_publication_allowed = true
state_transform = "esmc_to_fastplms_v1"
conversion_provenance = "Input: the pinned Biohub ESMC checkpoint. Transformation: apply the deterministic esmc_to_fastplms_v1 parameter map into the FastPLMs ESMC modules. Output: the pinned Synthyra ESMplusplus checkpoint. Validation: release parity compares keys, shapes, dtypes, values, aliases, and live inference. Limitation: runtime attention and precision selection are not serialized weight transforms."
representative = "esmc_small"
documentation = "docs/models.md#esm-and-esmc"
test_tiers = ["check", "compliance", "feature", "artifact", "benchmark"]
runtime_paths = ["__init__.py", "registry.py", "runtime.py", "models.toml", "models/__init__.py", "attention", "embeddings", "models/esm_plusplus", "models/ttt.py"]
auto_map = { AutoConfig = "fastplms.models.esm_plusplus.modeling_esm_plusplus.ESMplusplusConfig", AutoModel = "fastplms.models.esm_plusplus.modeling_esm_plusplus.ESMplusplusModel", AutoModelForMaskedLM = "fastplms.models.esm_plusplus.modeling_esm_plusplus.ESMplusplusForMaskedLM", AutoModelForSequenceClassification = "fastplms.models.esm_plusplus.modeling_esm_plusplus.ESMplusplusForSequenceClassification", AutoModelForTokenClassification = "fastplms.models.esm_plusplus.modeling_esm_plusplus.ESMplusplusForTokenClassification" }

[families.esm3]
architecture = "ESM3"
upstreams = ["biohub-esm", "biohub-transformers"]
tokenizer_mode = "tokenizer"
public_input = "Sequence, structure, and function tracks prepared through the multimodal helpers"
extra = "core"
reference_container = "reference-biohub-esm"
reference_adapter = "tests.parity.support.reference_adapters.esm3"
attention = ["eager", "sdpa", "flex_attention"]
dtypes = ["float32", "bfloat16"]
bf16_execution = "fp32_parameters_autocast"
precisions = ["default"]
vram_tier = "large-sequence"
checkpoint_license = "MIT"
hub_license = "mit"
weights_publication_allowed = true
state_transform = "esm3_to_fastplms_v1"
conversion_provenance = "Input: the pinned Biohub ESM3 checkpoint. Transformation: apply the deterministic esm3_to_fastplms_v1 parameter map for the supported sequence and multimodal modules and expand BF16 checkpoint tensors to FP32 storage. Output: the pinned Synthyra ESM3 checkpoint. Validation: release parity compares exact state identity after the declared map and live feature behavior. Limitation: unsupported upstream modalities may not be inferred from this record."
representative = "esm3_small"
documentation = "docs/models.md#esm3"
test_tiers = ["check", "compliance", "feature", "artifact", "benchmark"]
runtime_paths = ["__init__.py", "registry.py", "runtime.py", "models.toml", "models/__init__.py", "attention", "embeddings", "models/esm3", "models/ttt.py"]
auto_map = { AutoConfig = "fastplms.models.esm3.modeling_esm3.FastESM3Config", AutoModel = "fastplms.models.esm3.modeling_esm3.FastESM3Model", AutoModelForSequenceClassification = "fastplms.models.esm3.modeling_esm3.FastESM3ForSequenceClassification", AutoModelForTokenClassification = "fastplms.models.esm3.modeling_esm3.FastESM3ForTokenClassification" }

[families.e1]
architecture = "E1"
upstreams = ["e1"]
tokenizer_mode = "sequence"
public_input = "Raw amino-acid sequences prepared by the native E1 adapter"
extra = "core"
reference_container = "reference-e1"
reference_adapter = "tests.parity.support.reference_adapters.e1"
attention = ["sdpa", "flex_attention"]
dtypes = ["float32", "bfloat16"]
bf16_execution = "static_parameters"
precisions = ["default"]
vram_tier = "sequence"
checkpoint_license = "Profluent-E1-Agreement"
hub_license = "other"
hub_license_name = "Profluent-E1 Clickthrough License Agreement"
hub_license_link = "https://github.com/Profluent-AI/E1/blob/main/LICENSE"
weights_publication_allowed = true
state_transform = "e1_to_fastplms_v1"
conversion_provenance = "Input: the pinned Profluent-E1 checkpoint and tokenizer-free sequence contract. Transformation: apply e1_to_fastplms_v1 to the FastPLMs encoder and official task heads, storing floating tensors in BF16. Output: the pinned Synthyra Profluent-E1 checkpoint. Validation: release parity covers state identity after the declared cast, sequence and RAG preparation, aliases, and inference. Limitation: the FastPLMs scoring extension is not represented as an official E1 head."
representative = "e1_150m"
documentation = "docs/models.md#e1"
test_tiers = ["check", "compliance", "feature", "artifact", "benchmark"]
runtime_paths = ["__init__.py", "registry.py", "runtime.py", "models.toml", "models/__init__.py", "attention", "embeddings", "models/e1", "models/ttt.py"]
auto_map = { AutoConfig = "fastplms.models.e1.modeling_e1.E1Config", AutoModel = "fastplms.models.e1.modeling_e1.E1Model", AutoModelForMaskedLM = "fastplms.models.e1.modeling_e1.E1ForMaskedLM", AutoModelForSequenceClassification = "fastplms.models.e1.modeling_e1.E1ForSequenceClassification", AutoModelForTokenClassification = "fastplms.models.e1.modeling_e1.E1ForTokenClassification" }

[families.dplm]
architecture = "DPLM"
upstreams = ["dplm"]
tokenizer_mode = "tokenizer"
public_input = "Amino-acid sequences tokenized to masked or partially masked residue IDs"
extra = "core"
reference_container = "reference-dplm"
reference_adapter = "tests.parity.support.reference_adapters.dplm"
attention = ["eager", "sdpa", "flex_attention", "flash_attention_3"]
dtypes = ["float32", "bfloat16"]
bf16_execution = "fp32_parameters_autocast"
precisions = ["default"]
vram_tier = "sequence"
checkpoint_license = "Apache-2.0"
hub_license = "apache-2.0"
weights_publication_allowed = true
state_transform = "dplm_to_fastplms_v1"
conversion_provenance = "Input: the pinned official DPLM1 checkpoint. Transformation: apply dplm_to_fastplms_v1, omitting the unused absolute-position table for rotary checkpoints and materializing the tied input/output embedding values as independent tensors. Output: the pinned Synthyra DPLM checkpoint. Validation: release parity compares exact state identity after the declared transform, tokenizer behavior, generation, and inference. License basis: the pinned ByteDance DPLM Apache-2.0 LICENSE and README explicitly scope the repository release to the pretrained DPLM1 and DPLM2 weights; immutable evidence is recorded in LICENSES/dplm/SOURCE_RECORD.md. Limitation: redistribution remains subject to Apache-2.0 and the pinned source record; no broader rights are inferred."
representative = "dplm_150m"
documentation = "docs/models.md#dplm"
test_tiers = ["check", "compliance", "feature", "artifact", "benchmark"]
runtime_paths = ["__init__.py", "registry.py", "runtime.py", "models.toml", "models/__init__.py", "attention", "embeddings", "models/_diffusion_generation.py", "models/_esm_rotary.py", "models/dplm", "models/ttt.py"]
auto_map = { AutoConfig = "fastplms.models.dplm.modeling_dplm.DPLMConfig", AutoModel = "fastplms.models.dplm.modeling_dplm.DPLMModel", AutoModelForMaskedLM = "fastplms.models.dplm.modeling_dplm.DPLMForMaskedLM", AutoModelForSequenceClassification = "fastplms.models.dplm.modeling_dplm.DPLMForSequenceClassification", AutoModelForTokenClassification = "fastplms.models.dplm.modeling_dplm.DPLMForTokenClassification" }

[families.dplm2]
architecture = "DPLM2"
upstreams = ["dplm"]
tokenizer_mode = "tokenizer"
public_input = "Tokenized amino-acid and structure tracks with explicit modality boundaries"
extra = "core"
reference_container = "reference-dplm"
reference_adapter = "tests.parity.support.reference_adapters.dplm2"
attention = ["sdpa"]
dtypes = ["float32", "bfloat16"]
bf16_execution = "fp32_parameters_autocast"
precisions = ["default"]
vram_tier = "sequence"
checkpoint_license = "Apache-2.0"
hub_license = "apache-2.0"
weights_publication_allowed = true
state_transform = "dplm2_to_fastplms_v1"
conversion_provenance = "Input: the pinned official DPLM2 checkpoint. Transformation: apply dplm2_to_fastplms_v1, retaining the independent language-model head and trained encoder contact head while omitting the unused absolute-position table for rotary checkpoints. Output: the pinned Synthyra DPLM2 checkpoint. Validation: release parity compares exact keys and values after the declared omission, non-aliasing, tokenizer behavior, generation, and inference. License basis: the pinned ByteDance DPLM Apache-2.0 LICENSE and README explicitly scope the repository release to the pretrained DPLM1 and DPLM2 weights; immutable evidence is recorded in LICENSES/dplm/SOURCE_RECORD.md. Limitation: no head exception is permitted by this source record, and redistribution remains subject to Apache-2.0."
representative = "dplm2_150m"
documentation = "docs/models.md#dplm2"
test_tiers = ["check", "compliance", "feature", "artifact", "benchmark"]
runtime_paths = ["__init__.py", "registry.py", "runtime.py", "models.toml", "models/__init__.py", "attention", "embeddings", "models/_diffusion_generation.py", "models/_esm_rotary.py", "models/dplm2", "models/ttt.py"]
auto_map = { AutoConfig = "fastplms.models.dplm2.modeling_dplm2.DPLM2Config", AutoModel = "fastplms.models.dplm2.modeling_dplm2.DPLM2Model", AutoModelForMaskedLM = "fastplms.models.dplm2.modeling_dplm2.DPLM2ForMaskedLM", AutoModelForSequenceClassification = "fastplms.models.dplm2.modeling_dplm2.DPLM2ForSequenceClassification", AutoModelForTokenClassification = "fastplms.models.dplm2.modeling_dplm2.DPLM2ForTokenClassification" }
tokenizer_class = "fastplms.models.dplm2.tokenization_dplm2.DPLM2Tokenizer"

[families.ankh]
architecture = "ANKH"
upstreams = ["ankh"]
tokenizer_mode = "tokenizer"
public_input = "Amino-acid sequences tokenized for encoder or sequence-to-sequence use"
extra = "core"
reference_container = "reference-ankh"
reference_adapter = "tests.parity.support.reference_adapters.ankh"
attention = ["eager", "sdpa"]
dtypes = ["float32", "bfloat16"]
bf16_execution = "static_parameters"
precisions = ["default"]
vram_tier = "large-sequence"
checkpoint_license = "CC-BY-NC-SA-4.0"
hub_license = "cc-by-nc-sa-4.0"
weights_publication_allowed = true
state_transform = "ankh_t5_to_fastplms_v1"
conversion_provenance = "Input: the pinned official ANKH T5 checkpoint. Transformation: apply ankh_t5_to_fastplms_v1 to the official encoder and sequence-to-sequence heads. Output: the pinned Synthyra ANKH checkpoint. Validation: release parity compares exact mapped state, tokenizer behavior, official heads, and inference. Limitation: the separately named FastPLMs masked-language-model extension is not an official ANKH head."
representative = "ankh_base"
documentation = "docs/models.md#ankh"
test_tiers = ["check", "compliance", "feature", "artifact", "benchmark"]
requires_complete_weight_publication = false
runtime_paths = ["__init__.py", "registry.py", "runtime.py", "models.toml", "models/__init__.py", "attention", "embeddings", "models/ankh", "models/ttt.py"]
auto_map = { AutoConfig = "fastplms.models.ankh.modeling_ankh.FastAnkhConfig", AutoModel = "fastplms.models.ankh.modeling_ankh.FastAnkhModel", AutoModelForMaskedLM = "fastplms.models.ankh.modeling_ankh.FastAnkhForMaskedLMExtension", AutoModelForSeq2SeqLM = "fastplms.models.ankh.modeling_ankh.FastAnkhForConditionalGeneration", AutoModelForSequenceClassification = "fastplms.models.ankh.modeling_ankh.FastAnkhForSequenceClassification", AutoModelForTokenClassification = "fastplms.models.ankh.modeling_ankh.FastAnkhForTokenClassification" }

