Update FastPLMs files

LICENSES/fair-esm/SOURCE_RECORD.md

@@ -0,0 +1,8 @@
+# Meta ESM provenance
+
+FastPLMs uses `facebookresearch/esm` revision
+`2b369911bb5b4b0dda914521b9475cad1656b2ac` as the official parity oracle for
+ESM2 and ESMFold. The repository is pinned at
+`vendor/upstream/fair-esm/` and is not a production dependency or runtime image
+component. The accompanying `LICENSE` is the verbatim MIT text from that
+revision.

LICENSES/openfold/SOURCE_RECORD.md

@@ -0,0 +1,7 @@
+# OpenFold provenance
+
+FastPLMs uses `aqlaboratory/openfold` revision
+`4b41059694619831a7db195b7e0988fc4ff3a307` as an official ESMFold parity
+oracle. The repository is pinned at `vendor/upstream/openfold/` and is not a
+production dependency or runtime image component. The accompanying `LICENSE`
+is the verbatim Apache-2.0 text from that revision.

README.md

@@ -10,7 +10,7 @@ tags:
 
 # Synthyra/FastESMFold
 
-This checkpoint packages the FastPLMs `ESMFold` implementation.
+This checkpoint contains the FastPLMs `ESMFold` implementation.
 
 Accepted inputs are raw amino-acid sequences through folding helpers, or
 prepared residue tensors.
@@ -28,9 +28,7 @@ Supported Transformers entry points are `AutoConfig`, `AutoModel`.
 | Attention variants | Supported: `eager`, `sdpa`, `flex_attention` |
 | 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.
+A supported interface is not a pretrained downstream predictor. Classification heads start untrained. Compliance metadata does not show that a local build passed its release gate.
 
 ## Install and platform requirements
 
@@ -41,12 +39,12 @@ python -m pip install -r \
   "https://huggingface.co/Synthyra/FastESMFold/resolve/main/requirements.txt"
 ```
 
-The FastPLMs implementation itself is embedded in the model repository and loaded
-by Transformers through `trust_remote_code=True`.
+The FastPLMs implementation itself is embedded in the model repository.
+Transformers loads it 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 structure dependencies. The published execution contract requires a CUDA device. The current validated release target is the exact NVIDIA GH200 on Linux aarch64; Linux x86-64, CPU-only, Windows, and macOS structure runs are not current release evidence. 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.
+This model requires Python 3.11-3.14, PyTorch 2.13, and Transformers 5.13. The artifact requirements include the structure dependencies. The release contract requires a CUDA device. The current validated target is the exact NVIDIA GH200 on Linux aarch64. Linux x86-64, CPU-only, Windows, and macOS structure runs are not release evidence. 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
 
@@ -62,21 +60,21 @@ model = AutoModel.from_pretrained(
 ```
 
 For offline validation, replace `model_id` with the manifest-built
-`dist/hub/FastESMFold` path and pass `local_files_only=True`.
+`dist/hub/FastESMFold` path. Pass `local_files_only=True`.
 
 ## Attention and compliance
 
 The quick start selects `sdpa` explicitly. Declared variants are `eager`, `sdpa`, `flex_attention`. 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.
+requested backend raises. It does not silently change implementation.
+`output_attentions=True` can use the documented one-call eager fallback to
+materialize attention tensors. The configured backend does not change.
 
-This family declares the `compliance` tier. Release evidence binds the exact
+This family declares the `compliance` tier. Release evidence identifies the
 checkpoint, backend, dtype, hardware, inputs, and reference revision.
 
 ## PEFT fine-tuning
 
-Install the direct training dependencies, then attach LoRA to the loaded checkpoint:
+Install the training dependencies. Then attach LoRA to the loaded checkpoint:
 
 ```bash
 python -m pip install "datasets>=4.8,<5" "peft>=0.19,<0.20"
@@ -95,10 +93,10 @@ peft_model = get_peft_model(
 )
 ```
 
-This checkpoint has no advertised classifier. Supply the task-specific
-objective and preserve any new head through `modules_to_save`.
+This checkpoint has no advertised classifier. Supply the task objective and
+preserve any new head through `modules_to_save`.
 All FastPLMs checkpoints follow the Transformers `PreTrainedModel` contract and
-can be adapted with PEFT. The ESM2-specific shipped CLI is an example, not a
+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.
 
