Publish TeleOCR GGUF: F16, Q8_0, Q4_K_M

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+teleocr-f16.gguf filter=lfs diff=lfs merge=lfs -text
+teleocr-q8_0.gguf filter=lfs diff=lfs merge=lfs -text
+teleocr-q4_k-m.gguf filter=lfs diff=lfs merge=lfs -text

LICENSE

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README.md

@@ -0,0 +1,68 @@
+---
+license: apache-2.0
+license_link: LICENSE
+base_model: XingChen-AGI/TeleOCR
+base_model_relation: quantized
+pipeline_tag: image-text-to-text
+library_name: gguf
+tags: [gguf, ocr, crispembed, document-parsing, teleocr, qwen2.5-vl]
+---
+# TeleOCR GGUF (CrispEmbed)
+
+GGUF conversions of [XingChen-AGI/TeleOCR](https://huggingface.co/XingChen-AGI/TeleOCR/tree/b3f6a0c084db72f971e19a779d2fd8a6bc9e3b8d)
+by XingChen-AGI / China Telecom, for [CrispEmbed](https://github.com/CrispStrobe/CrispEmbed)'s native ggml runtime.
+All credit for the model belongs to the original authors; these files only change the storage format.
+They target CrispEmbed's TeleOCR profile (`general.architecture = teleocr`, GGUF v1) and are not loadable by llama.cpp.
+
+## License and attribution
+
+- The original model is released under the **Apache License 2.0** (XingChen-AGI / China Telecom; modeling code Copyright 2025 The Qwen Team and The HuggingFace Inc. team).
+  These derived files are distributed under the same license.
+- `LICENSE`: the model card declares `license: apache-2.0` and the model's `modeling_naviocr.py` carries an Apache-2.0 header, but no LICENSE file is shipped upstream, so this is the canonical Apache-2.0 text from apache.org (SHA256 `cfc7749b96f63bd31c3c42b5c471bf756814053e847c10f3eb003417bc523d30`). Upstream ships no NOTICE file,
+  so none is included.
+- `UPSTREAM_README.md` is the unmodified upstream model card at revision `b3f6a0c084db72f971e19a779d2fd8a6bc9e3b8d`
+  (SHA256 `176c705cd92eeb9006c048e72274741e839f2bafa2bae858e64c3277264eb0db`).
+
+## Statement of changes (Apache-2.0 section 4(b))
+
+These files were modified from the original by CrispEmbed: the safetensors weights of revision
+`b3f6a0c084db72f971e19a779d2fd8a6bc9e3b8d` were **format-converted** to GGUF (tensor renaming, casting, tokenizer and
+preprocessing metadata embedded) and, where noted, **quantized**.
+No weights were trained, fine-tuned, merged or otherwise altered.
+
+## Files
+
+| File | Precision | Bytes | SHA256 |
+|---|---|---:|---|
+| `teleocr-f16.gguf` | F16 | 2,526,109,984 | `6c52a2800e313964717cf4e5d06e0392c2b5936d8adeac423d563b8a026a2780` |
+| `teleocr-q8_0.gguf` | Q8_0 | 2,113,233,248 | `9de7e45ce6c13ba03d3d2879f2a1bff9f8407e12808be588cf057cd2553f224b` |
+| `teleocr-q4_k-m.gguf` | Q4_K_M | 1,923,310,816 | `e975568f8bd069b3bc81364d10b38143a834922cdf600150e7e74084bfbce293` |
+
+- **F16**: F16 matrices, F32 norms/biases
+- **Q8_0**: decoder matrices Q8_0; vision, merger and tied embedding F16
+- **Q4_K_M**: decoder Q4_K with ffn_down/attn_v Q6_K; vision, merger and tied embedding F16
+
+## How the files were produced
+
+1. Downloaded `XingChen-AGI/TeleOCR` at the pinned revision `b3f6a0c084db72f971e19a779d2fd8a6bc9e3b8d`; the converter
+   (`models/convert-teleocr-to-gguf.py`) checks the source files, tensor names and shapes.