[families.boltz2]
architecture = "Boltz2"
upstreams = ["boltz"]
tokenizer_mode = "structure"
public_input = "Raw amino-acid sequences through the convenience API, or prepared model features"
extra = "structure"
reference_container = "reference-boltz2"
reference_adapter = "tests.parity.support.reference_adapters.boltz"
attention = ["eager"]
dtypes = ["float32", "bfloat16"]
bf16_execution = "fp32_parameters_autocast"
precisions = ["default"]
vram_tier = "structure"
checkpoint_license = "MIT"
hub_license = "mit"
weights_publication_allowed = true
state_transform = "boltz2_inference_core_v1"
conversion_provenance = "Input: the pinned official Boltz2 checkpoint. Transformation: select and map the supported Boltz2 inference-core parameters with boltz2_inference_core_v1. Output: the pinned Synthyra Boltz2 checkpoint. Validation: release parity covers state identity for the declared subset, feature preparation, seeded inference, and structure outputs. Limitation: this record does not claim support for undeclared upstream training components."
representative = "boltz2"
documentation = "docs/models.md#boltz2"
test_tiers = ["structure", "artifact", "benchmark"]
runtime_paths = ["__init__.py", "registry.py", "runtime.py", "models.toml", "models/__init__.py", "models/boltz"]
auto_map = { AutoConfig = "fastplms.models.boltz.modeling_boltz2.Boltz2Config", AutoModel = "fastplms.models.boltz.modeling_boltz2.Boltz2Model" }

[families.esmfold]
architecture = "ESMFold"
upstreams = ["fair-esm", "openfold"]
tokenizer_mode = "structure"
public_input = "Raw amino-acid sequences through folding helpers, or prepared residue tensors"
extra = "structure"
reference_container = "reference-esmfold"
reference_adapter = "tests.parity.support.reference_adapters.esmfold"
attention = ["eager", "sdpa", "flex_attention"]
dtypes = ["float32", "bfloat16"]
bf16_execution = "fp32_parameters_autocast"
precisions = ["default"]
vram_tier = "structure"
checkpoint_license = "MIT"
hub_license = "mit"
weights_publication_allowed = true
state_transform = "esmfold_meta_to_fastplms_v1"
conversion_provenance = "Input: the pinned native Meta ESMFold checkpoint plus its pinned ESM2 backbone. Transformation: apply esmfold_meta_to_fastplms_v1 to map native ESM2 names into the structure-only FastPLMs backbone, retain folding tensors, omit five deterministically reconstructed geometry buffers, omit the folding-unused ESM2 masked-LM and contact-regression heads, and remove the obsolete random FastPLMs TTT head from earlier mirrors. Output: canonical FP32 FastPLMs ESMFold state with an explicit CUDA BF16-autocast execution path. Validation: release parity compares exact mapped keys, shapes, dtypes, values, aliases, semantic configuration, FP32 and BF16-compute seeded inference, and structure metrics with pLDDT normalized to (0, 1). Limitation: ESMFold TTT is rejected because the official checkpoint contains no trained masked-language-model head."
representative = "esmfold"
documentation = "docs/models.md#esmfold"
test_tiers = ["check", "compliance", "structure", "feature", "artifact", "benchmark"]
runtime_paths = ["__init__.py", "registry.py", "runtime.py", "models.toml", "models/__init__.py", "attention", "embeddings", "models/_esm_rotary.py", "models/classification_probe.py", "models/esmfold"]
auto_map = { AutoConfig = "fastplms.models.esmfold.modeling_fast_esmfold.FastEsmFoldConfig", AutoModel = "fastplms.models.esmfold.modeling_fast_esmfold.FastEsmForProteinFolding", AutoModelForSequenceClassification = "fastplms.models.esmfold.modeling_fast_esmfold.FastEsmForSequenceClassification", AutoModelForTokenClassification = "fastplms.models.esmfold.modeling_fast_esmfold.FastEsmForTokenClassification" }

[families.esmfold2]
architecture = "ESMFold2"
upstreams = ["biohub-esm", "biohub-transformers", "protein-ttt"]
backbone_model = "esmc_6b"
tokenizer_mode = "structure"
public_input = "Raw amino-acid sequences or typed molecular-complex specifications; low-level forward accepts prepared feature tensors"
extra = "structure"
reference_container = "reference-esmfold2"
reference_adapter = "tests.parity.support.reference_adapters.esmfold2"
attention = ["eager", "sdpa", "flex_attention"]
dtypes = ["float32", "bfloat16"]
bf16_execution = "fp32_parameters_autocast"
precisions = ["auto", "fp32", "bf16", "fp8"]
experimental_precisions = ["fp8"]
vram_tier = "structure-6b"
checkpoint_license = "MIT"
hub_license = "mit"
weights_publication_allowed = true
state_transform = "identity"
conversion_provenance = "Input: each pinned Biohub ESMFold2 checkpoint and its separately pinned ESMC checkpoint. Transformation: apply identity to preserve the folding checkpoint exactly, load its parameters in FP32 for CUDA BF16-autocast execution, retain canonical BF16 ESMC weights, and optionally rebuild exactly 80 ESMC attention output projections as transient Transformer Engine linears. Output: the corresponding pinned Synthyra ESMFold2 checkpoint plus its declared ESMC precision policy. Validation: release parity covers exact canonical state, learned projection, prepared features, and seeded BF16 folding; experimental FP8 validation covers strict unavailable-device behavior, the four 6B-backbone variants, and three BF16-to-FP8 reload cycles on the standard variant. Limitation: only the six manifest-listed ESMFold2 variants are supported; FP8 is experimental, applies only to inference-time ESMC execution, and requires direct CUDA loading with Transformer Engine availability."
representative = "esmfold2"
documentation = "docs/esmfold2.md"
test_tiers = ["check", "compliance", "structure", "feature", "artifact", "benchmark"]
runtime_paths = ["__init__.py", "registry.py", "runtime.py", "models.toml", "models/__init__.py", "attention", "embeddings", "models/classification_probe.py", "models/_esm_rotary.py", "models/esmfold2", "models/esm_plusplus", "models/ttt.py"]
auto_map = { AutoConfig = "fastplms.models.esmfold2.configuration_esmfold2.ESMFold2Config", AutoModel = "fastplms.models.esmfold2.modeling_esmfold2.ESMFold2Model", AutoModelForSequenceClassification = "fastplms.models.esmfold2.modeling_esmfold2_classification.ESMFold2ForSequenceClassification", AutoModelForTokenClassification = "fastplms.models.esmfold2.modeling_esmfold2_classification.ESMFold2ForTokenClassification" }

[[models]]
id = "esm2_8m"
family = "esm2"
size_category = "small"
generation_contract = "not_applicable"
official_golden = { metadata = "tests/goldens/esm2_8m.json=sha256:6975e86d1d8f27488bf2a676551feaa48cc19254c9d24b6acb09198122745609", tensors = "tests/goldens/esm2_8m.safetensors=sha256:b40217566c33c71988d28869de353be54a3b3ebfc21fdfd29056e88cf7e99f4c" }
fast_repo = "Synthyra/ESM2-8M"
fast_revision = "185ecbd45665d050a8dae326d91886d330c5f9d0"
fast_files = [
  "config.json=git-sha1:46d0a7b517f59123c6ebc6d1011585731cbab259",
  "model.safetensors=sha256:c824e6ded5fb71c72bc5ac05300699947819023cb26cdaf6897665e6b2645e1b",
  "special_tokens_map.json=git-sha1:ba0f9b53dbbf27934f7555e5d31e37bdea9317f1",
  "tokenizer_config.json=git-sha1:3cfc5db0c6790859a3bc2a4dc053a813acd65295",
  "vocab.txt=git-sha1:6b946952cc35537226f07fd70957ee2f848880d2",
]
official_repo = "facebook/esm2_t6_8M_UR50D"
official_revision = "c731040fcd8d73dceaa04b0a8e6329b345b0f5df"
official_files = [
  "config.json=git-sha1:c2c6e65a87d9d20d47699ae236d605b80c741dd3",
  "model.safetensors=sha256:24c5fa474c48f3b754b86efe752d5f189d2bcd88190fa2270fc92b2ef3034189",
  "special_tokens_map.json=git-sha1:ba0f9b53dbbf27934f7555e5d31e37bdea9317f1",
  "tokenizer_config.json=git-sha1:3f0d47e841e1cb75257aeaf76d156802899a217e",
  "vocab.txt=git-sha1:6b946952cc35537226f07fd70957ee2f848880d2",
]

[[models.oracle_assets]]
role = "weights"
path = "models/esm2_t6_8M_UR50D.pt"
url = "https://dl.fbaipublicfiles.com/fair-esm/models/esm2_t6_8M_UR50D.pt"
sha256 = "46f002a9870c9bdecd0ea887acb1f9a38a6b561e8f8bf8a6990b679b9d31b928"
size = 30099493

[[models.oracle_assets]]
role = "contact_regression"
path = "regression/esm2_t6_8M_UR50D-contact-regression.pt"
url = "https://dl.fbaipublicfiles.com/fair-esm/regression/esm2_t6_8M_UR50D-contact-regression.pt"
sha256 = "8f7a4557d57713b97ba0e484303007efb7230d25299c0ac47a0a1b12a87bbb9d"
size = 1511

[[models]]
id = "esm2_35m"
family = "esm2"
size_category = "small"
generation_contract = "not_applicable"
official_golden = { metadata = "tests/goldens/esm2_35m.json=sha256:e919d3ce6d20b6a942d27d92323814ae7594a0129dc9c4de27c5053e96675bcd", tensors = "tests/goldens/esm2_35m.safetensors=sha256:c9b8bb616cf884fb7744521a2fcc6eed23586342d11241e6c9ef16454ec31e17" }
fast_repo = "Synthyra/ESM2-35M"
fast_revision = "37ab9f56b41e365b3bd9e25d6fefe9150fd910f0"
fast_files = [
  "config.json=git-sha1:4d428c9934572f39e2a00db162249971f37c88e4",
  "model.safetensors=sha256:21d95ab6bb9aa91bfec87eff11da61a657b732f2df279cbddbae6a7f1f0bba9c",
  "special_tokens_map.json=git-sha1:ba0f9b53dbbf27934f7555e5d31e37bdea9317f1",
  "tokenizer_config.json=git-sha1:3cfc5db0c6790859a3bc2a4dc053a813acd65295",
  "vocab.txt=git-sha1:6b946952cc35537226f07fd70957ee2f848880d2",
]
official_repo = "facebook/esm2_t12_35M_UR50D"
official_revision = "6fbf070e65b0b7291e7bbcd451118c216cff79d8"
official_files = [
  "config.json=git-sha1:3f64131bb610ed1ce482c4b5421fc358c785278f",
  "model.safetensors=sha256:e35647818e0e064351d4531ed480d225a002567b4b2b93ad3a9246d753150fc0",
  "special_tokens_map.json=git-sha1:ba0f9b53dbbf27934f7555e5d31e37bdea9317f1",
  "tokenizer_config.json=git-sha1:3f0d47e841e1cb75257aeaf76d156802899a217e",
  "vocab.txt=git-sha1:6b946952cc35537226f07fd70957ee2f848880d2",
]

