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LICENSE.txt

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+Tencent is pleased to support the open source community by making POINTS-Reader available.
+
+Copyright (C) 2025 Tencent. All rights reserved. 
+
+POINTS-Reader is licensed under the POINTS-Reader License, except for the third-party components listed in the NOTICE file, which are licensed under their respective terms. Those third-party components remain solely subject to their original licenses, with no additional limitations imposed by us. Users must comply with all terms and conditions of original licenses of those third-party components and must ensure that the usage of the third party components adheres to all relevant laws and regulations. You may refer to the NOTICE file for further information.
+
+For the avoidance of doubt, POINTS-Reader refers only to the code, parameters, and weights as made publicly available by Tencent under the POINTS-Reader License.
+
+Terms of the POINTS-Reader License:
+--------------------------------------------------------------------
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
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+- You agree to use the POINTS-Reader only for research or evaluation purposes, and shall not use it for any commercial or production purposes under any circumstances.
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NOTICE.txt

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+Tencent is pleased to support the open source community by making POINTS-Reader available. 
+Copyright (C) 2025 Tencent. All rights reserved. 
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+The software and/or models listed below in this distribution may have been modified by Tencent ("Tencent Modifications"). All Tencent Modifications are Copyright (C) Tencent.
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+POINTS-Reader was built on Qwen2.5-3B-Instruct and fine-tuned by Tencent.
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+

README.md

@@ -0,0 +1,672 @@
+<p align="center">
+    <img src="images/logo.png" width="700"/>
+<p>
+
+<h1 align="center">
+POINTS-Reader: Distillation-Free Adaptation of Vision-Language Models for Document Conversion
+</h1>
+
+<p align="center">
+  <a href="https://huggingface.co/tencent/POINTS-Reader">
+    <img src="https://img.shields.io/badge/HuggingFace%20Weights-black.svg?logo=HuggingFace" alt="HuggingFace">
+  </a>
+  <a href="">
+    <img src="https://img.shields.io/badge/arXiv__-POINTS--Reader-d4333f?logo=arxiv&logoColor=white&colorA=cccccc&colorB=d4333f&style=flat" alt="arXiv">
+  </a>
+  <a href="">
+    <img src="https://komarev.com/ghpvc/?username=tencent&repo=POINTS-Reader&color=brightgreen&label=Views" alt="view">
+  </a>
+</p>
+
+We are delighted to announce that the WePOINTS family has welcomed a new member: POINTS-Reader, a vision-language model for end-to-end document conversion.
+
+## News
+
+- 2025.08.26: We released the weights of the most recent version of POINT-Reader🔥🔥🔥.
+- 2025.08.21: POINTS-Reader is accepted by **EMNLP 2025** for presentation at the **Main Conference**🎉🎉🎉.
+
+## Introduction
+
+1. **Simplicity**: POINTS-Reader is a very streamlined model that fully follows the structure of POINTS1.5, except that we have replaced Qwen2.5-7B-Instruct with Qwen2.5-3B-Instruct. Moreover, the input and output of POINTS-Reader are extremely straightforward. The input consists of a fixed prompt and a document image, and the output contains only a string (text extracted from the document image). The model's output is the final result delivered to the user without any post-processing.
+
+2. **Performance**: Currently, POINTS-Reader supports extraction from both Chinese and English documents, achieving impressive results, with scores of 0.133 for English and 0.212 for Chinese on OmniDocBench.
+
+3. **High Throughput**: With current mainstream inference frameworks, such as SGLang and vLLM, optimization is predominantly focused on LLMs. Thus, a large ViT would significantly impact the model’s throughput, which is why we selected a ViT with a moderate number of parameters (600M NaViT used in POINTS1.5). Combined with our support for SGLang, we currently achieve a very satisfactory throughput. We will also provide support for vLLM in the future.
+
+4. **Open-source Technical Approach**: In the POINTS-Reader paper, we propose a two-stage data augmentation strategy. The first stage leverages automated data to endow the model with basic document extraction capabilities. In the subsequent stage, continuous self-evolution improves the quality of data generated by the model. The self-evolution approach in the second stage is highly extensible and can be applied to virtually any model.