@@ -128,8 +126,8 @@ print(summary["plddt"], summary["ptm"])
 ```
 
 FastPLMs does not expose ProteinTTT for ESMFold. The pinned folding checkpoint
-does not contain a trained masked-language-model head for that objective, so
-`ttt()` and TTT folding requests raise explicitly.
+has no trained masked-language-model head for this objective. `ttt()` and TTT
+folding requests raise.
 
 ## Runtime contract
 
@@ -149,8 +147,8 @@ does not contain a trained masked-language-model head for that objective, so
 ## Release record
 
 - FastPLMs weights: `Synthyra/FastESMFold`
-- Runtime revision: recorded separately in the built artifact and published commit
-- Source-tree and runtime-bundle SHA-256: recorded in `provenance.json`
+- Runtime revision: recorded in the built artifact and published commit
+- Source-tree and runtime-bundle SHA-256: recorded in the source record
 - Official checkpoint: `facebook/esmfold_v1`
 - Artifact source: `fast`
 - State transform: `esmfold_meta_to_fastplms_v1`
@@ -158,19 +156,17 @@ does not contain a trained masked-language-model head for that objective, so
 - Release tiers: `check`, `compliance`, `structure`, `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.
+The source record records exact file identities, conversion, source revisions,
+legal texts, schema, and attestations. A nonzero unresolved count blocks a release.
 
 ## Validation boundary
 
-Declared tiers compare 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.
+Declared tiers compare configuration, tokenizer behavior, state, and
+representative inference with the pinned reference. Metadata does not show that
+a build passed, that a backend is faster, or that an output is biologically valid.
 
 ## License
 
 Checkpoint terms: MIT. The Hub model-card identifier is
-`mit`. Applicable source licenses, notices, attribution,
-and conversion records are distributed with the local artifact. Review them
-before use.
+`mit`. The local artifact contains applicable source
+licenses, notices, attribution, and conversion records. Review them before use.

THIRD_PARTY_NOTICES.md

@@ -46,7 +46,7 @@ explicitly defines the repository release as including pretrained DPLM1 and
 DPLM2 weights, and the same revision carries the complete
 [Apache-2.0 license](https://github.com/bytedance/dplm/blob/8a2e15e53416b4536f03f79ad1f6f6a9cbd5e19d/LICENSE).
 FastPLMs records both checkpoint families as Apache-2.0 and distributes the
-verbatim license plus `LICENSES/dplm/PROVENANCE.md`. Converted weights retain
+verbatim license plus `LICENSES/dplm/SOURCE_RECORD.md`. Converted weights retain
 those terms and remain subject to the ordinary artifact and publication gates.
 
 ## Biohub
@@ -80,7 +80,7 @@ TorchMetrics, Lightning Utilities, and NVIDIA DLLogger. Their exact versions or
 revision are pinned in `docker/constraints/esmfold.txt`; OpenFold imports them
 eagerly, and FastPLMs production code does not depend on them. DLLogger's exact
 source identity and installed-license handling are recorded in
-`LICENSES/dllogger/PROVENANCE.md`.
+`LICENSES/dllogger/SOURCE_RECORD.md`.
 
 ## ProteinTTT
 
@@ -93,7 +93,7 @@ revision-specific provenance are under `LICENSES/protein-ttt/`.
 For every supported family, `src/fastplms/models.toml` records an immutable
 official checkpoint revision, an immutable FastPLMs checkpoint revision, file
 digests, a named state transformation, and a mechanism-level conversion record.
