Update FastPLMs files

LICENSES/dplm/SOURCE_RECORD.md

@@ -0,0 +1,23 @@
+# DPLM checkpoint license provenance
+
+FastPLMs uses the ByteDance DPLM repository at immutable revision
+`8a2e15e53416b4536f03f79ad1f6f6a9cbd5e19d` as the official source for both
+DPLM1 and DPLM2.
+
+At that revision:
+
+- the repository contains the complete [Apache License 2.0](https://github.com/bytedance/dplm/blob/8a2e15e53416b4536f03f79ad1f6f6a9cbd5e19d/LICENSE); and
+- the [official README](https://github.com/bytedance/dplm/blob/8a2e15e53416b4536f03f79ad1f6f6a9cbd5e19d/README.md#overview)
+  defines the repository release as including the pretrained weights for the
+  DPLM family, specifically DPLM1 and DPLM2, alongside training and inference
+  implementations.
+
+FastPLMs therefore records the official DPLM1 and DPLM2 checkpoint weights as
+Apache-2.0. Converted Synthyra checkpoints retain that license and include the
+verbatim upstream `LICENSE`. The deterministic conversion identifiers are
+`dplm_to_fastplms_v1` and `dplm2_to_fastplms_v1`; neither adds restrictions to
+the upstream terms.
+
+Complete publication is permitted only after the ordinary FastPLMs artifact,
+state-parity, legal-inventory, and atomic-publication checks pass. This record
+does not change the terms of third-party training data or downstream outputs.

README.md

@@ -10,7 +10,7 @@ tags:
 
 # Synthyra/DPLM-650M
 
-This checkpoint packages the FastPLMs `DPLM` implementation.
+This checkpoint contains the FastPLMs `DPLM` implementation.
 
 Accepted inputs are amino-acid sequences tokenized to masked or partially
 masked residue IDs.
@@ -30,9 +30,7 @@ Supported Transformers entry points are `AutoConfig`, `AutoModel`,
 | Attention variants | Supported: `eager`, `sdpa`, `flex_attention`, `flash_attention_3` |
 | 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
 
@@ -43,12 +41,12 @@ python -m pip install -r \
   "https://huggingface.co/Synthyra/DPLM-650M/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 FlashAttention loader dependency. FlashAttention also requires compatible CUDA hardware and BF16 execution. The Hub quick start below requires network
-access on first download. For an air-gapped run, first build the manifest-pinned
-local artifact and use the offline form shown in the example.
+This model requires Python 3.11-3.14, PyTorch 2.13, and Transformers 5.13. The artifact requirements include the FlashAttention loader dependency. FlashAttention also requires compatible CUDA hardware and BF16 execution. The Hub quick start needs network access for
+the first download. For an air-gapped run, build the manifest-pinned local
+artifact first and use the offline example.
 
 ## Quick start
 
@@ -64,23 +62,23 @@ model = AutoModel.from_pretrained(
 ```
 
 For offline validation, replace `model_id` with the manifest-built
-`dist/hub/DPLM-650M` path and pass `local_files_only=True`.
+`dist/hub/DPLM-650M` path. Pass `local_files_only=True`.
 
 ## Attention and compliance
 
 The quick start selects `sdpa` explicitly. Declared variants are `eager`, `sdpa`, `flex_attention`, `flash_attention_3`.
-An unavailable requested backend raises instead of silently switching
-implementations.
-`output_attentions=True` may use the documented, one-call eager fallback solely
-to materialize attention tensors; the configured backend remains unchanged.
+An unavailable requested backend raises. It does not silently change
+implementation.
+`output_attentions=True` can use the documented one-call eager fallback to
+materialize attention tensors. The configured backend does not change.
 
-This family declares the `compliance` tier. Release evidence binds the exact
+This family declares the `compliance` tier. Release evidence identifies the
 checkpoint, backend, dtype, hardware, inputs, and reference revision.
 
 ## Tokenization and forward inference
 
-Load the tokenizer from the same artifact as the model. Padding is represented
-explicitly by the attention mask:
+Load the tokenizer from the same artifact as the model. The attention mask
+shows padding explicitly:
 
 ```python
 import torch
@@ -105,8 +103,8 @@ print(output.last_hidden_state.shape)
 
 ## Dataset embeddings
 
-The shared embedding mixin preserves input order and biological-position
-masking. It accepts sequences, identified records, mappings, or a FASTA path:
+The shared embedding mixin keeps input order and biological-position masking.
+It accepts sequences, identified records, mappings, or a FASTA path:
 
 ```python
 pooled = model.embed_dataset(
@@ -123,13 +121,13 @@ print(residues[0].tensor.shape) # (l, d)
 ```
 
 Set `output` and `format="safetensors"` or `"sqlite"` for transactional,
-bounded-memory persistence. Resume verifies input order, model state, tokenizer
-policy, backend, dtype, and pooling configuration before appending.
+bounded-memory storage. Resume checks input order, model state, tokenizer
+policy, backend, dtype, and pooling configuration before it appends data.
 