[[models.oracle_assets]]
role = "weights"
path = "models/esm2_t12_35M_UR50D.pt"
url = "https://dl.fbaipublicfiles.com/fair-esm/models/esm2_t12_35M_UR50D.pt"
sha256 = "7f21e80e61d16a71735163ef555d3009afb0c98da74c48e29df08606973cc55e"
size = 134095705

[[models.oracle_assets]]
role = "contact_regression"
path = "regression/esm2_t12_35M_UR50D-contact-regression.pt"
url = "https://dl.fbaipublicfiles.com/fair-esm/regression/esm2_t12_35M_UR50D-contact-regression.pt"
sha256 = "16641e05d830d0ce863dd152dbb8c2f3ddfa3c3ec2a66080152c8abad01d8585"
size = 1959

[[models]]
id = "esm2_150m"
family = "esm2"
size_category = "medium"
generation_contract = "not_applicable"
official_golden = { metadata = "tests/goldens/esm2_150m.json=sha256:c04c93486024ba0fa1c81fbfbe92ee79d1d4c7f1cfcc2c9886728522f752feab", tensors = "tests/goldens/esm2_150m.safetensors=sha256:c03fe9916dba137b452a6bbe944c7dc414db4019a6f0921e87b92d4bb6a8a42f" }
fast_repo = "Synthyra/ESM2-150M"
fast_revision = "979e0880dfc9e0c0080839b83d9d2dc05b92786a"
fast_files = [
  "config.json=git-sha1:efeae2af182b7d34dc35740a45f157661e7acdf4",
  "model.safetensors=sha256:d1f7c60f98c31af328381519a750972b6a31b13b97aa7cca2e71b5ae1b3f8f53",
  "special_tokens_map.json=git-sha1:ba0f9b53dbbf27934f7555e5d31e37bdea9317f1",
  "tokenizer_config.json=git-sha1:3cfc5db0c6790859a3bc2a4dc053a813acd65295",
  "vocab.txt=git-sha1:6b946952cc35537226f07fd70957ee2f848880d2",
]
official_repo = "facebook/esm2_t30_150M_UR50D"
official_revision = "a695f6045e2e32885fa60af20c13cb35398ce30c"
official_files = [
  "config.json=git-sha1:52e04179e6fbad6663a94ea5cc44f09d764c5cd4",
  "model.safetensors=sha256:c3f1da8aea53bddd32c246c86168c23b9fd72341fb9db9a94436f855f5053566",
  "special_tokens_map.json=git-sha1:ba0f9b53dbbf27934f7555e5d31e37bdea9317f1",
  "tokenizer_config.json=git-sha1:3f0d47e841e1cb75257aeaf76d156802899a217e",
  "vocab.txt=git-sha1:6b946952cc35537226f07fd70957ee2f848880d2",
]

[[models.oracle_assets]]
role = "weights"
path = "models/esm2_t30_150M_UR50D.pt"
url = "https://dl.fbaipublicfiles.com/fair-esm/models/esm2_t30_150M_UR50D.pt"
sha256 = "881c7176cf198ef8dec26a3c375d40eb58d0c33df95c22562ca6cc6d3f812c62"
size = 592774773

[[models.oracle_assets]]
role = "contact_regression"
path = "regression/esm2_t30_150M_UR50D-contact-regression.pt"
url = "https://dl.fbaipublicfiles.com/fair-esm/regression/esm2_t30_150M_UR50D-contact-regression.pt"
sha256 = "6a604b96722ed052eef8a094ad90b275ba2e987d406315dbed0bdc6b3c4238a7"
size = 3431

[[models]]
id = "esm2_650m"
family = "esm2"
size_category = "large"
generation_contract = "not_applicable"
official_golden = { metadata = "tests/goldens/esm2_650m.json=sha256:f18332172fcb3abf5dd2485fd55f5b0d193ad3b93a44cc744e0d02817c927477", tensors = "tests/goldens/esm2_650m.safetensors=sha256:c3a66b75add03628e62e238cb63da6a9e4d321f8160e84bdf2a131c096977f86" }
fast_repo = "Synthyra/ESM2-650M"
fast_revision = "ca0718a5d52b80d5c60dd76860e55e061a95fb0a"
fast_files = [
  "config.json=git-sha1:88f6bd240680b29c3244df8292246048401f5caf",
  "model.safetensors=sha256:a15142e94ecf36f0edde9b37796f591e609ebe1694ca411e93640f0ee384994a",
  "special_tokens_map.json=git-sha1:ba0f9b53dbbf27934f7555e5d31e37bdea9317f1",
  "tokenizer_config.json=git-sha1:3cfc5db0c6790859a3bc2a4dc053a813acd65295",
  "vocab.txt=git-sha1:6b946952cc35537226f07fd70957ee2f848880d2",
]
official_repo = "facebook/esm2_t33_650M_UR50D"
official_revision = "08e4846e537177426273712802403f7ba8261b6c"
official_files = [
  "config.json=git-sha1:a956a25d277f30bd870d3760b9a116f19ead885e",
  "model.safetensors=sha256:a08adabb949fa67ad3c14b509d04fd60368b35007b0095e3358f81200c4f4db0",
  "special_tokens_map.json=git-sha1:ba0f9b53dbbf27934f7555e5d31e37bdea9317f1",
  "tokenizer_config.json=git-sha1:3f0d47e841e1cb75257aeaf76d156802899a217e",
  "vocab.txt=git-sha1:6b946952cc35537226f07fd70957ee2f848880d2",
]

[[models.oracle_assets]]
role = "weights"
path = "models/esm2_t33_650M_UR50D.pt"
url = "https://dl.fbaipublicfiles.com/fair-esm/models/esm2_t33_650M_UR50D.pt"
sha256 = "ea9d0522b335a8778dea6535a65301f10208dece28cd5865482b0b1fc446168c"
size = 2604537549

[[models.oracle_assets]]
role = "contact_regression"
path = "regression/esm2_t33_650M_UR50D-contact-regression.pt"
url = "https://dl.fbaipublicfiles.com/fair-esm/regression/esm2_t33_650M_UR50D-contact-regression.pt"
sha256 = "8ffe6edbd4173dc8d45c2cd5cb27d43aad77ec26b4c768200c58ae1f96693575"
size = 3687

[[models]]
id = "esm2_3b"
family = "esm2"
size_category = "xlarge"
generation_contract = "not_applicable"
official_golden = { metadata = "tests/goldens/esm2_3b.json=sha256:5043b2333c57a34d54fac53916722d1acb4b6fd50395b9abafa805435b184a48", tensors = "tests/goldens/esm2_3b.safetensors=sha256:dfd5a8cb05d3e814a080185c4808c8e7ec2277f070f395562fcfbe4376789e4e" }
notes = "The pinned default SDPA BF16 path uses a checkpoint-specific numeric calibration: relative L2 target/hard limit 0.06/0.07, relative Q99.9 0.15/0.18, first-percentile residue cosine 0.994/0.992, and pooled cosine 0.998/0.997. Exact state identity and the global logits-distribution contract remain required."
fast_repo = "Synthyra/ESM2-3B"
fast_revision = "ff89d0180f414ab9c677219a25da79bf09185456"
fast_files = [
  "config.json=git-sha1:94944ad6cabaa40a3ce1cbe6699cf464fdc1b2c0",
  "model-00001-of-00003.safetensors=sha256:04b57854545c23779b562ee2ae22f10021ba0f4d586ba0ad482ee6eda187d562",
  "model-00002-of-00003.safetensors=sha256:34954aaa05bc91635776ba6672946da5822626753d80db97b38c0538e9525102",
  "model-00003-of-00003.safetensors=sha256:a6b3a55b9e3b2e1778de34c665c3dd17bdfdf6da9d6d5c97730c57168709ccae",
  "special_tokens_map.json=git-sha1:ba0f9b53dbbf27934f7555e5d31e37bdea9317f1",
  "tokenizer_config.json=git-sha1:3cfc5db0c6790859a3bc2a4dc053a813acd65295",
  "vocab.txt=git-sha1:6b946952cc35537226f07fd70957ee2f848880d2",
]
official_repo = "facebook/esm2_t36_3B_UR50D"
official_revision = "476b639933c8baad5ad09a60ac1a87f987b656fc"
official_files = [
  "config.json=git-sha1:69e7563923f87d2d7439bfb83e5a19b44b46d71b",
  "pytorch_model-00001-of-00002.bin=sha256:0f971f11c449d21422aa982b791619c10351972992c735f4c3cd43fe09790412",
  "pytorch_model-00002-of-00002.bin=sha256:7560b46fc383c691fb74b915b7d4bcef40d3df181447f16ba4b298845e308d0c",
  "special_tokens_map.json=git-sha1:ba0f9b53dbbf27934f7555e5d31e37bdea9317f1",
  "tokenizer_config.json=git-sha1:3f0d47e841e1cb75257aeaf76d156802899a217e",
  "vocab.txt=git-sha1:6b946952cc35537226f07fd70957ee2f848880d2",
]

[[models.oracle_assets]]
role = "weights"
path = "models/esm2_t36_3B_UR50D.pt"
url = "https://dl.fbaipublicfiles.com/fair-esm/models/esm2_t36_3B_UR50D.pt"
sha256 = "7de8b4082ba15891959ab368b77ce3886697af1efb16d3c9e9e7b0c5d3f07500"
size = 5678116398

[[models.oracle_assets]]
role = "contact_regression"
path = "regression/esm2_t36_3B_UR50D-contact-regression.pt"
url = "https://dl.fbaipublicfiles.com/fair-esm/regression/esm2_t36_3B_UR50D-contact-regression.pt"
sha256 = "4da500eab246481dc9c8c95bc7b1d02f2803d761c380b0e95186d4a07d0fc84e"
size = 6759

[[models]]
id = "esmc_small"
family = "esm_plusplus"
size_category = "medium"
generation_contract = "not_applicable"
official_golden = { metadata = "tests/goldens/esmc_small.json=sha256:bb02652cf3cc484756b98ffa4ba55ed4c55870d2cea3342adb1d920ba9dfe10a", tensors = "tests/goldens/esmc_small.safetensors=sha256:03378d0f0fdd8161178ebb2c1f0da1b9776a726c8e8d3a10c009808a24de5654" }
notes = "Release contract: SDPA must match the pinned Biohub implementation bit-for-bit across every hidden state, last hidden state, logits, special token, and padding position. Eager and FlashAttention 2 are release-gated in BF16 against the pinned boundary-length and biological panels with a relative-L2 engineering target of 0.029, hard limit of 0.03, relative-Q99.9 target of 0.049, first-percentile residue-cosine target of 0.997, and Jensen-Shannon target of 0.0004. The global pooled-cosine and top-1 thresholds remain unchanged. Flex Attention and FlashAttention 3 remain selectable as opt-in alternatives, but they are not strict-parity choices: on the locked H100 BF16 generated-boundary panel, ESMC-6B Flex Attention exceeds the 0.03 relative-L2 hard limit and FlashAttention 3 falls below the 0.995 residue-cosine hard limit. The deviation is consistent with backend-specific BF16 kernel arithmetic; it is not a weight-conversion difference or silent fallback. Use SDPA for exact Biohub parity or FlashAttention 2 for release-gated acceleration."
fast_repo = "Synthyra/ESMplusplus_small"
fast_revision = "46c5f7d562e47d4c14165b424c71ab7db008e6fb"
fast_files = [
  "config.json=git-sha1:df2f44187157b0cc371c48c887b77b1783679201",
  "model.safetensors=sha256:d099223765bc4f1ae8d6c7e18561ce41df1d54073fdc5327ef0a229235a8f52a",
  "special_tokens_map.json=git-sha1:c907ee1dc19b24241749b32d665c291c7e6e8e4b",
  "tokenizer.json=git-sha1:f49735e56cebab0e791aeaae777757b7fd114f71",
  "tokenizer_config.json=git-sha1:2985ed2b8aa8ecfb1d12f53f47d2b8a44cc21756",
]
official_repo = "biohub/ESMC-300M"
official_revision = "a59b831785f907e96e6a246b1d142bfb76df31ee"
official_files = [
  "config.json=git-sha1:9a49eacf4e65c39f74381f0f0d240e3b89ef43d7",
  "model.safetensors=sha256:0772d8fe64bb25e14fe6f23b80e3c9a7d215d0da3c6cba5bd356d7c0e0bb22cc",
  "special_tokens_map.json=git-sha1:c907ee1dc19b24241749b32d665c291c7e6e8e4b",
  "tokenizer.json=git-sha1:81c797f56768b22dec0301fa771f018b7e43e98c",
  "tokenizer_config.json=git-sha1:2238856624f8d39f03af53a2576c2d9b18c82f61",
]