-Generated artifacts reproduce that record in `provenance.json`. A release or
+Generated artifacts reproduce that record in `source-record.json`. A release or
 artifact build must fail when a required file identity, legal text, attribution
 notice, modified-file notice, upstream revision, or conversion record is absent
 or differs from its manifest digest.

fastplms/models.toml

@@ -88,7 +88,7 @@ license_files = ["LICENSE"]
 license_digests = ["LICENSE=sha256:cfc7749b96f63bd31c3c42b5c471bf756814053e847c10f3eb003417bc523d30"]
 distribution_files = [
   "LICENSE=sha256:cfc7749b96f63bd31c3c42b5c471bf756814053e847c10f3eb003417bc523d30",
-  "PROVENANCE.md=sha256:a659f74be9073cf1ad2d2f7071531ca56959b421f111152cf4c41184ace5970e",
+  "SOURCE_RECORD.md=sha256:a659f74be9073cf1ad2d2f7071531ca56959b421f111152cf4c41184ace5970e",
 ]
 
 [[upstreams]]
@@ -122,7 +122,7 @@ license_files = ["LICENSE"]
 license_digests = ["LICENSE=sha256:da6d3703ed11cbe42bd212c725957c98da23cbff1998c05fa4b3d976d1a58e93"]
 distribution_files = [
   "LICENSE=sha256:da6d3703ed11cbe42bd212c725957c98da23cbff1998c05fa4b3d976d1a58e93",
-  "PROVENANCE.md=sha256:950adb94daf15e646ddf226dacfe2a8e77801aa0793e439a9a3490a48eb666e7",
+  "SOURCE_RECORD.md=sha256:950adb94daf15e646ddf226dacfe2a8e77801aa0793e439a9a3490a48eb666e7",
 ]
 
 [[upstreams]]
@@ -136,7 +136,7 @@ license_digests = ["LICENSE=sha256:cfc7749b96f63bd31c3c42b5c471bf756814053e847c1
 distribution_files = [
   "LICENSE=sha256:cfc7749b96f63bd31c3c42b5c471bf756814053e847c10f3eb003417bc523d30",
   "MODIFICATIONS.md=sha256:fd6f0aa1086a0c996cf967b326d18e965660cda0ad5c7f36a3474a8490720da3",
-  "PROVENANCE.md=sha256:48c903db43a217a3126afaefbac60b7ddac7efda2dfcc0cbff0bffc7d6c30081",
+  "SOURCE_RECORD.md=sha256:48c903db43a217a3126afaefbac60b7ddac7efda2dfcc0cbff0bffc7d6c30081",
 ]
 
 [[upstreams]]
@@ -149,7 +149,7 @@ license_files = ["LICENSE"]
 license_digests = ["LICENSE=sha256:bb01e7d5554f9e2e117172e56551452f68a7818df7bc8e71cd7a776a1d4ba3df"]
 distribution_files = [
   "LICENSE=sha256:bb01e7d5554f9e2e117172e56551452f68a7818df7bc8e71cd7a776a1d4ba3df",
-  "PROVENANCE.md=sha256:dc641c37353c2efd50ccbdb316ca4aae495ec02c1563e0e15bac92f75fc482e5",
+  "SOURCE_RECORD.md=sha256:dc641c37353c2efd50ccbdb316ca4aae495ec02c1563e0e15bac92f75fc482e5",
 ]
 
 [families.esm2]
@@ -187,7 +187,8 @@ 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"]
+precisions = ["default", "fp8"]
+experimental_precisions = ["fp8"]
 vram_tier = "sequence"
 checkpoint_license = "MIT"
 hub_license = "mit"
@@ -267,7 +268,7 @@ 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/PROVENANCE.md. Limitation: redistribution remains subject to Apache-2.0 and the pinned source record; no broader rights are inferred."
+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"]
@@ -291,7 +292,7 @@ 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/PROVENANCE.md. Limitation: no head exception is permitted by this source record, and redistribution remains subject to Apache-2.0."
+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"]

modeling_fastplms.py

@@ -12,7 +12,7 @@ from zipfile import ZIP_DEFLATED, ZipFile
 
 from .fastplms_bundle import RUNTIME_DATA, RUNTIME_HASH
 
-if RUNTIME_HASH != "899b79836910097d523aa944c1342d227221c535365e90dfd15ba72dabbb1875":
+if RUNTIME_HASH != "9a0627bd8e6f68ac389ee97690f802284cffc7e7a7a537880aacd8a7ae440cc3":
     raise RuntimeError("FastPLMs runtime identity differs from the bridge.")
 
 _RUNTIME_TEMPORARIES = []