+
+## Results
+
+We take the following results from [OmniDocBench](https://github.com/opendatalab/OmniDocBench/tree/main) and POINTS-Reader for comparison:
+
+<table style="width: 92%; margin: auto; border-collapse: collapse;">
+<thead>
+<tr>
+<th rowspan="2">Method Type</th>
+<th rowspan="2">Methods</th>
+<th colspan="2">Overall<sup>Edit</sup>↓</th>
+<th colspan="2">Text<sup>Edit</sup>↓</th>
+<th colspan="2">Formula<sup>Edit</sup>↓</th>
+<th colspan="2">Formula<sup>CDM</sup>↑</th>
+<th colspan="2">Table<sup>TEDS</sup>↑</th>
+<th colspan="2">Table<sup>Edit</sup>↓</th>
+<th colspan="2">Read Order<sup>Edit</sup>↓</th>
+</tr>
+<tr>
+<th>EN</th>
+<th>ZH</th>
+<th>EN</th>
+<th>ZH</th>
+<th>EN</th>
+<th>ZH</th>
+<th>EN</th>
+<th>ZH</th>
+<th>EN</th>
+<th>ZH</th>
+<th>EN</th>
+<th>ZH</th>
+<th>EN</th>
+<th>ZH</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td rowspan="9">Pipeline Tools</td>
+<td>MinerU-pipeline-2.1.1</td>
+<td>0.162</td>
+<td>0.244</td>
+<td>0.072</td>
+<td>0.111</td>
+<td>0.313</td>
+<td>0.581</td>
+<td>79.2</td>
+<td>48.8</td>
+<td>77.4</td>
+<td>79.5</td>
+<td>0.166</td>
+<td>0.15</td>
+<td>0.097</td>
+<td>0.136</td>
+</tr>
+<tr>
+<td>Marker-1.2.3</td>
+<td>0.336</td>
+<td>0.556</td>
+<td>0.08</td>
+<td>0.315</td>
+<td>0.53</td>
+<td>0.883</td>
+<td>17.6</td>
+<td>11.7</td>
+<td>67.6</td>
+<td>49.2</td>
+<td>0.619</td>
+<td>0.685</td>
+<td>0.114</td>
+<td>0.34</td>
+</tr>
+<tr>
+<td>Marker-1.7.1</td>
+<td>0.296</td>
+<td>0.497</td>
+<td>0.085</td>
+<td>0.293</td>
+<td>0.374</td>
+<td>0.688</td>
+<td>79.0</td>
+<td>36.7</td>
+<td>67.6</td>
+<td>54.0</td>
+<td>0.609</td>
+<td>0.678</td>
+<td>0.116</td>
+<td>0.329</td>
+</tr>
+<tr>
+<td>PaddleOCR PP-StructureV3</td>
+<td>0.145</td>
+<td>0.206</td>
+<td>0.058</td>
+<td>0.088</td>
+<td>0.295</td>
+<td>0.535</td>
+<td>81.8</td>
+<td>52.1</td>
+<td>77.2</td>
+<td>83.9</td>
+<td>0.159</td>
+<td>0.109</td>
+<td>0.069</td>
+<td>0.091</td>
+</tr>
+<tr>
+<td>Mathpix</td>
+<td>0.191</td>
+<td>0.364</td>
+<td>0.105</td>
+<td>0.381</td>
+<td>0.306</td>
+<td>0.454</td>
+<td>82.7</td>
+<td>64.6</td>
+<td>77.0</td>
+<td>67.1</td>
+<td>0.243</td>
+<td>0.32</td>
+<td>0.108</td>
+<td>0.304</td>
+</tr>
+<tr>
+<td>Docling-2.14.0</td>
+<td>0.589</td>
+<td>0.909</td>
+<td>0.416</td>
+<td>0.987</td>
+<td>0.999</td>
+<td>1</td>
+<td>-</td>
+<td>-</td>
+<td>61.3</td>
+<td>25.0</td>
+<td>0.627</td>
+<td>0.810</td>
+<td>0.313</td>
+<td>0.837</td>
+</tr>
+<tr>
+<td>Pix2Text-1.1.2.3</td>
+<td>0.32</td>
+<td>0.528</td>
+<td>0.138</td>
+<td>0.356</td>
+<td>0.276</td>
+<td>0.611</td>
+<td>78.4</td>
+<td>39.6</td>
+<td>73.6</td>
+<td>66.2</td>
+<td>0.584</td>
+<td>0.645</td>
+<td>0.281</td>
+<td>0.499</td>
+</tr>
+<tr>
+<td>Unstructured-0.17.2</td>
+<td>0.586</td>
+<td>0.716</td>
+<td>0.198</td>
+<td>0.481</td>