[[models]]
id = "esmc_large"
family = "esm_plusplus"
size_category = "large"
generation_contract = "not_applicable"
official_golden = { metadata = "tests/goldens/esmc_large.json=sha256:7a4d614f67b6fde417f3fd89f61e7ec442ae284769734b2b73e14945a816a8fd", tensors = "tests/goldens/esmc_large.safetensors=sha256:e13302df4cf7e8381552f1043a8fd0f31f3e0d50b2ab6009fb86b7940ae8ff79" }
notes = "Release contract: SDPA must match the pinned Biohub implementation bit-for-bit across every hidden state, last hidden state, logits, special token, and padding position. Eager and FlashAttention 2 are release-gated in BF16 against the pinned boundary-length and biological panels with a relative-L2 engineering target of 0.029, hard limit of 0.03, relative-Q99.9 target of 0.049, first-percentile residue-cosine target of 0.997, and Jensen-Shannon target of 0.0004. The global pooled-cosine and top-1 thresholds remain unchanged. Flex Attention and FlashAttention 3 remain selectable as opt-in alternatives, but they are not strict-parity choices: on the locked H100 BF16 generated-boundary panel, ESMC-6B Flex Attention exceeds the 0.03 relative-L2 hard limit and FlashAttention 3 falls below the 0.995 residue-cosine hard limit. The deviation is consistent with backend-specific BF16 kernel arithmetic; it is not a weight-conversion difference or silent fallback. Use SDPA for exact Biohub parity or FlashAttention 2 for release-gated acceleration."
fast_repo = "Synthyra/ESMplusplus_large"
fast_revision = "f813401638b3fddab09748aec1ad2bf537aa4208"
fast_files = [
  "config.json=git-sha1:5736371902fe5d04e2859be30ac7dbd31b271b25",
  "model.safetensors=sha256:4aff3f8c5de68c4d3e3824eb2c478e4a47355d3f849f3c745e5c8a5ee6cff851",
  "special_tokens_map.json=git-sha1:c907ee1dc19b24241749b32d665c291c7e6e8e4b",
  "tokenizer.json=git-sha1:f49735e56cebab0e791aeaae777757b7fd114f71",
  "tokenizer_config.json=git-sha1:2985ed2b8aa8ecfb1d12f53f47d2b8a44cc21756",
]
official_repo = "biohub/ESMC-600M"
official_revision = "a7e82012c83126b9eedb055fea9fa84b6c02f094"
official_files = [
  "config.json=git-sha1:71c8241dc28a5fb636248267a0927c0242b264c1",
  "model.safetensors=sha256:e4232c30fd35fe2f57051ec88a703996ac94520580b4b836894207a3d45d9ff8",
  "special_tokens_map.json=git-sha1:c907ee1dc19b24241749b32d665c291c7e6e8e4b",
  "tokenizer.json=git-sha1:81c797f56768b22dec0301fa771f018b7e43e98c",
  "tokenizer_config.json=git-sha1:2238856624f8d39f03af53a2576c2d9b18c82f61",
]

[[models]]
id = "esmc_6b"
family = "esm_plusplus"
size_category = "xlarge"
generation_contract = "not_applicable"
official_golden = { metadata = "tests/goldens/esmc_6b.json=sha256:e229d938719782f280fab22dfc4c43e86109fdb0cc523631168c5a491afaace3", tensors = "tests/goldens/esmc_6b.safetensors=sha256:a948945e985c7deaca7be8b7eed09c0a9521a2af3f2b10fc2ec7a7d2a0f99ada" }
notes = "Release contract: SDPA must match the pinned Biohub implementation bit-for-bit across every hidden state, last hidden state, logits, special token, and padding position. Eager and FlashAttention 2 are release-gated in BF16 against the pinned boundary-length and biological panels with a relative-L2 engineering target of 0.029, hard limit of 0.03, relative-Q99.9 target of 0.049, first-percentile residue-cosine target of 0.997, and Jensen-Shannon target of 0.0004. The global pooled-cosine and top-1 thresholds remain unchanged. Flex Attention and FlashAttention 3 remain selectable as opt-in alternatives, but they are not strict-parity choices: on the locked H100 BF16 generated-boundary panel, ESMC-6B Flex Attention exceeds the 0.03 relative-L2 hard limit and FlashAttention 3 falls below the 0.995 residue-cosine hard limit. The deviation is consistent with backend-specific BF16 kernel arithmetic; it is not a weight-conversion difference or silent fallback. Use SDPA for exact Biohub parity or FlashAttention 2 for release-gated acceleration."
fast_repo = "Synthyra/ESMplusplus_6B"
fast_revision = "0d579cce3b0f09efa6b3baddf6cc3fd8c9b616c8"
fast_files = [
  "config.json=git-sha1:e740cbcf211f2511c70c25a1ff6017a757ba7a69",
  "model-00001-of-00006.safetensors=sha256:d30d18703453019f2d2d050866309888720c28eebc9a10307d1ddf3799e85a65",
  "model-00002-of-00006.safetensors=sha256:b3d85378ab5023f4160a96e9c8cbd4cc6f78a771a83c856e88d48112f555bc13",
  "model-00003-of-00006.safetensors=sha256:52595519b59349c5c6e373e6f5ca4a3d48ea6dde345f7e61e24766df5fab0e5b",
  "model-00004-of-00006.safetensors=sha256:e46c6113c89c6f3e9b072c1bef02d763a625c37bcd8f9da2ed9363891c9a0758",
  "model-00005-of-00006.safetensors=sha256:6d92cb2bf9791de644de2ae86f8523d802ac3b4aaabfff0716ab6c2b97f6fb14",
  "model-00006-of-00006.safetensors=sha256:5fc1a8632490bb34162823c35d0d591337b9e4195b22cc0560741397a6e9d0b3",
  "special_tokens_map.json=git-sha1:c907ee1dc19b24241749b32d665c291c7e6e8e4b",
  "tokenizer.json=git-sha1:f49735e56cebab0e791aeaae777757b7fd114f71",
  "tokenizer_config.json=git-sha1:2985ed2b8aa8ecfb1d12f53f47d2b8a44cc21756",
]
official_repo = "biohub/ESMC-6B"
official_revision = "45b0fa5d7fb06faefbd5e3b89bdcef35d564e79a"
official_files = [
  "config.json=git-sha1:19f5fb09e4f630fb5b748a497183c22a87ec5102",
  "model-00001-of-00006.safetensors=sha256:bd90149ff223e6ac1a0cac6147a5ae0df20d3a21df4f65356a1f19cd14f4aa8a",
  "model-00002-of-00006.safetensors=sha256:f75e2144d8269fe2eb4b3e0823fb089b94f176d8024153e85b8fb573a42294fa",
  "model-00003-of-00006.safetensors=sha256:f699f01ecc9691d9c6470492765fe54b8b5d2e9f277c139e89427433ffdfe0b2",
  "model-00004-of-00006.safetensors=sha256:46add1b7be098bbfdc3073884851ba3057f1b33ea23a158b650a37007dabd13d",
  "model-00005-of-00006.safetensors=sha256:1e1cb62f060a34e18f54a31a76683ef888b8cec59e73315f5b31d25d45a1f88c",
  "model-00006-of-00006.safetensors=sha256:56c73e13ae96e777ce65eee99364056069ef93b646470f352f83c5f1037b1b18",
  "special_tokens_map.json=git-sha1:c907ee1dc19b24241749b32d665c291c7e6e8e4b",
  "tokenizer.json=git-sha1:81c797f56768b22dec0301fa771f018b7e43e98c",
  "tokenizer_config.json=git-sha1:2238856624f8d39f03af53a2576c2d9b18c82f61",
]

[[models]]
id = "esm3_small"
family = "esm3"
tokenizer_source = "esmc_small"
size_category = "large"
generation_contract = "not_applicable"
official_golden = { metadata = "tests/goldens/esm3_small.json=sha256:5470e8596cbba0e2882647eccbc53c36d8b48b0f3947d1fe0bcea68da1078c32", tensors = "tests/goldens/esm3_small.safetensors=sha256:d957922f810c9ab4c557d80d5aaaf6a3aab79a5a45e4638012a634a4134803b1" }
fast_repo = "Synthyra/ESM3_small"
fast_revision = "7ddb5a740f9e5f93933eb6410c0ee8684bc63ec1"
fast_files = [
  "config.json=git-sha1:60526e2fdd8af9d4fba17f323775458ef5a1a1f9",
  "model-00001-of-00002.safetensors=sha256:a4c9b736c4c59d51180e966005a164859b47d5cd36e1f8ecdea619fbd34a0e92",
  "model-00002-of-00002.safetensors=sha256:bea60e4e91b03bb00b6cedd29b07606b8543f0869fb74454af7b26e216d80d2b",
  "special_tokens_map.json=git-sha1:c907ee1dc19b24241749b32d665c291c7e6e8e4b",
  "tokenizer.json=git-sha1:f49735e56cebab0e791aeaae777757b7fd114f71",
  "tokenizer_config.json=git-sha1:2985ed2b8aa8ecfb1d12f53f47d2b8a44cc21756",
]
official_repo = "biohub/esm3-sm-open-v1"
official_revision = "47f0545b2b6daf26a93439a3cd610f4f7f3d5478"
official_files = [
  "config.json=git-sha1:0967ef424bce6791893e9a57bb952f80fd536e93",
  "data/weights/esm3_function_decoder_v0.pth=sha256:f76d074efcaccfe21365a4fa96f212dadd66798e1e49d809ab7ffbe025d227c9",
  "data/weights/esm3_sm_open_v1.pth=sha256:5ead5a135c658068db6a4f1b933e72d6110992c4668822e1c0e2dcc53e38acd9",
  "data/weights/esm3_structure_decoder_v0.pth=sha256:3b726258a44274792b40ce7ea307e10c5da09936368a4ffa2970264d909da65b",
  "data/weights/esm3_structure_encoder_v0.pth=sha256:467acbaee703ba3ccde6e75241a912a316952e5ff071355f85c1d33c68704f40",
]

[[models]]
id = "e1_150m"
family = "e1"
size_category = "small"
generation_contract = "not_applicable"
official_golden = { metadata = "tests/goldens/e1_150m.json=sha256:701a64a6ab1a2fec5a427555b6af96232526c15cb3d5b4dc7fb253ac8f20b922", tensors = "tests/goldens/e1_150m.safetensors=sha256:6558bc8f1a7b20629eaaaa6f72601d0c2cdb859a5dc13595549b1773b6e2de41" }
fast_repo = "Synthyra/Profluent-E1-150M"
fast_revision = "7c5f3bbf697226a2e0900db7a100f9201774a907"
fast_files = [
  "config.json=git-sha1:562ef21e722ca708064fc3d54d25b731d4ac8171",
  "model.safetensors=sha256:d779ed3a4e23799aafc932dc09c9963428d10aa7075999b5f8851b39c76b67f6",
]
official_repo = "Profluent-Bio/E1-150m"
official_revision = "c4dbfe827e4aa6ed7f95eaef50dc1e084f4d77dc"
official_files = [
  "config.json=git-sha1:485e649199b46fe6ee7456bebf7aae9b3d4baeab",
  "model.safetensors=sha256:ba2656339005e6598642836acfdafde480fecc7e145ce0058eb54adf572c3484",
]