+2. Quantized variants were produced with CrispEmbed's own `crispembed-quantize` from the listed source GGUF.
+3. Built and uploaded by CrispEmbed commit `e830fc9961afd741d0c757ce8b2c8b44a6b3a2fa` (workflow `ocr-gguf-publish.yml`,
+   protocol `docs/teleocr-publication-v1.json`); `manifest.json` lists every recipe, input pin, receipt and file hash.
+
+## Parity status (CrispEmbed native vs. the original Python model, CPU, greedy)
+
+- The reference arms (f32) reproduce the original model's generated token IDs
+  **exactly** on all 6 frozen requests (`text` (66), `formula` (166), `table` (283), `code` (366), `layout` (768), `synthetic` (19) tokens), CrispEmbed commit
+  `9d24b621`, reference revision `b3f6a0c084db`.
+- **F16** is token-ID exact on the same 6 cases (receipt `tests/results/teleocr/hosted-precision.json`); **Q8_0** is token-ID exact on the same 6 cases (receipt `tests/results/teleocr/hosted-precision.json`); **Q4_K_M** is token-ID exact on the same 6 cases (receipt `tests/results/teleocr/hosted-precision.json`).
+- Strict intermediate numerical budgets are not met, including F32: one-ulp differences on the model's massive activations are amplified where they emerge (vision block 17); decoded IDs and text are exact. Also not verified: GPU numerical parity, other CPU/kernel combinations, mobile/WASM, aggregate OCR benchmarks beyond the six frozen cases.
+
+## Usage
+
+TeleOCR support is experimental and default-off in CrispEmbed (CPU by default):
+
+```bash
+CRISPEMBED_TELEOCR=1 crispembed --ocr-pipeline page.png \\
+  --ocr-engine teleocr --ocr-rec teleocr-f16.gguf
+```

UPSTREAM_README.md

@@ -0,0 +1,196 @@
+---
+license: apache-2.0
+tags:
+  - ocr
+  - document-parsing
+  - multimodal
+library_name: transformers
+pipeline_tag: image-text-to-text
+language:
+  - zh
+  - en
+---
+<div align="center">
+
+<h1 align="center">
+TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
+
+</h1>
+
+[![License](https://img.shields.io/badge/License-Apache--2.0-blue.svg)](LICENSE)
+[![GitHub](https://img.shields.io/badge/GitHub-Repository-black?logo=github)](https://github.com/caipeng328/TeleOCR)
+[![TeleAI](https://img.shields.io/badge/webapp_on_teleai.com-blue?logo=data:image/png;base64,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&labelColor=white)](https://www.teleai.com.cn/docparse/DocumentParsing)
+[![arXiv](https://img.shields.io/badge/TeleOCR-Technical%20Report-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2608.12898)
+</div>
+
+<div align="center">
+  <img src="https://raw.githubusercontent.com/caipeng328/TeleOCR/refs/heads/main/assets/score.png" width="800">
+</div>
+
+## 🔥 News
+- **2026/09/10** - We have renamed NaviDC-OCR to TeleOCR, and all subsequent model iterations will be developed and released under the TeleOCR version.
+- **2026/09/01** - We noticed that EMNLP 2026 is hosting the [Dr.DocBench Challenge](https://eval.ai/web/challenges/challenge-page/2717/overview), a document parsing competition. We evaluated NaviDC-OCR with its native weights, achieving better results than MinerU 2.5 Pro and PaddleOCR-VL 1.6. Detailed results are shown below **dr.docbench-challenge**. We welcome the use of NaviDC‑OCR for competitions. Going forward, we will continue to deliver competitive parsing models for the community.
