2026-09-24: PP-DocLayoutV3_fp32.onnx, PP-DocLayoutV3_fp16_batchable.onnx, PP-OCRv6_medium_det.onnx, PP-OCRv6_tiny_rec_fp32.onnx, PP-OCRv6_tiny_rec_fp16.onnx, PP-OCRv6_medium_rec_fp32.onnx, PP-OCRv6_medium_rec_fp16.onnx, PP-OCRv6_small_rec_fp32.onnx, PP-OCRv6_small_rec_fp16.onnx, PP-FormulaNet_plus-M_encoder.onnx, PP-FormulaNet_plus-M_prep.onnx, PP-FormulaNet_plus-M_decoder_step.onnx, PP-FormulaNet_plus-M_tokenizer.json, PP-FormulaNet_plus-S_encoder.onnx, PP-FormulaNet_plus-S_prep.onnx, PP-FormulaNet_plus-S_decoder_step.onnx, PP-FormulaNet_plus-S_tokenizer.json, SLANet_plus_encoder.onnx, SLANet_plus_decoder.bin

LICENSE-texo

@@ -1,673 +0,0 @@
-The `formula` slot serves alephpi/Texo — the `transfer` model of
-https://github.com/alephpi/Texo (weights: https://huggingface.co/alephpi/FormulaNet,
-revision b2668efe5112082846fde4d446b9bfaab3989533; code at commit 7773a3e) —
-as the three ONNX graphs `scripts/models/export_texo.py` produces with
-optimum from the unmodified checkpoint (formula_encoder, formula_decoder,
-formula_decoder_past) plus its tokenizer (formula/1/tokenizer.json).
-Texo's code and weights are AGPL-3.0, reproduced below. No part of Texo is
-modified by this repository: the export recipe and the Triton Python model
-around the graphs (formula/1/model.py: preprocessing, greedy decode loop)
-are this project's, and the weights are never committed here
-(models/MANIFEST.toml lists their hashes and provenance).
-
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-  Each version is given a distinguishing version number.  If the
-Program specifies that a certain numbered version of the GNU Affero General
-Public License "or any later version" applies to it, you have the
-option of following the terms and conditions either of that numbered
-version or of any later version published by the Free Software
-Foundation.  If the Program does not specify a version number of the
-GNU Affero General Public License, you may choose any version ever published
-by the Free Software Foundation.
-
-  If the Program specifies that a proxy can decide which future
-versions of the GNU Affero General Public License can be used, that proxy's
-public statement of acceptance of a version permanently authorizes you
-to choose that version for the Program.
-
-  Later license versions may give you additional or different
-permissions.  However, no additional obligations are imposed on any
-author or copyright holder as a result of your choosing to follow a
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-
-  15. Disclaimer of Warranty.
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-  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
-APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
-HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
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-THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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-IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
-ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
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-  16. Limitation of Liability.
-
-  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
-WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
-THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
-GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
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-DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
-PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
-EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
-SUCH DAMAGES.
-
-  17. Interpretation of Sections 15 and 16.
-
-  If the disclaimer of warranty and limitation of liability provided
-above cannot be given local legal effect according to their terms,
-reviewing courts shall apply local law that most closely approximates
-an absolute waiver of all civil liability in connection with the
-Program, unless a warranty or assumption of liability accompanies a
-copy of the Program in return for a fee.
-
-                     END OF TERMS AND CONDITIONS
-
-            How to Apply These Terms to Your New Programs
-
-  If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
-
-  To do so, attach the following notices to the program.  It is safest
-to attach them to the start of each source file to most effectively
-state the exclusion of warranty; and each file should have at least
-the "copyright" line and a pointer to where the full notice is found.
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-    <one line to give the program's name and a brief idea of what it does.>
-    Copyright (C) <year>  <name of author>
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-    This program is free software: you can redistribute it and/or modify
-    it under the terms of the GNU Affero General Public License as published
-    by the Free Software Foundation, either version 3 of the License, or
-    (at your option) any later version.
-
-    This program is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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-    along with this program.  If not, see <https://www.gnu.org/licenses/>.
-
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-if any, to sign a "copyright disclaimer" for the program, if necessary.
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-<https://www.gnu.org/licenses/>.