[[models]]
id = "e1_300m"
family = "e1"
size_category = "medium"
generation_contract = "not_applicable"
official_golden = { metadata = "tests/goldens/e1_300m.json=sha256:d3478f3f5957a0e0377864074dde0107de890019f96cb63548ee17ffb8f3ec3a", tensors = "tests/goldens/e1_300m.safetensors=sha256:92778b9ef95a803ddc84b3e3ca764c59e045872a94bcff0eb0cd47647732c188" }
fast_repo = "Synthyra/Profluent-E1-300M"
fast_revision = "5ef52c0ad2ae2578f40622696b763523810e8e26"
fast_files = [
  "config.json=git-sha1:f5c91498b76a3e3282a0d716d87738abb1a1b6c1",
  "model.safetensors=sha256:9271c4176a8a2e0905a0bb769570ba1c2978fb999a87da92db4cf2b041224864",
]
official_repo = "Profluent-Bio/E1-300m"
official_revision = "5a2871c587eadbcc9237bc686ea45e5b4d28dfb3"
official_files = [
  "config.json=git-sha1:918cb09e6e96d4719ed85951f38c693360f9cdb8",
  "model.safetensors=sha256:31e09a2542f45b04e6ce4adafb3b657f21e2d56d12bf68fd2266b1576a80bc9b",
]

[[models]]
id = "e1_600m"
family = "e1"
size_category = "large"
generation_contract = "not_applicable"
official_golden = { metadata = "tests/goldens/e1_600m.json=sha256:914be191c28141c1f84535cdb69ead0588a2057bb19d46c5bc7f3891a3d6739e", tensors = "tests/goldens/e1_600m.safetensors=sha256:22ed8417a4651ded255099f6d15c63c2c40552e700d2b0470d1adfde3a39c513" }
fast_repo = "Synthyra/Profluent-E1-600M"
fast_revision = "6c8bf0ec83b0e0178677c528b101efffd0677742"
fast_files = [
  "config.json=git-sha1:1d35c0b35b473259875fd29ee80167487a0d6afe",
  "model.safetensors=sha256:793483b1b3411eab73fe5214b94d1424ca0545992dfac6889cfc0186af472363",
]
official_repo = "Profluent-Bio/E1-600m"
official_revision = "52d959fb87a609d15cf223a485127b29ed5c382a"
official_files = [
  "config.json=git-sha1:8a0a439ed4201462bc01189c9f8b43523b257b5c",
  "model.safetensors=sha256:cfc108d4b98baaa62932331b40be265eae39dc382595bc3cde4a5ab55db1bf7a",
]

[[models]]
id = "dplm_150m"
family = "dplm"
size_category = "small"
generation_contract = "required"
official_golden = { metadata = "tests/goldens/dplm_150m.json=sha256:3228551fe3bed951db9ec97347143ec4462ce7c221ac240b7ce7730948c1dc1f", tensors = "tests/goldens/dplm_150m.safetensors=sha256:392992235195beed97ab8359b90a2e11e52f4326606f99a471447bed81d146bd" }
fast_repo = "Synthyra/DPLM-150M"
fast_revision = "90ba742754151a774f3b7ed580170d0a76b3e69d"
fast_files = [
  "config.json=git-sha1:117ac2c1222152ef378abaad1f605e18c4a18ab0",
  "model.safetensors=sha256:8bac5ac767ceb8deb511b272d32883f811768d56cb25e920cea94ba9b979ca14",
  "special_tokens_map.json=git-sha1:ef5f0f7d7baf4947564eafcf79972d272cd80a15",
  "tokenizer_config.json=git-sha1:80100348e3f2b8ab05b59f3352ea7631685083cd",
  "vocab.txt=git-sha1:6b946952cc35537226f07fd70957ee2f848880d2",
]
official_repo = "airkingbd/dplm_150m"
official_revision = "49b7125a5d28c6418fcc2f3c4fe799352ac1488b"
official_files = [
  "config.json=git-sha1:4910cb02f1840e9ac577026f601829604af58c74",
  "pytorch_model.bin=sha256:ea4eaa99536b60ed76f945f71a1a5e604f08447ec3def5104a93ca6001a59961",
  "special_tokens_map.json=git-sha1:ba0f9b53dbbf27934f7555e5d31e37bdea9317f1",
  "tokenizer_config.json=git-sha1:dbcdd9fb2e742627ee310713615e0d7aeed0c34e",
  "vocab.txt=git-sha1:6b946952cc35537226f07fd70957ee2f848880d2",
]

[[models]]
id = "dplm_650m"
family = "dplm"
size_category = "large"
generation_contract = "required"
official_golden = { metadata = "tests/goldens/dplm_650m.json=sha256:bf58d0ce73aaac7e6fb1923ef3d9adad67122df2a3dd414c3229488ef9587a6d", tensors = "tests/goldens/dplm_650m.safetensors=sha256:073f0a6abea7e48f28c2d921ff8329a28e22627f01979277cb324908a01b3378" }
fast_repo = "Synthyra/DPLM-650M"
fast_revision = "05dc16d97c5c028aed924c9ed681cee4ab609760"
fast_files = [
  "config.json=git-sha1:3537150eb87b213a676d5840548625e220b60e8b",
  "model.safetensors=sha256:e27a47b8ec1c078b3fccb36542210e20f0380c88828db2ca9acf3d8a25048bd8",
  "special_tokens_map.json=git-sha1:ef5f0f7d7baf4947564eafcf79972d272cd80a15",
  "tokenizer_config.json=git-sha1:80100348e3f2b8ab05b59f3352ea7631685083cd",
  "vocab.txt=git-sha1:6b946952cc35537226f07fd70957ee2f848880d2",
]
official_repo = "airkingbd/dplm_650m"
official_revision = "7a7e651baa667d094aba05e9dc1cf52a3332110a"
official_files = [
  "config.json=git-sha1:625574d625a4178ca6966e9545fee56026c0b634",
  "pytorch_model.bin=sha256:db4e54343a89e7600f41c3aacbc593db1b0caee82ec28cab25ff2ae090eba39c",
  "special_tokens_map.json=git-sha1:ba0f9b53dbbf27934f7555e5d31e37bdea9317f1",
  "tokenizer_config.json=git-sha1:dbcdd9fb2e742627ee310713615e0d7aeed0c34e",
  "vocab.txt=git-sha1:6b946952cc35537226f07fd70957ee2f848880d2",
]

[[models]]
id = "dplm_3b"
family = "dplm"
size_category = "xlarge"
generation_contract = "required"
official_golden = { metadata = "tests/goldens/dplm_3b.json=sha256:a5b6df8b9c7b371976892ec1d6c45581a32ad3a6325c6c0a0b3267012848c8ed", tensors = "tests/goldens/dplm_3b.safetensors=sha256:75b0a0854fc391133920b0feaaeb8f69ab7568a88b3759627aca1556c4338c1e" }
fast_repo = "Synthyra/DPLM-3B"
fast_revision = "7d764dd3d70ecf1ac0e64693de64a0064aacac65"
fast_files = [
  "config.json=git-sha1:7f5baf9426be06760c86882948b0f4af2e681e22",
  "model-00001-of-00003.safetensors=sha256:37b54855d087ef3e7d883464ae9d5ea3127ec15a16c6323d91ad16a6b98305c9",
  "model-00002-of-00003.safetensors=sha256:042604fefb05ea8c360a48416ce7ba662a4f90b176b4baf646c5c1814c35e6e8",
  "model-00003-of-00003.safetensors=sha256:b9ae04012665163c3fc9781dd04fcd69738ac20c07e615e98fc4483fd2c4de45",
  "special_tokens_map.json=git-sha1:ef5f0f7d7baf4947564eafcf79972d272cd80a15",
  "tokenizer_config.json=git-sha1:80100348e3f2b8ab05b59f3352ea7631685083cd",
  "vocab.txt=git-sha1:6b946952cc35537226f07fd70957ee2f848880d2",
]
official_repo = "airkingbd/dplm_3b"
official_revision = "53849d4a7fe944ae0b9cf2bbc0d2cc0054795b51"
official_files = [
  "config.json=git-sha1:f6206456e8c2f22ebe1d37fce3b5d50fd8073e68",
  "pytorch_model-00001-of-00004.bin=sha256:0bcb86a115fe744ed686756db143f78851304e855e2f83cec58681c6080ced5f",
  "pytorch_model-00002-of-00004.bin=sha256:daf3324f3be949e7dd1c3c84b28da7fec5151b1890cb0904e73427266856a06f",
  "pytorch_model-00003-of-00004.bin=sha256:dbbeb7924a21059854f994931e23590b054aa000b10370a71c052c4aa36e9246",
  "pytorch_model-00004-of-00004.bin=sha256:21c01740d091487db43446489d8a893dea1fcc6f2e1c1991ece13945f7ab4e07",
  "special_tokens_map.json=git-sha1:ba0f9b53dbbf27934f7555e5d31e37bdea9317f1",
  "tokenizer_config.json=git-sha1:dbcdd9fb2e742627ee310713615e0d7aeed0c34e",
  "vocab.txt=git-sha1:6b946952cc35537226f07fd70957ee2f848880d2",
]

[[models]]
id = "dplm2_150m"
family = "dplm2"
size_category = "small"
generation_contract = "required"
official_golden = { metadata = "tests/goldens/dplm2_150m.json=sha256:d269de779ea1503de72c77e7b2e6224afc9797bd945b40c571ff6faec782e4aa", tensors = "tests/goldens/dplm2_150m.safetensors=sha256:17fc26600938ba5364b8ecb96750786d33e9f92bcd4ea4df3e12a389340748eb" }
artifact_source = "official"
canonical_state_sha256 = "82e1751f59052b8de72b082517557db47947e8d9b4ac2f11278369e6c0cbf001"
fast_repo = "Synthyra/DPLM2-150M"
fast_revision = "182745b8dc5661f898481a4fa60a7af9d53385c4"
fast_files = [
  "config.json=git-sha1:07905a2e4327d27d073cd0390f140aec2976125a",
  "model.safetensors=sha256:0a7751b3113027b1d9c966a5bda2d6ab831855de7aaa047b911731665a7c3cc6",
  "special_tokens_map.json=git-sha1:e6378d20e897b8806734e65fd3ef9cf42a17631b",
  "tokenizer_config.json=git-sha1:f2090783e3368b7323aa877e2b740e09f0862259",
  "vocab.txt=git-sha1:9706a4277a5c39dc9b4ec7b283e8eb130ceaa7f2",
]
official_repo = "airkingbd/dplm2_150m"
official_revision = "3451d984d06497f835ed49634bd68c9dfb54d730"
official_files = [
  "config.json=git-sha1:20f1e55c64fdc4d1d30f7b1df64b6167fa23dc7c",
  "pytorch_model.bin=sha256:be7f5cf9e421f59fcc437e63ce1c7391099a314a4e9a4f10b8688785fa581238",
  "special_tokens_map.json=git-sha1:eb760e9f49a55145bbe0c64922d4ec2d3de1692a",
  "tokenizer_config.json=git-sha1:fc8c21760dcff173955afb106859e5f015d4f757",
  "vocab.txt=git-sha1:e133a3abd4350ddc3fc62548e162c8df7e62cf37",
]