+- **2026/08/29** — Thanks to Nandraj for the GGUF conversion and llama.cpp support  [🔗 NaviDC-OCR-GGUF](https://huggingface.co/nandraj/NaviDC-OCR-GGUF), and to the community for sharing their experience deploying NaviDC-OCR on [Ascend 910B!](https://zhuanlan.zhihu.com/p/2078516899093155893)
+- **2026/08/17** — NaviDC-OCR [model weights](https://huggingface.co/StarDoc-AI/NaviDC-OCR) and [technical report](https://arxiv.org/pdf/2608.12898) have been released.
+
+## 📖 Introduction
+
+**TeleOCR** is a lightweight (~1.2B parameters), open-source Vision-Language Model designed specifically for document parsing.
+
+Unlike existing methods that mainly target either digital documents or camera-captured documents, TeleOCR unifies both scenarios within a single framework.
+
+Compared with previous document parsing models, TeleOCR introduces
+
+- Multi-node Consensus Voting (MCV) for automatic pseudo-label generation
+- Geometry-aware document modeling for camera-captured documents
+- Curvature-Guided Douglas-Peucker Sampling (CGDP)
+- Image-to-image self-verification for automatic data refinement
+- Progressive four-stage training pipeline
+- Content-Structure Decoupled Learning for tables and formulas
+
+These techniques enable TeleOCR to achieve state-of-the-art performance on both digital and camera-captured document benchmarks while remaining lightweight enough for practical deployment.
+
+## 📊 Experimental Results
+
+TeleOCR  achieves state-of-the-art performance on multiple public document parsing benchmarks.
+
+### Layout Visualization of Distorted Documents
+To evaluate the model's ability to understand complex document deformations, we conduct a visual evaluation on the public dewarping datasets DocUNet and DIR300, with representative results shown in Figure. TeleOCR directly performs layout and content parsing on distorted documents without dewarping preprocessing or a dedicated rectification model, demonstrating robust parsing under complex geometric deformations.
+<div align="center">
+
+<img src="https://raw.githubusercontent.com/caipeng328/TeleOCR/refs/heads/main/assets/dir300.png" width="800" alt="Parsing evaluation on the DIR300 dataset.">
+<img src="https://raw.githubusercontent.com/caipeng328/TeleOCR/refs/heads/main/assets/docunet.png" width="800" alt="Parsing evaluation on the DocUNet dataset.">
+</div>
+
+---
+
+## Dr.DocBench Challenge  
+
+|模型|overall ↑|Text edit ↓|formula cdm ↑|Table teds ↑|order edit ↓|
+|---|---|---|---|---|---|
+|**Specialized VLMs**| | | | | |
+|TeleOCR|67.96|0.1903|0.02|64.97|0.398|
+|Mineru 2.5 pro|62.26|0.3402|0.04|67.75|0.356|
+|OvisOCR2|59.25|0.3883|0.00|61.59|0.3791|
+|PaddleOCRvl 1.6|55.11 | 0.4364 | 0.21 | 51.34 |0.412 |
+
+
+---
+
+### [OmniDocBench v1.6](https://github.com/opendatalab/OmniDocBench)
+
+| Model Type | Methods | Param | Overall ↑ | Text Edit ↓ | Formula CDM ↑ | Table TEDS ↑ | Table TEDS-S ↑ | Read Order Edit ↓ |
+|---|---|---:|---:|---:|---:|---:|---:|---:|
+| **Specialized VLMs** | **TeleOCR** | **1.2B** | **96.87** | <u>0.027</u> | 96.36 | **97.05** | **98.52** | <u>0.122</u> |
+| | OvisOCR2 | 0.8B | <u>96.58</u> |  **0.025** | **97.53** | <u>94.76</u> | <u>97.16</u> | **0.111** |
+| | PaddleOCR-VL-1.6 | 0.9B | 96.33 | 0.033 | <u>97.49</u> | <u>94.76</u> | 97.11 | 0.127 |
+| | MinerU2.5-Pro | 1.2B | 95.75 | 0.036 | 97.45 | 93.42 | 95.92 | 0.120 |
+| | GLM-OCR | 0.9B | 95.22 | 0.044 | 97.18 | 92.83 | 95.39 | 0.133 |
+| | PaddleOCR-VL-1.5 | 0.9B | 94.87 | 0.038 | 96.69 | 91.67 | 94.37 | 0.130 |
+| | HunyuanOCR-1.5 | 1B | 94.74 | 0.033 | 97.49 | 94.76 | 97.11 | 0.127 |