MANIFEST.txt

@@ -1,23 +1,19 @@
-45bf71750b00739a41fc209f132eb104a4d6b5bb29483c9078164d8b87cf28ba  layout_fp32.onnx
-1ca9480da1b2ac3bd374fd4e0f9145808d0916246b656bd139045bd0ccbf852d  layout_fp16_batchable.onnx
-eb13b44b25bb36f89528b68720af8a61d9cf381176107f465db1757b65d086e1  ocr_det.onnx
-9ef676d6ed3c88256a2d92c640c44f25b0c40947e111b14b8be8f594091563e6  ocr_rec_tiny_fp32.onnx
-4831ab3e84fc176b8a0839da81076a550301a7468f3c3eb3e9b3cdb8e8721cad  ocr_rec_tiny_fp16.onnx
-9c09abf0957f7968c7586464b7397b84ad2387a0497a351af40e9acc71b673ba  ocr_rec_medium_fp32.onnx
-a9bfe969471bedda06bc0135938aec308798fcef795d0161cd45908df6235970  ocr_rec_medium_fp16.onnx
-5435fd747c9e0efe15a96d0b378d5bd157e9492ed8fd80edf08f30d02fa24634  ocr_rec_small_fp32.onnx
-3d994c22285b8dba95d3e92add80191640054e92c316bb04b6b303a562947743  ocr_rec_small_fp16.onnx
-e15dc1f333c680ba2b9eef5a8be713391ad9b587c074ff5120254063ea7a1244  plusm_encoder.onnx
-6fd37a4dc090b28c1a2a62516ec863786f8033dca14638400c0f7e3ce71d061f  plusm_prep.onnx
-28b622239781155ab646917ac5cc0b15d32f24cebb59a7cd7a996bd6c7fdf789  plusm_decoder_step.onnx
-2811d82701ec97c192fa256aa2b4516929373870ae660326cc5b1dc879b95ff2  plusm_tokenizer.json
-a021388d62aa43953b1dc7a3e0ea919b076aa8fdb98d8491830c6dbf2e5e4706  pluss_encoder.onnx
-c18cf4eb94c3d16cf88faad285333a5b4b30b6e1718c2b92b4564baf984fbd7e  pluss_prep.onnx
-e424f4400c921496eefc63a313b61c2167497f4a3c758cfc8dc40686ef6f5036  pluss_decoder_step.onnx
-2811d82701ec97c192fa256aa2b4516929373870ae660326cc5b1dc879b95ff2  pluss_tokenizer.json
-7e32c7e7e5b9c43dd11880749928c00a7cb22e074c378a01e1df9352c1b9f9b5  table_encoder.onnx
-f4b9f9b2d3fd5e85aebc839ebcd6bb5b27863077cc2b77c8e0a7ca01c6c0542d  table_decoder.bin
-9d0c79aa80f813688f6b59ef29e3bd71af89a8623cd9cdf8236606ad671231d4  formula_encoder.onnx
-e6b449a8398ad5c6241c0148d6487c959f232fe7c8ca1c48aaf8b949f1228df4  formula_decoder.onnx
-e15ec5bd9998b919dbf69581434dc0342534856097775487d6663c86544c8819  formula_decoder_past.onnx
-1240f9d178e1ad2a0076fe95ba62e332871c702accdd5ce3ae3ef33ffd6c3a1e  formula_tokenizer.json
+45bf71750b00739a41fc209f132eb104a4d6b5bb29483c9078164d8b87cf28ba  PP-DocLayoutV3_fp32.onnx
+1ca9480da1b2ac3bd374fd4e0f9145808d0916246b656bd139045bd0ccbf852d  PP-DocLayoutV3_fp16_batchable.onnx
+eb13b44b25bb36f89528b68720af8a61d9cf381176107f465db1757b65d086e1  PP-OCRv6_medium_det.onnx
+9ef676d6ed3c88256a2d92c640c44f25b0c40947e111b14b8be8f594091563e6  PP-OCRv6_tiny_rec_fp32.onnx
+4831ab3e84fc176b8a0839da81076a550301a7468f3c3eb3e9b3cdb8e8721cad  PP-OCRv6_tiny_rec_fp16.onnx
+9c09abf0957f7968c7586464b7397b84ad2387a0497a351af40e9acc71b673ba  PP-OCRv6_medium_rec_fp32.onnx
+a9bfe969471bedda06bc0135938aec308798fcef795d0161cd45908df6235970  PP-OCRv6_medium_rec_fp16.onnx
+5435fd747c9e0efe15a96d0b378d5bd157e9492ed8fd80edf08f30d02fa24634  PP-OCRv6_small_rec_fp32.onnx
+3d994c22285b8dba95d3e92add80191640054e92c316bb04b6b303a562947743  PP-OCRv6_small_rec_fp16.onnx
+e15dc1f333c680ba2b9eef5a8be713391ad9b587c074ff5120254063ea7a1244  PP-FormulaNet_plus-M_encoder.onnx
+6fd37a4dc090b28c1a2a62516ec863786f8033dca14638400c0f7e3ce71d061f  PP-FormulaNet_plus-M_prep.onnx
+28b622239781155ab646917ac5cc0b15d32f24cebb59a7cd7a996bd6c7fdf789  PP-FormulaNet_plus-M_decoder_step.onnx
+2811d82701ec97c192fa256aa2b4516929373870ae660326cc5b1dc879b95ff2  PP-FormulaNet_plus-M_tokenizer.json
+a021388d62aa43953b1dc7a3e0ea919b076aa8fdb98d8491830c6dbf2e5e4706  PP-FormulaNet_plus-S_encoder.onnx
+c18cf4eb94c3d16cf88faad285333a5b4b30b6e1718c2b92b4564baf984fbd7e  PP-FormulaNet_plus-S_prep.onnx
+e424f4400c921496eefc63a313b61c2167497f4a3c758cfc8dc40686ef6f5036  PP-FormulaNet_plus-S_decoder_step.onnx
+2811d82701ec97c192fa256aa2b4516929373870ae660326cc5b1dc879b95ff2  PP-FormulaNet_plus-S_tokenizer.json
+7e32c7e7e5b9c43dd11880749928c00a7cb22e074c378a01e1df9352c1b9f9b5  SLANet_plus_encoder.onnx
+f4b9f9b2d3fd5e85aebc839ebcd6bb5b27863077cc2b77c8e0a7ca01c6c0542d  SLANet_plus_decoder.bin