[[models]]
id = "dplm2_650m"
family = "dplm2"
size_category = "large"
generation_contract = "required"
official_golden = { metadata = "tests/goldens/dplm2_650m.json=sha256:d9a7548f9af657a72d441ca70f27379863724fcce8ddd3da4f672104b7bfb772", tensors = "tests/goldens/dplm2_650m.safetensors=sha256:c4e0e467c252c3ac813363d2d4b17a5e3bd99e75fad315e76d97689b4655ddac" }
artifact_source = "official"
canonical_state_sha256 = "cba76b6602d2258de9fffff953b608d93cb8ef4a9e89b0bbd27e160c81e78bb4"
fast_repo = "Synthyra/DPLM2-650M"
fast_revision = "b9d8527a9473a54954fa2764f590b9ea1b435bb2"
fast_files = [
  "config.json=git-sha1:3e079579b214d48a09db57f2c60be6a1acea5baf",
  "model.safetensors=sha256:92db08c7dbfd6c5e03fbfeaea3f36b09640ee794dcf5ea8d550527869a9f1d63",
  "special_tokens_map.json=git-sha1:e6378d20e897b8806734e65fd3ef9cf42a17631b",
  "tokenizer_config.json=git-sha1:f2090783e3368b7323aa877e2b740e09f0862259",
  "vocab.txt=git-sha1:9706a4277a5c39dc9b4ec7b283e8eb130ceaa7f2",
]
official_repo = "airkingbd/dplm2_650m"
official_revision = "0bc69b644976c6680ab7e26669854d1979e8876e"
official_files = [
  "config.json=git-sha1:4cce8d9dc212cdace0e20e89169790bcf199c158",
  "pytorch_model.bin=sha256:8d6e08cc05e4858064a714013c74cc88c9caa2cc8b12c34605a3c24bcd877cfb",
  "special_tokens_map.json=git-sha1:eb760e9f49a55145bbe0c64922d4ec2d3de1692a",
  "tokenizer_config.json=git-sha1:fc8c21760dcff173955afb106859e5f015d4f757",
  "vocab.txt=git-sha1:e133a3abd4350ddc3fc62548e162c8df7e62cf37",
]

[[models]]
id = "dplm2_3b"
family = "dplm2"
size_category = "xlarge"
# The pinned public sampler fails before generation because cls_token_id is None.
# State, tokenizer, and inference parity remain required for this checkpoint.
generation_contract = "official_unavailable"
official_golden = { metadata = "tests/goldens/dplm2_3b.json=sha256:d6e0e02af53b13cb129192f06e264758aa21c9ebf4ee82411cf67037082d2329", tensors = "tests/goldens/dplm2_3b.safetensors=sha256:838b11824d08f83bcb0c0b3268e579f3a87dbfb965370cfe5c3f8793b96b1964" }
notes = "The pinned official DPLM2-3B sampler fails before generation, so live generation equivalence cannot be established for this checkpoint. State, tokenizer, and inference parity remain required."
artifact_source = "official"
canonical_state_sha256 = "8c46ec09115dbe6cbfb91d94ab5e906369d57e27fe620a7741c6f8cb1b6ca890"
fast_repo = "Synthyra/DPLM2-3B"
fast_revision = "2a63babe8848abf5233d31bd55891dff8285fc50"
fast_files = [
  "config.json=git-sha1:5932b1d501fed28b84614e0d2c1ecc4e89f10d6e",
  "model-00001-of-00003.safetensors=sha256:2ff393f6e8df1568ce075d50de69ff4e5e9d9886e5ec47e43d6c24df23459be3",
  "model-00002-of-00003.safetensors=sha256:feb3cea852c2aa849cc30783a984a97f0d076990ade6606cda5e38bf2a5a9621",
  "model-00003-of-00003.safetensors=sha256:9be363ddb98436af20901981ffbed2f1097377424987f6c1baad27d512b62e71",
  "special_tokens_map.json=git-sha1:e6378d20e897b8806734e65fd3ef9cf42a17631b",
  "tokenizer_config.json=git-sha1:f2090783e3368b7323aa877e2b740e09f0862259",
  "vocab.txt=git-sha1:9706a4277a5c39dc9b4ec7b283e8eb130ceaa7f2",
]
official_repo = "airkingbd/dplm2_3b"
official_revision = "9e77567926f98d1b997ea9131a8eeb035b9bf827"
official_files = [
  "config.json=git-sha1:22d51ce44cd6da8d819e0d00566987bb51d74753",
  "pytorch_model-00001-of-00004.bin=sha256:d8c641eae6bf891581ec64d543169891b093e296f5679ac75c695bcf596b4211",
  "pytorch_model-00002-of-00004.bin=sha256:6478ad86ec5fef3d1d26580493af2d8666009d3ff884f3f88548080c8bbf94b5",
  "pytorch_model-00003-of-00004.bin=sha256:dde8f88dac4a6355488c2fb433ee12cd69f1169950566624fba43684d4d99dc6",
  "pytorch_model-00004-of-00004.bin=sha256:17ec0145152bc10e4dd3b4c2edff337979f6b99ee7c7bfd6cf4e6dbd7262d079",
  "special_tokens_map.json=git-sha1:eb760e9f49a55145bbe0c64922d4ec2d3de1692a",
  "tokenizer_config.json=git-sha1:fc8c21760dcff173955afb106859e5f015d4f757",
  "vocab.txt=git-sha1:e133a3abd4350ddc3fc62548e162c8df7e62cf37",
]

[[models]]
id = "ankh_base"
family = "ankh"
size_category = "medium"
generation_contract = "required"
official_golden = { metadata = "tests/goldens/ankh_base.json=sha256:ebce8d7de821827ee995789c9b38d79252d3b2f76888130b0a8a7eedafaefe2b", tensors = "tests/goldens/ankh_base.safetensors=sha256:f0e78aa15d11749e0c64ff57f9e88c51cec6538a0adf8951f839df70cc708b65" }
notes = "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."
artifact_source = "official"
canonical_state_sha256 = "cdd8d30d88e5bf41f44e1eef4470d8e46607aba5f7c7c805b06c035b89c8c16f"
fast_repo = "Synthyra/ANKH_base"
fast_revision = "a3afa1db21c876dff57b3540fa7241e138fb1ed6"
fast_files = [
  "config.json=git-sha1:d1b81bb97129bc75dea04daef1ea2af373018e6b",
  "model-00001-of-00001.safetensors=sha256:c943d25cacdafd2c8e3518a74450b5f90f715becf30ceb24c327f1c5a0bc8b5d",
  "model.safetensors.index.json=git-sha1:ca251ab9277c06081b33e027f68f5bdc0808b443",
  "special_tokens_map.json=git-sha1:55b145827029ae9672e50d4bb368540daacce791",
  "tokenizer.json=git-sha1:212c5ef08819fa2463c6289ba4ef7db30e715c0a",
  "tokenizer_config.json=git-sha1:a8a872ae3441e7cc85ce19210dff1e4c5d2d7bd0",
]
official_repo = "ElnaggarLab/ankh-base"
official_revision = "d99cb6b966530dfc2ae96bc69d9255c2a07308b0"
official_files = [
  "config.json=git-sha1:abd44a36b5469e9a7cb019e4059b5ac1392d8422",
  "pytorch_model.bin=sha256:9b2a886374f0ff4a893f4e7a989deed76bb2458c8998bd5202ea8e97d92ddcc3",
  "special_tokens_map.json=git-sha1:55b145827029ae9672e50d4bb368540daacce791",
  "tokenizer.json=git-sha1:212c5ef08819fa2463c6289ba4ef7db30e715c0a",
  "tokenizer_config.json=git-sha1:a8a872ae3441e7cc85ce19210dff1e4c5d2d7bd0",
]

[[models]]
id = "ankh_large"
family = "ankh"
size_category = "large"
generation_contract = "required"
official_golden = { metadata = "tests/goldens/ankh_large.json=sha256:59492518b021de5cfaea87d672c9448c8558e99a3443ba2cc7ab544963196ecb", tensors = "tests/goldens/ankh_large.safetensors=sha256:3fb8d3ac27716d15a9ea92aeef6acf2b977bcc887d9b535000539e523673459b" }
notes = "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."
artifact_source = "official"
canonical_state_sha256 = "e498a2e9aea76ef784cbe3e596c6b3f5e9a40e209ad837f7e3207099e4d74483"
fast_repo = "Synthyra/ANKH_large"
fast_revision = "92d2403bbe3c32acaa944fbb8dc2beb5f571f008"
fast_files = [
  "config.json=git-sha1:46eef0fff286107820f8ffc523127fa981435aeb",
  "model-00001-of-00002.safetensors=sha256:79301f0b6a4fcbfd3b8bd10ca892846d79b1aad6ad06976da7380249e36f5158",
  "model-00002-of-00002.safetensors=sha256:20062a5049fcde509030024527665a75062a95d64966558dfcaa9245b441cbec",
  "model.safetensors.index.json=git-sha1:6b707ca3ce7255d241a52feeca68c0cbbe2a383f",
  "special_tokens_map.json=git-sha1:55b145827029ae9672e50d4bb368540daacce791",
  "tokenizer.json=git-sha1:212c5ef08819fa2463c6289ba4ef7db30e715c0a",
  "tokenizer_config.json=git-sha1:d7fe02ba6f2b18d9ccfa19ac129c9fdc9ec24d09",
]
official_repo = "ElnaggarLab/ankh-large"
official_revision = "74b371dbfa3ee0a05d32ae74df0c2e0b82d6b9a6"
official_files = [
  "config.json=git-sha1:1abf33e52ee3d6be67d780ec57d32ac2b27b5306",
  "pytorch_model.bin=sha256:517b6e8b279dedcb477af240b35c46bd6eb3307723eb281e60d4b2c8a87b889b",
  "special_tokens_map.json=git-sha1:55b145827029ae9672e50d4bb368540daacce791",
  "tokenizer.json=git-sha1:212c5ef08819fa2463c6289ba4ef7db30e715c0a",
  "tokenizer_config.json=git-sha1:d7fe02ba6f2b18d9ccfa19ac129c9fdc9ec24d09",
]

[[models]]
id = "ankh2_large"
family = "ankh"
size_category = "large"
generation_contract = "required"
official_golden = { metadata = "tests/goldens/ankh2_large.json=sha256:e8df38994ca1a1e0c598ace34a0b257b264937e4fdbb01bc41544985116b02a4", tensors = "tests/goldens/ankh2_large.safetensors=sha256:25fe1569f55c635fab8fa49c1d62a889a35a2a738bad921f5764a85b58fd4b5d" }
notes = "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."
artifact_source = "official"
canonical_state_sha256 = "597c4fe2fa8711f11a25317905f1d62fa92905e55fdd5c0a79614cd9c9d2bca3"
fast_repo = "Synthyra/ANKH2_large"
fast_revision = "729167c1980316ae61691338838447491926033f"
fast_files = [
  "config.json=git-sha1:dd5d59e6b74bc8afa9fd4a5bda13526c235dabb8",
  "generation_config.json=git-sha1:91f792e452403d46e170e206f9e50be5ddef9b9a",
  "model-00001-of-00002.safetensors=sha256:7c0c297f60bcf81c732cdfeae6e99e140272807eb52afd70356fc6fdfa94e5a8",
  "model-00002-of-00002.safetensors=sha256:f3d425d3e8741ccbdd925446559a9bf317c2c91e328f2eee44924423b56e3a3d",
  "model.safetensors.index.json=git-sha1:6b707ca3ce7255d241a52feeca68c0cbbe2a383f",
  "special_tokens_map.json=git-sha1:55b145827029ae9672e50d4bb368540daacce791",
  "tokenizer.json=git-sha1:212c5ef08819fa2463c6289ba4ef7db30e715c0a",
  "tokenizer_config.json=git-sha1:854e5db75dae8b1e9dd39c5bae80dae5508b3e25",
]
official_repo = "ElnaggarLab/ankh2-ext2"
official_revision = "aa9b9fa72288c47d9f618ce80c011e24b54e17a8"
official_files = [
  "config.json=git-sha1:9286bed4ecbc4f7113024919d16ec9719b0c0748",
  "generation_config.json=git-sha1:91f792e452403d46e170e206f9e50be5ddef9b9a",
  "pytorch_model.bin=sha256:2df583f28f111276ee22a7b76007f4297e9a69766d60bccd9c8d7169c06ac606",
  "special_tokens_map.json=git-sha1:55b145827029ae9672e50d4bb368540daacce791",
  "tokenizer.json=git-sha1:212c5ef08819fa2463c6289ba4ef7db30e715c0a",
  "tokenizer_config.json=git-sha1:854e5db75dae8b1e9dd39c5bae80dae5508b3e25",
]