+| | PaddleOCR-VL | 0.9B | 94.11 | 0.040 | 95.70 | 90.65 | 93.74 | 0.135 |
+| | Youtu-Parsing | 2.5B | 93.68 | 0.044 | 93.45 | 92.02 | 95.00 | 0.116 |
+| | Logics-Parsing-v2 | 4B | 93.27 | 0.041 | 95.47 | 88.42 | 91.98 | 0.137 |
+| | FireRed-OCR | 2B | 93.20 | 0.037 | 95.27 | 88.04 | 91.06 | 0.131 |
+| | MinerU2.5 | 1.2B | 92.98 | 0.045 | 95.59 | 87.88 | 91.47 | 0.130 |
+| | OpenDoc-0.1B | 0.1B | 90.64 | 0.049 | 92.93 | 83.88 | 87.45 | 0.140 |
+| | dots.ocr | 3B | 90.50 | 0.048 | 89.12 | 87.18 | 90.58 | 0.138 |
+| | DeepSeek-OCR 2 | 3B | 90.17 | 0.050 | 91.59 | 83.89 | 87.75 | 0.144 |
+| | HunyuanOCR | 1B | 89.87 | 0.089 | 87.44 | 91.01 | 93.23 | 0.171 |
+| | Dolphin-v2 | 3B | 89.34 | 0.069 | 90.53 | 84.40 | 87.44 | 0.150 |
+| | OCRVerse | 4B | 88.44 | 0.063 | 89.14 | 82.44 | 86.27 | 0.163 |
+| | MonkeyOCR-pro-3B | 3B | 88.43 | 0.074 | 88.33 | 84.35 | 88.62 | 0.189 |
+| **General VLMs** | Ovis2.6-30B-A3B | 30B | 93.62 | 0.035 | 94.93 | 89.44 | 92.40 | 0.135 |
+| | Gemini 3 Pro | -- | 92.85 | 0.064 | 95.83 | 89.15 | 92.96 | 0.165 |
+| | Gemini 3 Flash | -- | 92.58 | 0.066 | 95.03 | 89.29 | 93.51 | 0.173 |
+| | Qwen3-VL-235B | 235B | 89.78 | 0.063 | 92.53 | 83.07 | 86.75 | 0.166 |
+| | GPT-5.2 | -- | 86.52 | 0.114 | 88.00 | 82.95 | 87.93 | 0.193 |
+| | InternVL3.5-241B | 241B | 83.61 | 0.130 | 89.52 | 74.35 | 79.78 | 0.215 |
+
+---
+
+
+### [Wild_OmniDocBench](https://github.com/VirtualLUOUCAS/Wild_OmniDocBench) 
+| Model Type          | Methods           | Param |    Overall ↑ | Text Edit ↓ | Formula CDM ↑ | Table TEDS ↑ | Table TEDS-S ↑ | Read Order Edit ↓ |
+| ------------------- | ----------------- | ----: | -----------: | ----------: | ------------: | -----------: | -------------: | ----------------: |
+| **Decoupled VLMs**  | **TeleOCR**    |  1.2B |    **88.53** |  **0.1173** |         88.26 |    **89.05** |      **92.14** |        **0.2011** |
+|                     | PaddleOCR-VL-1.6  |  0.9B |        87.36 |      0.1369 |         88.42 | <u>85.76</u> |   <u>90.14</u> |            0.2057 |
+|                     | MinerU2.5-Pro     |  1.2B |        87.33 |      0.1362 |  <u>90.15</u> |        85.46 |          90.12 |     <u>0.2013</u> |
+|                     | GLM-OCR           |  0.9B |        85.08 |      0.1514 |         89.09 |        81.31 |          85.90 |            0.2228 |
+|                     | PaddleOCR-VL-1.5  |  0.9B |        84.64 |      0.1461 |         86.72 |        81.80 |          86.52 |            0.2138 |
+| **End-to-End VLMs** | OvisOCR2          |  0.8B | <u>87.91</u> |       0.129 |     **90.37** |        85.13 |          89.11 |            0.2021 |
+|                     | dots.ocr          |    3B |        81.84 |      0.1483 |          85.0 |        75.32 |          80.20 |            0.2200 |
+|                     | HunyuanOCR-1.5    |    1B |        77.62 |      0.1979 |         85.12 |        67.54 |          70.67 |            0.2750 |
+|                     | Logics-Parsing-v2 |    4B |        77.10 |      0.4029 |          91.4 |        80.19 |          87.16 |            0.2355 |
+
+---
+
+
+### [PureDocBench](https://github.com/zhihengli-casia/puredocbench/)
+| Model Type         | Model             | Clean Overall ↑ | Clean Text ↓ | Clean Formula ↑ | Clean Table ↑ | Digital Degraded Overall ↑ | Digital Degraded Text ↓ | Digital Degraded Formula ↑ | Digital Degraded Table ↑ | Real Degraded Overall ↑ | Real Degraded Text ↓ | Real Degraded Formula ↑ | Real Degraded Table ↑ |