+<td>0.999</td>
+<td>1</td>
+<td>-</td>
+<td>-</td>
+<td>0</td>
+<td>0.064</td>
+<td>1</td>
+<td>0.998</td>
+<td>0.145</td>
+<td>0.387</td>
+</tr>
+<tr>
+<td>OpenParse-0.7.0</td>
+<td>0.646</td>
+<td>0.814</td>
+<td>0.681</td>
+<td>0.974</td>
+<td>0.996</td>
+<td>1</td>
+<td>0.106</td>
+<td>0</td>
+<td>64.8</td>
+<td>27.5</td>
+<td>0.284</td>
+<td>0.639</td>
+<td>0.595</td>
+<td>0.641</td>
+</tr>
+<tr>
+<td rowspan="10">Expert VLMs</td>
+<td>POINTS-Reader-3B</td>
+<td>0.133</td>
+<td>0.212</td>
+<td>0.062</td>
+<td>0.139</td>
+<td>0.304</td>
+<td>0.465</td>
+<td>-</td>
+<td>-</td>
+<td>83.7</td>
+<td>85.0</td>
+<td>0.128</td>
+<td>0.136</td>
+<td>0.036</td>
+<td>0.106</td>
+</tr>
+<tr>
+<td>MinerU2.0-2505-0.9B</td>
+<td>0.133</td>
+<td>0.238</td>
+<td>0.045</td>
+<td>0.115</td>
+<td>0.273</td>
+<td>0.506</td>
+<td>79.0</td>
+<td>50.8</td>
+<td>82.1</td>
+<td>83.4</td>
+<td>0.15</td>
+<td>0.209</td>
+<td>0.066</td>
+<td>0.122</td>
+</tr>
+<tr>
+<td>MonkeyOCR-pro-1.2B</td>
+<td>0.146</td>
+<td>0.221</td>
+<td>0.068</td>
+<td>0.118</td>
+<td>0.272</td>
+<td>0.452</td>
+<td>76.7</td>
+<td>63.3</td>
+<td>81.3</td>
+<td>85.5</td>
+<td>0.149</td>
+<td>0.134</td>
+<td>0.093</td>
+<td>0.179</td>
+</tr>
+<tr>
+<td>Dolphin</td>
+<td>0.356</td>
+<td>0.440</td>
+<td>0.352</td>
+<td>0.440</td>
+<td>0.465</td>
+<td>0.604</td>
+<td>61.6</td>
+<td>40.4</td>
+<td>70.2</td>
+<td>56.8</td>
+<td>0.258</td>
+<td>0.367</td>
+<td>0.35</td>
+<td>0.351</td>
+</tr>
+<tr>
+<td>Nanonets-OCR-s</td>
+<td>0.283</td>
+<td>0.295</td>
+<td>0.134</td>
+<td>0.231</td>
+<td>0.518</td>
+<td>0.546</td>
+<td>63.2</td>
+<td>52.0</td>
+<td>76.8</td>
+<td>79.4</td>
+<td>0.343</td>
+<td>0.201</td>
+<td>0.135</td>
+<td>0.2</td>
+</tr>
+<tr>
+<td>OCRFlux-3B</td>
+<td>0.238</td>
+<td>0.349</td>
+<td>0.112</td>
+<td>0.256</td>
+<td>0.447</td>
+<td>0.716</td>
+<td>60.2</td>
+<td>31.9</td>
+<td>69.0</td>
+<td>80.0</td>
+<td>0.269</td>
+<td>0.162</td>
+<td>0.126</td>
+<td>0.263</td>
+</tr>
+<tr>
+<td>GOT-OCR</td>
+<td>0.287</td>
+<td>0.411</td>
+<td>0.189</td>
+<td>0.315</td>
+<td>0.360</td>
+<td>0.528</td>
+<td>74.3</td>
+<td>45.3</td>
+<td>53.2</td>
+<td>47.2</td>
+<td>0.459</td>
+<td>0.52</td>
+<td>0.141</td>
+<td>0.28</td>
+</tr>
+<tr>
+<td>Nougat</td>
+<td>0.452</td>
+<td>0.973</td>
+<td>0.365</td>
+<td>0.998</td>
+<td>0.488</td>
+<td>0.941</td>
+<td>15.1</td>
+<td>16.8</td>
+<td>39.9</td>
+<td>0.0</td>
+<td>0.572</td>
+<td>1.000</td>
+<td>0.382</td>
+<td>0.954</td>
+</tr>
+<tr>
+<td>Mistral OCR</td>
+<td>0.268</td>
+<td>0.439</td>
+<td>0.072</td>
+<td>0.325</td>
+<td>0.318</td>
+<td>0.495</td>
+<td>64.6</td>
+<td>45.9</td>
+<td>75.8</td>
+<td>63.6</td>
+<td>0.6</td>
+<td>0.65</td>
+<td>0.083</td>
+<td>0.284</td>
+</tr>
+<tr>
+<td>OLMOCR-sglang</td>
+<td>0.326</td>
+<td>0.469</td>