[[models]]
id = "ankh3_large"
family = "ankh"
size_category = "large"
generation_contract = "required"
official_golden = { metadata = "tests/goldens/ankh3_large.json=sha256:2e5bb05b3baa5baa78f61fef7d2a2c669b0da5dbfaf6b50b12abd3e17253a961", tensors = "tests/goldens/ankh3_large.safetensors=sha256:e5c494ac418e0a2fe7bdad1376676d48960d58ec9e044d19bfffccb8c3288513" }
notes = "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."
artifact_source = "official"
canonical_state_sha256 = "60acb7ef86e85dc0c51fc1edf4c8e69a0480049723b6b2c95e6e9faa720c112a"
fast_repo = "Synthyra/ANKH3_large"
fast_revision = "c6d16ca2a1b3b27a27bcf3875e816a059029d264"
fast_files = [
  "config.json=git-sha1:813ffc6c319549a2c1f3503e1309c36110202d65",
  "generation_config.json=git-sha1:5767cc0cacebfd06884eb27ae1c796d3ca829fd2",
  "model-00001-of-00002.safetensors=sha256:7f1f5c5dcff4b6bc6b8464fe9a7eebdd99b0789ee8da895f42a41bdb04191654",
  "model-00002-of-00002.safetensors=sha256:c1a67cef9b76202362ff00c9d2b2dc4b5fc7acd1f22d30c8b3f2e3d2597d0f22",
  "model.safetensors.index.json=git-sha1:a20cfc1f8517ef47d12d08604dc93c064f1e6736",
  "special_tokens_map.json=git-sha1:d596919b7fa2a197edd441ec3ec4685ecacd2de4",
  "spiece.model=sha256:f2b5e1bbd110b71ca9b2878e1fcd3265610076ecc97bd696e8a745c9bacc54e0",
  "tokenizer.json=git-sha1:90f0c94b43c81496b3ca81e3ec1c092ef2dd7fca",
  "tokenizer_config.json=git-sha1:0e699eebfa778698473b4faf1e66ef363b93fb21",
]
official_repo = "ElnaggarLab/ankh3-large"
official_revision = "2be091622e8a393f0ef21735070084123c874b6e"
official_files = [
  "config.json=git-sha1:f5278f77d158cdd8a173df888e3ed365e84a80a3",
  "generation_config.json=git-sha1:5767cc0cacebfd06884eb27ae1c796d3ca829fd2",
  "pytorch_model.bin=sha256:26321a345e07a25b21c6c41b651c4db91b420892e52c0dcbc55bd7a8f510f95b",
  "special_tokens_map.json=git-sha1:d596919b7fa2a197edd441ec3ec4685ecacd2de4",
  "spiece.model=sha256:f2b5e1bbd110b71ca9b2878e1fcd3265610076ecc97bd696e8a745c9bacc54e0",
  "tokenizer.json=git-sha1:90f0c94b43c81496b3ca81e3ec1c092ef2dd7fca",
  "tokenizer_config.json=git-sha1:0e699eebfa778698473b4faf1e66ef363b93fb21",
]

[[models]]
id = "ankh3_xl"
family = "ankh"
size_category = "xlarge"
generation_contract = "required"
official_golden = { metadata = "tests/goldens/ankh3_xl.json=sha256:66bb12e033e4163be225d636108a479393228a4f5061015c8af114e766c3c486", tensors = "tests/goldens/ankh3_xl.safetensors=sha256:72d34567d0228cb6f1ee701c578ed4039fead4346e3f161a52e0e74df28dc8ae" }
notes = "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. The official PyTorch shard index is deliberately excluded: the builder verifies every declared source shard directly and writes a new canonical safetensors index."
artifact_source = "official"
canonical_state_sha256 = "dd2188e0d2ca65232135714eef6de394239734d843ddae4928c7398685d858e7"
fast_repo = "Synthyra/ANKH3_xl"
fast_revision = "d2856892e7535af2f55c2c4de043b1b272a29ed8"
fast_files = [
  "config.json=git-sha1:791460a5c0d6c03bebbac1d7eec7e35805eaf7b7",
  "generation_config.json=git-sha1:91f792e452403d46e170e206f9e50be5ddef9b9a",
  "model-00001-of-00005.safetensors=sha256:f6b841f6b800e436b08e362d04f8442fd044839b1e32ba6ac01ecb30a9d2bae5",
  "model-00002-of-00005.safetensors=sha256:ea556d511d4747ada49b9d0c24ef503774410093e53c0d003ffb9407efc2be31",
  "model-00003-of-00005.safetensors=sha256:125884f5dcb5b44435e3b76330582f31f74547b67c9b6cadf9a4d7cf38748eb7",
  "model-00004-of-00005.safetensors=sha256:e776ef6c5d6a50d4b3fcf0bbb2431de7ce813eddd2903290e54809c801ddb241",
  "model-00005-of-00005.safetensors=sha256:58ee3b065cfcccd179fdbecef9827dfb91feb33e0d8385b692e6309d56ec530e",
  "model.safetensors.index.json=git-sha1:74d149f64234c3f43eb85971f40b4a1c6d05a407",
  "special_tokens_map.json=git-sha1:d596919b7fa2a197edd441ec3ec4685ecacd2de4",
  "spiece.model=sha256:f2b5e1bbd110b71ca9b2878e1fcd3265610076ecc97bd696e8a745c9bacc54e0",
  "tokenizer.json=git-sha1:90f0c94b43c81496b3ca81e3ec1c092ef2dd7fca",
  "tokenizer_config.json=git-sha1:0e699eebfa778698473b4faf1e66ef363b93fb21",
]
official_repo = "ElnaggarLab/ankh3-xl"
official_revision = "e00113df5c95ef71df7ea3f5a73d56bd00e473a4"
official_files = [
  "config.json=git-sha1:f8997040e8913df75fd2eebe71a2a8eb750ed0d0",
  "generation_config.json=git-sha1:91f792e452403d46e170e206f9e50be5ddef9b9a",
  "pytorch_model-00001-of-00003.bin=sha256:2c9793cbee16697cd4149debe07d3a27143e280f6e970fa46042aae820fea981",
  "pytorch_model-00002-of-00003.bin=sha256:31c5a860e414513c829ae52affb0970d7cef2c0545df2d6e1338b6806ab7174b",
  "pytorch_model-00003-of-00003.bin=sha256:055a853bdd3623db95a637935aa299427e837cd8ea69fc04708b0262508bec75",
  "special_tokens_map.json=git-sha1:d596919b7fa2a197edd441ec3ec4685ecacd2de4",
  "spiece.model=sha256:f2b5e1bbd110b71ca9b2878e1fcd3265610076ecc97bd696e8a745c9bacc54e0",
  "tokenizer.json=git-sha1:90f0c94b43c81496b3ca81e3ec1c092ef2dd7fca",
  "tokenizer_config.json=git-sha1:0e699eebfa778698473b4faf1e66ef363b93fb21",
]

[[models]]
id = "boltz2"
family = "boltz2"
size_category = "structure"
generation_contract = "not_applicable"
notes = "Boltz2 is provisional in FastPLMs 1.0. Exact configuration, the declared inference-core state, feature preparation, and seeded execution remain tested, but native-environment BF16 end-to-end inference currently exceeds the fixed numerical-equivalence limits. FastPLMs therefore does not claim official inference equivalence for this checkpoint yet. Work on that numerical gap continues independently of the ESM++ and ESMFold2 release gates."
fast_repo = "Synthyra/Boltz2"
fast_revision = "3b148fc5efea109c065ec82ba8683d024de7134e"
fast_files = [
  "config.json=git-sha1:8682ccb12e177e73bc7a351ff7e3af484bfb6fac",
  "model.safetensors=sha256:5c863fd200a1613a0e311071e2ad73ab350635e3fd336e6822cf45c52cb960e5",
]
official_repo = "boltz-community/boltz-2"
official_revision = "6fdef46d763fee7fbb83ca5501ccceff43b85607"
official_files = [
  "boltz2_conf.ckpt=sha256:090e82ac8c92f5e943fa1b39e7410a44027bea7243c0bbb3caa67a77fc1428e1",
  "mols.tar=sha256:39e076d96dbec6b4e86982bbda16f3a53a2a60c9bdc17828d88f6f9a0c7d1fd7",
]

[[models]]
id = "esmfold"
family = "esmfold"
size_category = "structure"
generation_contract = "not_applicable"
official_golden = { metadata = "tests/goldens/esmfold.json=sha256:380b9a96168410717d1f698feaabb826b1606444cbdeec86c2ea06d9ffe8f186", tensors = "tests/goldens/esmfold.safetensors=sha256:873b1b325a43d8e0f35f355c8914a2a9fe611cc48763875e9e6a22e09ec9ebcb" }
fast_repo = "Synthyra/FastESMFold"
fast_revision = "b88c8cb50d19b2cf7ab4fee4b0a61f5e02da7823"
fast_files = [
  "config.json=git-sha1:18e0091dcbf6140bf68924d53c4c8917b9cd90b1",
  "model-00001-of-00003.safetensors=sha256:36fab9e5c96d409b2a34a8b4f1273acac8c07f119c32c4fcfa7d47bbcd55b83c",
  "model-00002-of-00003.safetensors=sha256:34954aaa05bc91635776ba6672946da5822626753d80db97b38c0538e9525102",
  "model-00003-of-00003.safetensors=sha256:2f1178cda0e6cff3b1e158e1acc59c83e3f4fc46e246388a5127bc56b8d9c4f2",
  "special_tokens_map.json=git-sha1:53cd95604a28eb7e23da763c8da23f5006ab2179",
  "tokenizer_config.json=git-sha1:10213f69b51b4b38876a29271b8f908e853a5800",
  "vocab.txt=git-sha1:eee0a1fc93c82568f78f086550fbd7c591cf423a",
]
official_repo = "facebook/esmfold_v1"
official_revision = "75a3841ee059df2bf4d56688166c8fb459ddd97a"
official_files = [
  "config.json=git-sha1:1232d0aee4be551021d8e70e66ed2b062df917bf",
  "pytorch_model.bin=sha256:2ee07356b125d1e3e57503c204111fd7323347fc4735d41d3caac57c2a78e116",
  "special_tokens_map.json=git-sha1:121c8d54f8ea66cdf678f48b3cb37c05b4de5c0d",
  "tokenizer_config.json=git-sha1:aad24fba9f1bad2d74ed79d414ddcd60e6b0f812",
  "vocab.txt=git-sha1:9abfdf5472c0ed970648b683b86ab131256b3e42",
]

[[models.oracle_assets]]
role = "weights"
path = "models/esmfold_3B_v1.pt"
url = "https://dl.fbaipublicfiles.com/fair-esm/models/esmfold_3B_v1.pt"
sha256 = "e9a52579027e77d2d2e0a18218e755821f395730e86624cab9413dc117f5ca62"
size = 2771653574