README.md

@@ -19,19 +19,23 @@ backend) loads, flat, one commit per deployment. The checkout's
 `models/MANIFEST.toml` pins a commit and maps each file to its place in
 the Triton model repository; `scripts/fetch-models.sh` downloads and
 verifies them against `MANIFEST.txt` here (sha256 per file). Nothing is
-trained here: the files are PaddlePaddle's own ONNX exports, scripted
-derivations of them, and Texo's optimum export.
+trained here: the files are PaddlePaddle's own ONNX exports and scripted
+derivations of them, all Apache-2.0.
+
+Each file is named `<official PaddlePaddle model name>_<part>`, so the name
+says which published checkpoint it came from.
 
 | File | What | Provenance | Licence |
 | --- | --- | --- | --- |
-| `layout_fp32.onnx` | PP-DocLayoutV3 (RT-DETR-L, 25 classes + reading order), FP32 | bit-identical copy of [`PaddlePaddle/PP-DocLayoutV3_onnx`](https://huggingface.co/PaddlePaddle/PP-DocLayoutV3_onnx) `inference.onnx` @ `46bbdf18` (sha256 `45bf7175…`) | Apache-2.0 |
-| `layout_fp16_batchable.onnx` | the same, output reshaped to `[B,300,7]` and converted to FP16 (GridSample / NMS kept FP32) for Triton batching on TensorRT | `scripts/models/batchable_layout.py` + `convert_fp16.py` over the file above (onnx 1.21, onnxconverter-common 1.16) | Apache-2.0 |
-| `ocr_det.onnx` | PP-OCRv6 text detection, medium tier | bit-identical copy of [`PaddlePaddle/PP-OCRv6_medium_det_onnx`](https://huggingface.co/PaddlePaddle/PP-OCRv6_medium_det_onnx) `inference.onnx` @ `61323801` (`eb13b44b…`) | Apache-2.0 |
-| `ocr_rec_tiny_fp32.onnx` | PP-OCRv6 text recognition, tiny tier (6906-way CTC) | bit-identical copy of [`PaddlePaddle/PP-OCRv6_tiny_rec_onnx`](https://huggingface.co/PaddlePaddle/PP-OCRv6_tiny_rec_onnx) `inference.onnx` @ `2612ab37` (`9ef676d6…`) | Apache-2.0 |
-| `ocr_rec_tiny_fp16.onnx` | the same in FP16 | `convert_fp16.py` over the file above | Apache-2.0 |
-| `table_encoder.onnx` | SLANet-Plus encoder (PP-LCNet), the official graph up to the GRU loop's feature input | `onnx.utils.extract_model` over [`PaddlePaddle/SLANet_plus_onnx`](https://huggingface.co/PaddlePaddle/SLANet_plus_onnx) `inference.onnx` @ `7dbe640e` (`7790c0c1…`) — `scripts/models/export_slanet_plus.py::extract_encoder` | Apache-2.0 |
-| `table_decoder.bin` | the GRU decoder's 16 parameter tensors as float32 (DocParser's host decoder) | `export_slanet_plus.py::dump_decoder` over the same file; byte-identical to TurboOCR's `SLANeXt_wired_decoder.bin` (`f4b9f9b2…`) | Apache-2.0 |
-| `formula_encoder.onnx`, `formula_decoder.onnx`, `formula_decoder_past.onnx`, `formula_tokenizer.json` | [Texo](https://github.com/alephpi/Texo) `transfer` (20M): HGNetV2 encoder, MBart decoder without / with KV cache, tokenizer (687 tokens) | optimum `image-to-text-with-past` export of `alephpi/FormulaNet` @ `b2668efe` (`scripts/models/export_texo.py`; torch 2.14, transformers 4.57.6, optimum 2.1.0, opset 18) | **AGPL-3.0** (`LICENSE-texo`) |