+| ------------------ | ----------------- | --------------: | -----------: | --------------: | ------------: | -------------------------: | ----------------------: | -------------------------: | -----------------------: | ----------------------: | -------------------: | ----------------------: | --------------------: |
+| **Decoupled VLM**  | **TeleOCR**    |       **86.90** |    **0.111** |       **81.01** |     **91.09** |               <u>77.47</u> |                   0.206 |                  **72.59** |                    80.45 |               **70.85** |         <u>0.302</u> |               **65.11** |             **77.66** |
+|                    | DotsMOCR          |           76.27 |        0.151 |           66.23 |         77.65 |                      73.16 |            <u>0.198</u> |                      64.32 |                    74.95 |                   61.73 |                0.312 |                   54.39 |                 61.97 |
+|                    | MinerU2.5-Pro     |           75.87 |        0.222 |           65.14 |         84.68 |                      71.77 |                   0.272 |                      61.79 |                    80.73 |                   62.56 |                0.375 |                   52.70 |                 72.47 |
+|                    | YouTu-Parsing     |           75.02 |        0.230 |           67.34 |         80.74 |                      69.66 |                   0.270 |                      61.44 |                    74.49 |                   60.29 |                0.360 |                   52.20 |                 64.69 |
+|                    | PaddleOCR-VL-1.5  |           73.01 |        0.266 |           63.53 |         82.12 |                      66.73 |                   0.339 |                      58.03 |                    76.07 |                   60.50 |                0.398 |                   54.00 |                 67.33 |
+|                    | GLM-OCR           |           68.65 |        0.314 |           57.89 |         79.44 |                      63.06 |                   0.383 |                      53.23 |                    74.21 |                   58.31 |                0.433 |                   50.34 |                 67.83 |
+|                    | Dolphin-v2        |           65.90 |        0.342 |           59.80 |         72.12 |                      60.24 |                   0.393 |                      52.20 |                    67.86 |                   44.92 |                0.553 |                   39.98 |                 50.04 |
+|                    | MonkeyOCR-pro-3B  |           62.23 |        0.346 |           48.46 |         72.83 |                      57.40 |                   0.397 |                      45.57 |                    66.32 |                   46.49 |                0.511 |                   38.18 |                 52.43 |
+| **End-to-End VLM** | OvisOCR2          |    <u>82.14</u> | <u>0.149</u> |    <u>71.29</u> |  <u>90.12</u> |                  **77.77** |               **0.192** |               <u>67.87</u> |                **84.71** |                   66.61 |                0.316 |                   57.64 |          <u>73.79</u> |
+|                    | FD-RL             |           78.38 |        0.193 |           68.21 |         86.22 |                      76.33 |                   0.214 |                      67.16 |             <u>83.22</u> |            <u>67.04</u> |            **0.298** |                   58.82 |                 72.08 |
+|                    | Logics-Parsing-v2 |           76.35 |        0.213 |           67.67 |         82.67 |                      73.85 |                   0.248 |                      67.33 |                    79.02 |                   67.64 |                0.304 |            <u>61.65</u> |                 71.64 |