[[models]]
id = "esmfold2"
family = "esmfold2"
size_category = "structure"
generation_contract = "not_applicable"
msa_conditioning = true
official_golden = { metadata = "tests/goldens/esmfold2.json=sha256:f6e0ed1ec400b9a0fcc817db51774be968dc454b7a32645a07c479e42423ab20", tensors = "tests/goldens/esmfold2.safetensors=sha256:e4d6be4344c528e26b13f79a9303549e3de7e582da195c0078db3ce957fad420" }
fast_repo = "Synthyra/ESMFold2"
fast_revision = "cd5a0927cec585a778d983b99a8db23d2e9b281e"
fast_files = [
  "config.json=git-sha1:67e81ff571f393f0b630cd5a22398bd84979c030",
  "model.safetensors=sha256:138fd4350d6892b81ce6be7ff9bf5a93ae9d4d3751f46a27438a3f9f0dcefa0e",
]
official_repo = "biohub/ESMFold2"
official_revision = "1ebf0e3481a5184eb6171d40615c79e384b48796"
official_files = [
  "config.json=git-sha1:0300c084b990b2bd600efd9f538aa5de27109fea",
  "model.safetensors=sha256:138fd4350d6892b81ce6be7ff9bf5a93ae9d4d3751f46a27438a3f9f0dcefa0e",
]

[[models]]
id = "esmfold2_fast"
family = "esmfold2"
size_category = "structure"
generation_contract = "not_applicable"
msa_conditioning = false
official_golden = { metadata = "tests/goldens/esmfold2_fast.json=sha256:091b004c0b330217b59c12acd6da3d6edaf91e48d95f6d5f40fc20399cef9478", tensors = "tests/goldens/esmfold2_fast.safetensors=sha256:6e2e1cd07401538b4d9df994f82abe7a5b38a01e8d1ee26681e1216d44a81990" }
fast_repo = "Synthyra/ESMFold2-Fast"
fast_revision = "407875bfcaa42552bfcb25acd67ee1888b790170"
fast_files = [
  "config.json=git-sha1:62ccca15a416a5dcbd02cd6ce161f432c7b4de58",
  "model.safetensors=sha256:60ca19f2898188beba92944365f7b909efd9c99212f5018af75cc47cd9a6184a",
]
official_repo = "biohub/ESMFold2-Fast"
official_revision = "b28d8ace5e05e61e5bec1e6820cfd3e221819d12"
official_files = [
  "config.json=git-sha1:c0ca526090fa7f8342ee4666d56e7fe3a4b8cbb2",
  "model.safetensors=sha256:60ca19f2898188beba92944365f7b909efd9c99212f5018af75cc47cd9a6184a",
]

[[models]]
id = "esmfold2_experimental_cutoff2025"
family = "esmfold2"
size_category = "structure"
generation_contract = "not_applicable"
msa_conditioning = true
official_golden = { metadata = "tests/goldens/esmfold2_experimental_cutoff2025.json=sha256:cfd0e35b2bc468a0dc4f614d3acfa2fce004f96e9ae2433256ed095b829d55cc", tensors = "tests/goldens/esmfold2_experimental_cutoff2025.safetensors=sha256:9347466bbe803b6f5dc82e3356ca6cbbf2c2edd8765f9fd273385bda255019f6" }
fast_repo = "Synthyra/ESMFold2-Experimental-Cutoff2025"
fast_revision = "632ff4a9e68f1de78ee956a613267bdcdb5b354d"
fast_files = [
  "config.json=git-sha1:41119745d38bc5503a0212ad923e75211dec565f",
  "model.safetensors=sha256:01358c317428d38535e3db513cab177336fc0f7fab0d84002e64b7741d5181b3",
]
official_repo = "biohub/ESMFold2-Experimental-Cutoff2025"
official_revision = "56f94f5c1069ecde17512c96928850518340d287"
official_files = [
  "config.json=git-sha1:79ed0dc0f867b8f09bfa004d6f77397c2ab9b38d",
  "model.safetensors=sha256:01358c317428d38535e3db513cab177336fc0f7fab0d84002e64b7741d5181b3",
]
auto_map = { AutoConfig = "fastplms.models.esmfold2.configuration_esmfold2.ESMFold2Config", AutoModel = "fastplms.models.esmfold2.modeling_esmfold2_experimental.ESMFold2ExperimentalModel", AutoModelForSequenceClassification = "fastplms.models.esmfold2.modeling_esmfold2_classification.ESMFold2ExperimentalForSequenceClassification", AutoModelForTokenClassification = "fastplms.models.esmfold2.modeling_esmfold2_classification.ESMFold2ExperimentalForTokenClassification" }

[[models]]
id = "esmfold2_experimental_fast_cutoff2025"
family = "esmfold2"
size_category = "structure"
generation_contract = "not_applicable"
msa_conditioning = false
official_golden = { metadata = "tests/goldens/esmfold2_experimental_fast_cutoff2025.json=sha256:1d0b2da4f1579243f37ae04bd4b834b747005cd8e8e7665e00d088123c43afd9", tensors = "tests/goldens/esmfold2_experimental_fast_cutoff2025.safetensors=sha256:516e216d05d7e6bee59e77126d3e595e2bb7821929433f00c259c5d5241964bb" }
fast_repo = "Synthyra/ESMFold2-Experimental-Fast-Cutoff2025"
fast_revision = "8f022c2514a6c32692aaca078a8391d6bc6c4bac"
fast_files = [
  "config.json=git-sha1:b9d39e941050179ca51faaed58cbbd77778c1143",
  "model.safetensors=sha256:4e903b740ad6ad704ec60881bfd593e0d6c874a630ffa0f0838276e0b665088f",
]
official_repo = "biohub/ESMFold2-Experimental-Fast-Cutoff2025"
official_revision = "74b88548bf19688b8727432db0d698cb2e1d8783"
official_files = [
  "config.json=git-sha1:0333d68ddb12ed2f066741dcb801142f466c0a2c",
  "model.safetensors=sha256:4e903b740ad6ad704ec60881bfd593e0d6c874a630ffa0f0838276e0b665088f",
]
auto_map = { AutoConfig = "fastplms.models.esmfold2.configuration_esmfold2.ESMFold2Config", AutoModel = "fastplms.models.esmfold2.modeling_esmfold2_experimental.ESMFold2ExperimentalModel", AutoModelForSequenceClassification = "fastplms.models.esmfold2.modeling_esmfold2_classification.ESMFold2ExperimentalForSequenceClassification", AutoModelForTokenClassification = "fastplms.models.esmfold2.modeling_esmfold2_classification.ESMFold2ExperimentalForTokenClassification" }

[[models]]
id = "esmfold2_300"
family = "esmfold2"
size_category = "structure"
generation_contract = "not_applicable"
msa_conditioning = false
publication_status = "published"
fast_repo = "Synthyra/ESMFold2-300"
fast_revision = "a38a62ae930d157484b331c2bf4241684573adba"
fast_files = ["config.json=git-sha1:47ec20cf8b234c3b41d6f3ae1bdfe95d4eb4849e", "model.safetensors=sha256:44d6797c5efebf24753d502b40950e0874871c96ceea14f2d7f7e39cebac67fd"]
official_repo = "biohub/ESMFold2-Experimental-Fast-base300M-step1500k"
official_revision = "21531e59002c9205284715e28ee802dafb430637"
official_files = ["config.json=git-sha1:8c9a04fe22b0e5fca77bc4e2861a12c9494ef4d4", "model.safetensors=sha256:44d6797c5efebf24753d502b40950e0874871c96ceea14f2d7f7e39cebac67fd"]
notes = "Experimental Fast checkpoint with a frozen 300M ESM++ backbone, tensor-exact in BF16 with the pinned step-1500000 source, 24 folding blocks, no MSA conditioning, and no confidence head. BF16 execution uses FP32 folding parameters with CUDA autocast. FP8 is unsupported. Docker BF16 inference validation passed on the compact Protein G case."
auto_map = { AutoConfig = "fastplms.models.esmfold2.configuration_esmfold2.ESMFold2Config", AutoModel = "fastplms.models.esmfold2.modeling_esmfold2_experimental.ESMFold2ExperimentalModel", AutoModelForSequenceClassification = "fastplms.models.esmfold2.modeling_esmfold2_classification.ESMFold2ExperimentalForSequenceClassification", AutoModelForTokenClassification = "fastplms.models.esmfold2.modeling_esmfold2_classification.ESMFold2ExperimentalForTokenClassification" }
backbone_model = "esmc_small"
backbone = { repo = "biohub/ESMC-300M-1500000", revision = "56803b6378b82e16c3b24aac49d1fce4445540b7", files = ["config.json=git-sha1:7fe728a0eb3fb81b24491d6cc1de816bf7797c27", "model.safetensors=sha256:8bd6cacf9b5a92d51954b64b20407f1f9f564a7e4849b8663470784d2a8b7ed2", "tokenizer.json=git-sha1:81c797f56768b22dec0301fa771f018b7e43e98c", "tokenizer_config.json=git-sha1:f49f57b24a1c93bd544974811e8ecbd61b7fae89", "special_tokens_map.json=git-sha1:c907ee1dc19b24241749b32d665c291c7e6e8e4b"] }

[[models]]
id = "esmfold2_600"
family = "esmfold2"
size_category = "structure"
generation_contract = "not_applicable"
msa_conditioning = false
publication_status = "published"
fast_repo = "Synthyra/ESMFold2-600"
fast_revision = "71c67d0b2b73dc245ea7c3cc0d0476439a882d08"
fast_files = ["config.json=git-sha1:8e271837cbdada96c4974c8e543f84065e0f06f1", "model.safetensors=sha256:11a53c1b4700b6c62a5a464fc3ec7076160c19e8584157f88e13275116cfd602"]
official_repo = "biohub/ESMFold2-Experimental-Fast-base600M-step1500k"
official_revision = "15cf2d6648692f6c17cee1297d8a285476fffa9b"
official_files = ["config.json=git-sha1:95517e555f17a1eca4b68866c89033a1f6916a5d", "model.safetensors=sha256:11a53c1b4700b6c62a5a464fc3ec7076160c19e8584157f88e13275116cfd602"]
notes = "Experimental Fast checkpoint with a frozen 600M ESM++ backbone, tensor-exact in BF16 with the pinned step-1500000 source, 24 folding blocks, no MSA conditioning, and no confidence head. BF16 execution uses FP32 folding parameters with CUDA autocast. FP8 is unsupported. Configuration, weight identities, and artifact reload are verified; this model is not inference-validated."
auto_map = { AutoConfig = "fastplms.models.esmfold2.configuration_esmfold2.ESMFold2Config", AutoModel = "fastplms.models.esmfold2.modeling_esmfold2_experimental.ESMFold2ExperimentalModel", AutoModelForSequenceClassification = "fastplms.models.esmfold2.modeling_esmfold2_classification.ESMFold2ExperimentalForSequenceClassification", AutoModelForTokenClassification = "fastplms.models.esmfold2.modeling_esmfold2_classification.ESMFold2ExperimentalForTokenClassification" }
backbone_model = "esmc_large"
backbone = { repo = "biohub/ESMC-600M-1500000", revision = "21af9cc429af76ebda6c48074fb624db4735aaaf", files = ["config.json=git-sha1:ec29f6009b21d710f64bf1c058f3a9710833d692", "model.safetensors=sha256:d6869f5ae0f11e5dc829b195e062e87cfcc2f851a08a5edbaf5d1083ae7f76cc", "tokenizer.json=git-sha1:81c797f56768b22dec0301fa771f018b7e43e98c", "tokenizer_config.json=git-sha1:f49f57b24a1c93bd544974811e8ecbd61b7fae89", "special_tokens_map.json=git-sha1:c907ee1dc19b24241749b32d665c291c7e6e8e4b"] }