+| `PP-DocLayoutV3_fp32.onnx` | PP-DocLayoutV3 (RT-DETR-L, 25 classes + reading order), FP32 | bit-identical copy of [`PaddlePaddle/PP-DocLayoutV3_onnx`](https://huggingface.co/PaddlePaddle/PP-DocLayoutV3_onnx) `inference.onnx` @ `46bbdf18` (sha256 `45bf7175…`) | Apache-2.0 |
+| `PP-DocLayoutV3_fp16_batchable.onnx` | the same, output reshaped to `[B,300,7]` and converted to FP16 (GridSample / NMS kept FP32) for Triton batching on TensorRT | `scripts/models/batchable_layout.py` + `convert_fp16.py` over the file above (onnx 1.21, onnxconverter-common 1.16) | Apache-2.0 |
+| `PP-OCRv6_medium_det.onnx` | PP-OCRv6 text detection, medium tier | bit-identical copy of [`PaddlePaddle/PP-OCRv6_medium_det_onnx`](https://huggingface.co/PaddlePaddle/PP-OCRv6_medium_det_onnx) `inference.onnx` @ `61323801` (`eb13b44b…`) | Apache-2.0 |
+| `PP-OCRv6_tiny_rec_fp32.onnx` | PP-OCRv6 text recognition, tiny tier (6906-way CTC) | bit-identical copy of [`PaddlePaddle/PP-OCRv6_tiny_rec_onnx`](https://huggingface.co/PaddlePaddle/PP-OCRv6_tiny_rec_onnx) `inference.onnx` @ `2612ab37` (`9ef676d6…`) | Apache-2.0 |
+| `PP-OCRv6_tiny_rec_fp16.onnx` | the same in FP16 | `convert_fp16.py` over the file above | Apache-2.0 |
+| `SLANet_plus_encoder.onnx` | SLANet-Plus encoder (PP-LCNet), the official graph up to the GRU loop's feature input | `onnx.utils.extract_model` over [`PaddlePaddle/SLANet_plus_onnx`](https://huggingface.co/PaddlePaddle/SLANet_plus_onnx) `inference.onnx` @ `7dbe640e` (`7790c0c1…`) — `scripts/models/export_slanet_plus.py::extract_encoder` | Apache-2.0 |
+| `SLANet_plus_decoder.bin` | the GRU decoder's 16 parameter tensors as float32 (DocParser's host decoder) | `export_slanet_plus.py::dump_decoder` over the same file; byte-identical to TurboOCR's `SLANeXt_wired_decoder.bin` (`f4b9f9b2…`) | Apache-2.0 |
+| `PP-FormulaNet_plus-M_encoder.onnx`, `_prep.onnx`, `_decoder_step.onnx`, `_tokenizer.json` | PP-FormulaNet_plus-M split for a host decode loop: vision encoder, the cross-attention K/V computed once per crop, and one greedy step against a static KV cache | `scripts/models/export_ppformulanet.py --model plus_m` over [`PaddlePaddle/PP-FormulaNet_plus-M`](https://huggingface.co/PaddlePaddle/PP-FormulaNet_plus-M); gated at 12/12 identical tokens against PaddleX's own runtime | Apache-2.0 |
+| `PP-FormulaNet_plus-S_encoder.onnx`, `_prep.onnx`, `_decoder_step.onnx`, `_tokenizer.json` | the same split for plus-S, which decodes **three tokens per step** (`parallel_step: 3`) — the throughput tier | `scripts/models/export_ppformulanet.py --model plus_s`; same gate, 12/12 | Apache-2.0 |
 