+|                    | dots.ocr          |           72.01 |        0.248 |           61.37 |         79.51 |                      65.95 |                   0.307 |                      56.67 |                    71.86 |                   55.68 |                0.403 |                   47.70 |                 59.63 |
+|                    | Qianfan-OCR       |           57.22 |        0.370 |           49.79 |         58.83 |                      50.85 |                   0.438 |                      44.41 |                    51.96 |                   45.06 |                0.494 |                   39.08 |                 45.53 |
+| **General VLMs**   | Qwen3-VL-8B       |           72.44 |        0.261 |           65.10 |         78.35 |                      72.03 |                   0.266 |                      64.88 |                    77.82 |                   62.73 |                0.342 |                   55.55 |                 66.81 |
+|                    | Kimi K2.6         |           72.32 |        0.303 |           66.93 |         80.30 |                      69.95 |                   0.322 |                      64.69 |                    77.31 |                   68.02 |                0.335 |                   62.44 |                 75.14 |
+|                    | Gemini-3.1-Pro    |           70.04 |        0.306 |           65.63 |         75.08 |                      69.28 |                   0.322 |                      65.81 |                    74.24 |               **71.98** |                0.300 |                   68.62 |                 77.26 |
+|                    | Qwen3.5-397B-A17B |           69.12 |        0.233 |           65.26 |         65.40 |                      68.34 |                   0.244 |                      63.91 |                    65.53 |                   62.70 |                0.287 |                   60.70 |                 56.12 |
+
+---
+
+
+### [ICDAR2026 Sci-ImageMiner](https://sites.google.com/view/sci-imageminer/)
+|     # | Team            |       RMS |      TEDS |  Weighted |
+| ----: | --------------- | --------: | --------: | --------: |
+| **1** | **TeleOCR**  | **17.23** | **66.39** | **41.81** |
+|     2 | VLMinators      |     17.29 |     64.31 |     40.80 |
+|     3 | Ricoh_SRCB      |     16.23 |     61.12 |     38.67 |
+|     4 | Vassilis Sioros |     14.94 |     55.20 |     35.07 |
+|     5 | DocMiner        |     12.67 |     53.72 |     33.19 |
+|     6 | Qwen3 VL 8B     |     14.08 |     57.86 |     35.97 |
+
+If you would like to perform complete document parsing, please refer to our GitHub repository: https://github.com/caipeng328/NaviDC-OCR.
+
+## Citation
+
+```bibtex
+@article{teleocr,
+  title={TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents},
+  author={Cai, Peng and Zou, Zhaofan and Liu, Shifa and Wang, Yikun and Tang, Jiawei and Yang, Kaicheng and Tong, Meng and He, Zhongjiang and Sun, Hao},
+  journal={arXiv preprint arXiv:2608.12898},
+  year={2026}
+}
+```
+## Community Contributions
+
+Thanks to Nandraj for the GGUF conversion and llama.cpp support!  [🔗 NaviDC-OCR-GGUF](https://huggingface.co/nandraj/NaviDC-OCR-GGUF)
+
+## Acknowledgements
+
+TeleOCR is built upon
+
+- MinerU
+- Qwen2.5-VL
+- Qwen3
+- Transformers
+- PyTorch
+- FlashAttention
+
+We sincerely thank these excellent open-source projects.
+
+---
+
+## Contact
+
+If you have any questions, feel free to open an issue or contact us.	

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