+<td>0.097</td>
+<td>0.293</td>
+<td>0.455</td>
+<td>0.655</td>
+<td>74.3</td>
+<td>43.2</td>
+<td>68.1</td>
+<td>61.3</td>
+<td>0.608</td>
+<td>0.652</td>
+<td>0.145</td>
+<td>0.277</td>
+</tr>
+<tr>
+<td>SmolDocling-256M_transformer</td>
+<td>0.493</td>
+<td>0.816</td>
+<td>0.262</td>
+<td>0.838</td>
+<td>0.753</td>
+<td>0.997</td>
+<td>32.1</td>
+<td>0.551</td>
+<td>44.9</td>
+<td>16.5</td>
+<td>0.729</td>
+<td>0.907</td>
+<td>0.227</td>
+<td>0.522</td>
+</tr>
+<tr>
+<td rowspan="9">General VLMs</td>
+<tr>
+<td>Gemini2.0-flash</td>
+<td>0.191</td>
+<td>0.264</td>
+<td>0.091</td>
+<td>0.139</td>
+<td>0.389</td>
+<td>0.584</td>
+<td>77.6</td>
+<td>43.6</td>
+<td>79.7</td>
+<td>78.9</td>
+<td>0.193</td>
+<td>0.206</td>
+<td>0.092</td>
+<td>0.128</td>
+</tr>
+<tr>
+<td>Gemini2.5-Pro</td>
+<td>0.148</td>
+<td>0.212</td>
+<td>0.055</td>
+<td>0.168</td>
+<td>0.356</td>
+<td>0.439</td>
+<td>80.0</td>
+<td>69.4</td>
+<td>85.8</td>
+<td>86.4</td>
+<td>0.13</td>
+<td>0.119</td>
+<td>0.049</td>
+<td>0.121</td>
+</tr>
+<tr>
+<td>GPT4o</td>
+<td>0.233</td>
+<td>0.399</td>
+<td>0.144</td>
+<td>0.409</td>
+<td>0.425</td>
+<td>0.606</td>
+<td>72.8</td>
+<td>42.8</td>
+<td>72.0</td>
+<td>62.9</td>
+<td>0.234</td>
+<td>0.329</td>
+<td>0.128</td>
+<td>0.251</td>
+</tr>
+<tr>
+<td>Qwen2-VL-72B</td>
+<td>0.252</td>
+<td>0.327</td>
+<td>0.096</td>
+<td>0.218</td>
+<td>0.404</td>
+<td>0.487</td>
+<td>82.2</td>
+<td>61.2</td>
+<td>76.8</td>
+<td>76.4</td>
+<td>0.387</td>
+<td>0.408</td>
+<td>0.119</td>
+<td>0.193</td>
+</tr>
+<tr>
+<td>Qwen2.5-VL-7B</td>
+<td>0.316</td>
+<td>0.399</td>
+<td>0.151</td>
+<td>0.243</td>
+<td>0.376</td>
+<td>0.5</td>
+<td>75.3</td>
+<td>57.3</td>
+<td>71.1</td>
+<td>71.3</td>
+<td>0.598</td>
+<td>0.627</td>
+<td>0.138</td>
+<td>0.226</td>
+</tr>
+
+<tr>
+<td>Qwen2.5-VL-72B</td>
+<td>0.214</td>
+<td>0.261</td>
+<td>0.092</td>
+<td>0.18</td>
+<td>0.315</td>
+<td>0.434</td>
+<td>81.4</td>
+<td>64.1</td>
+<td>81.4</td>
+<td>83.0</td>
+<td>0.341</td>
+<td>0.262</td>
+<td>0.106</td>
+<td>0.168</td>
+</tr>
+<tr>
+<td>InternVL2-76B</td>
+<td>0.44</td>
+<td>0.443</td>
+<td>0.353</td>
+<td>0.290</td>
+<td>0.543</td>
+<td>0.701</td>
+<td>67.4</td>
+<td>44.1</td>
+<td>63.0</td>
+<td>60.2</td>
+<td>0.547</td>
+<td>0.555</td>
+<td>0.317</td>
+<td>0.228</td>
+</tr>
+<tr>
+<td>InternVL3-78B</td>
+<td>0.218</td>
+<td>0.296</td>
+<td>0.117</td>
+<td>0.21</td>
+<td>0.38</td>
+<td>0.533</td>
+<td>79.2</td>
+<td>58.8</td>
+<td>69.0</td>
+<td>73.9</td>
+<td>0.279</td>
+<td>0.282</td>
+<td>0.095</td>
+<td>0.161</td>
+</tr>
+</tbody>
+</table>
+
+## Getting Started
+
+This following code snippet has been tested with following environment:
+
+```
+python==3.10.12
+torch==2.5.1
+transformers==4.46.1
+cuda==12.1
+```
+