 ## Downstream classification
 
-Both downstream AutoClasses reuse the checkpoint backbone and initialize a new,
-untrained `classifier`. Sequence labels have shape `(b,)`; residue labels have
+Both downstream AutoClasses use the checkpoint backbone and create a new,
+untrained `classifier`. Sequence labels have shape `(b,)`. Residue labels have
 shape `(b, l)` and use `-100` outside biological positions:
 
 ```python
@@ -167,7 +165,7 @@ print(token_output.logits.shape)     # (b, l, 3)
 
 ## 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"
@@ -188,17 +186,17 @@ peft_model = get_peft_model(
 )
 ```
 
-This checkpoint advertises a classification head, so the separately trained
-`classifier` is saved with the adapter.
+This checkpoint advertises a classification head. Save the separately trained
+`classifier` with the adapter.
 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.
 
 ## Test-time training
 
 TTT samples masked views of one protein and updates only injected low-rank
-adapters. Base checkpoint weights remain frozen:
+adapters. Base checkpoint weights stay frozen:
 
 ```python
 from transformers import AutoModelForMaskedLM
@@ -216,13 +214,13 @@ ttt_model.ttt_reset()
 print(metrics)
 ```
 
-Persisted adapters retain their deterministic reset state. TTT adds latency
-and memory, can worsen an output, and does not establish biological function.
+Saved adapters retain their deterministic reset state. TTT adds latency and
+memory, can worsen an output, and does not show biological function.
 
 ## Diffusion sequence generation
 
-DPLM defines the requested length from biological positions in a tokenized
-input, masks those positions, and iteratively retains confident predictions:
+DPLM gets the requested length from biological positions in a tokenized input.
+It masks these positions and retains confident predictions at each iteration:
 
 ```python
 import torch
@@ -246,20 +244,18 @@ sequence = tokenizer.decode(
 print(sequence)
 ```
 
-Omitting `max_iter` uses the official 500-step schedule. A shorter schedule
-changes the sampling process rather than providing an equivalent faster mode.
+If you omit `max_iter`, DPLM uses the official 500-step schedule. A shorter
+schedule changes the sampling process. It is not an equivalent faster mode.
 
-Plain `AutoModel` omits the optional ESM pooler because this diffusion
-checkpoint contains no trained pooler weights. Pass `add_pooling_layer=True`
-only when intentionally initializing and training that head.
+Plain `AutoModel` omits the optional ESM pooler because this diffusion checkpoint
+has no trained pooler weights. Pass `add_pooling_layer=True` only when you intend
+to initialize and train that head.
 
-DPLM1 and DPLM2 checkpoint weights are Apache-2.0. The maintained ByteDance
-[LICENSE](https://github.com/bytedance/dplm/blob/main/LICENSE) is Apache-2.0 and the
-[README](https://github.com/bytedance/dplm/blob/main/README.md#overview)
-explicitly scopes the repository release to the pretrained DPLM1 and DPLM2
-weights. FastPLMs artifacts record `weights_license_status="resolved"` and
-`redistributable=true`; complete publication is permitted only after all
-artifact, legal, parity, and atomic-publication preflights pass.
+DPLM1 and DPLM2 checkpoint weights use Apache-2.0. The ByteDance
+[LICENSE](https://github.com/bytedance/dplm/blob/main/LICENSE) uses Apache-2.0. Its [README](https://github.com/bytedance/dplm/blob/main/README.md#overview) limits the
+repository release to pretrained DPLM1 and DPLM2 weights. FastPLMs artifacts
+record `weights_license_status="resolved"` and `redistributable=true`. Complete
+publication requires all artifact, legal, parity, and atomic-publication checks.
 
 ## Runtime contract
 
@@ -279,8 +275,8 @@ artifact, legal, parity, and atomic-publication preflights pass.
 ## Release record
 
 - FastPLMs weights: `Synthyra/DPLM-650M`
-- 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: `airkingbd/dplm_650m`
 - Artifact source: `fast`
 - State transform: `dplm_to_fastplms_v1`
@@ -288,19 +284,17 @@ artifact, legal, parity, and atomic-publication preflights pass.
 - Release tiers: `check`, `compliance`, `feature`, `artifact`, `benchmark`
 - Unresolved required file identities: `0`
 
-`provenance.json` records exact file identities, conversion, source revisions,
-legal texts, schema, and attestations. A nonzero unresolved count blocks release.
+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: Apache-2.0. The Hub model-card identifier is
-`apache-2.0`. Applicable source licenses, notices, attribution,
-and conversion records are distributed with the local artifact. Review them
-before use.
+`apache-2.0`. 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 != "09e101fa439b48bd098f51549708eca52ea4cb88caac3ca6f5e76c262b461609":
+if RUNTIME_HASH != "f894c69f8a000ae80f19edfecf1bd8b306796ccb3e5c3891b3afeb3cdd6bf77d":
     raise RuntimeError("FastPLMs runtime identity differs from the bridge.")
 
 _RUNTIME_TEMPORARIES = []