 Contracts (inputs, normalisation, post-processing) follow each source's
 `inference.yml`; the pre/post-processing code is in the DocParser checkout
@@ -41,5 +45,7 @@ a text file in that checkout, not here. Why these models and not their
 siblings is measured in `docparser-bench/docs/BENCH-2026-09.md` (OmniDocBench
 v1.6 §8, the per-slot ablations §1–7).
 
-Redistribution: the Apache-2.0 files under their licence with this
-attribution; Texo's files under the AGPL-3.0, whose text is `LICENSE-texo`.
+Redistribution: all files are Apache-2.0, under that licence with this
+attribution. (Texo, an AGPL-3.0 checkpoint, served the formula slot's
+throughput tier until Stage 14 and was replaced by PP-FormulaNet_plus-S;
+nothing here is copyleft any more.)

formula_decoder.onnx

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e6b449a8398ad5c6241c0148d6487c959f232fe7c8ca1c48aaf8b949f1228df4
-size 25860950

formula_decoder_past.onnx

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e15ec5bd9998b919dbf69581434dc0342534856097775487d6663c86544c8819
-size 20339981

formula_encoder.onnx

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9d0c79aa80f813688f6b59ef29e3bd71af89a8623cd9cdf8236606ad671231d4
-size 54168562

formula_tokenizer.json

@@ -1,798 +0,0 @@
-{
-  "version": "1.0",
-  "truncation": null,
-  "padding": null,
-  "added_tokens": [
-    {
-      "id": 0,
-      "content": "<s>",
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-      "content": "<pad>",
-      "single_word": false,
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-      "normalized": false,
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-    },
-    {
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-      "content": "</s>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 3,
-      "content": "<unk>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    }
-  ],
-  "normalizer": null,
-  "pre_tokenizer": {
-    "type": "WhitespaceSplit"
-  },
-  "post_processor": {
-    "type": "TemplateProcessing",
-    "single": [
-      {
-        "SpecialToken": {
-          "id": "<s>",
-          "type_id": 0
-        }
-      },
-      {
-        "Sequence": {
-          "id": "A",
-          "type_id": 0
-        }
-      },
-      {
-        "SpecialToken": {
-          "id": "</s>",
-          "type_id": 0
-        }
-      }
-    ],
-    "pair": [
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-        }
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-    "special_tokens": {
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