2026-09-24: PP-DocLayoutV3_fp32.onnx, PP-DocLayoutV3_fp16_batchable.onnx, PP-OCRv6_medium_det.onnx, PP-OCRv6_small_det.onnx, PP-OCRv6_tiny_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

MANIFEST.txt

@@ -1,6 +1,8 @@
 45bf71750b00739a41fc209f132eb104a4d6b5bb29483c9078164d8b87cf28ba  PP-DocLayoutV3_fp32.onnx
 1ca9480da1b2ac3bd374fd4e0f9145808d0916246b656bd139045bd0ccbf852d  PP-DocLayoutV3_fp16_batchable.onnx
 eb13b44b25bb36f89528b68720af8a61d9cf381176107f465db1757b65d086e1  PP-OCRv6_medium_det.onnx
+d73e0058b7a8086bbd57f3d10b8bcd4ff95363f67e06e2762b5e814fe9c9410e  PP-OCRv6_small_det.onnx
+193bab7a04fca699a6c82e6abb5b81bdb28177f0abd4062552b04908dafb19f8  PP-OCRv6_tiny_det.onnx
 9ef676d6ed3c88256a2d92c640c44f25b0c40947e111b14b8be8f594091563e6  PP-OCRv6_tiny_rec_fp32.onnx
 4831ab3e84fc176b8a0839da81076a550301a7468f3c3eb3e9b3cdb8e8721cad  PP-OCRv6_tiny_rec_fp16.onnx
 9c09abf0957f7968c7586464b7397b84ad2387a0497a351af40e9acc71b673ba  PP-OCRv6_medium_rec_fp32.onnx

PP-OCRv6_small_det.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d73e0058b7a8086bbd57f3d10b8bcd4ff95363f67e06e2762b5e814fe9c9410e
+size 9880512

PP-OCRv6_tiny_det.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:193bab7a04fca699a6c82e6abb5b81bdb28177f0abd4062552b04908dafb19f8
+size 1780590

README.md

@@ -29,13 +29,19 @@ says which published checkpoint it came from.
 | --- | --- | --- | --- |
 | `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_medium_det.onnx` | PP-OCRv6 text detection, medium tier (`OCR_DET_MODEL=medium`) | 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_small_det.onnx` | PP-OCRv6 text detection, small tier — **the tier served by default** (`OCR_DET_MODEL`) | bit-identical copy of [`PaddlePaddle/PP-OCRv6_small_det_onnx`](https://huggingface.co/PaddlePaddle/PP-OCRv6_small_det_onnx) `inference.onnx` @ `28fe5895` (`d73e0058…`) | Apache-2.0 |
+| `PP-OCRv6_tiny_det.onnx` | PP-OCRv6 text detection, tiny tier (0.43 M parameters) | bit-identical copy of [`PaddlePaddle/PP-OCRv6_tiny_det_onnx`](https://huggingface.co/PaddlePaddle/PP-OCRv6_tiny_det_onnx) `inference.onnx` @ `2ba1506c` (`193bab7a…`) | 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 |
+| `PP-OCRv6_small_rec_fp32.onnx` | PP-OCRv6 text recognition, small tier (18710-way CTC) — **the tier served by default** (`OCR_REC_MODEL`) | bit-identical copy of [`PaddlePaddle/PP-OCRv6_small_rec_onnx`](https://huggingface.co/PaddlePaddle/PP-OCRv6_small_rec_onnx) `inference.onnx` @ `b8f84f0b` (`5435fd74…`) | Apache-2.0 |
+| `PP-OCRv6_small_rec_fp16.onnx` | the same in FP16 | `convert_fp16.py` over the file above | Apache-2.0 |
+| `PP-OCRv6_medium_rec_fp32.onnx` | PP-OCRv6 text recognition, medium tier (18710-way CTC, the same dictionary as small) | bit-identical copy of [`PaddlePaddle/PP-OCRv6_medium_rec_onnx`](https://huggingface.co/PaddlePaddle/PP-OCRv6_medium_rec_onnx) `inference.onnx` @ `50c7eaca` (`9c09abf0…`) | Apache-2.0 |
+| `PP-OCRv6_medium_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 |
+| `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 `slanet_plus_decoder.bin` (`f4b9f9b2…`), and 16 of 16 tensors bit-equal to the named `head.` subtree of `SLANet_plus_pretrained.pdparams` (`--verify-decoder`) | 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) — the encoder cut from the paddle2onnx graph, the decoder's 165 tensors read by name from the official training checkpoint (`PP-FormulaNet_plus-M_pretrained.pdparams`, 165 of 165 bit-equal to the conversion's own numbering); 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`; decoder weights by name likewise (61 of 61); 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