+If you encounter environment issues, please feel free to open an issue.
+
+### Run with Transformers
+
+Before you run the following code, make sure you install the `WePOINTS` package by running:
+
+```
+git clone https://github.com/WePOINTS/WePOINTS.git
+cd WePOINTS
+pip install -e.
+```
+
+```python
+from wepoints.utils.images import Qwen2ImageProcessorForPOINTSV15
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+
+# We recommend using the following prompt to better performance,
+# since it is used throughout the training process.
+prompt = (
+    'Please extract all the text from the image with the following requirements:\n'
+    '1. Return tables in HTML format.\n'
+    '2. Return all other text in Markdown format.'
+)
+image_path = '/path/to/your/local/image'
+model_path = 'tencent/POINTS-Reader'
+model = AutoModelForCausalLM.from_pretrained(model_path,
+                                                    trust_remote_code=True,
+                                                    torch_dtype=torch.float16,
+                                                    device_map='cuda')
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+image_processor = Qwen2ImageProcessorForPOINTSV15.from_pretrained(model_path)
+content = [
+            dict(type='image', image=image_path),
+            dict(type='text', text=prompt)
+          ]
+messages = [
+        {
+            'role': 'user',
+            'content': content
+        }
+    ]
+generation_config = {
+        'max_new_tokens': 2048,
+        'repetition_penalty': 1.05,
+        'temperature': 0.7,
+        'top_p': 0.8,
+        'top_k': 20,
+        'do_sample': True
+    }
+response = model.chat(
+    messages,
+    tokenizer,
+    image_processor,
+    generation_config
+)
+print(response)
+```
+
+If you encounter issues like repeation, please try to increase the resolution of the image to allievate the problem.
+
+### Deploy with SGLang
+
+We will create a Pull Request to SGLang, please stay tuned.
+
+## Known Issues
+
+- **Complex Document Parsing**: POINTS-Reader can struggle with complex layouts (e.g., newspapers), often producing repeated or missing content.  
+- **Handwritten Document Parsing**: It also has difficulty handling handwritten inputs (e.g., receipts, notes), which can lead to recognition errors or omissions.  
+- **Multi-language Document Parsing**: POINTS-Reader currently supports only English and Chinese, limiting its effectiveness on other languages.
+
+## Citation
+
+If you use this model in your work, please cite the following paper:
+
+```
+@article{points-reader,
+  title={POINTS-Reader: Distillation-Free Adaptation of Vision-Language Models for Document Conversion},
+  author={Liu, Yuan and Zhongyin Zhao and Tian, Le and Haicheng Wang and Xubing Ye and Yangxiu You and Zilin Yu and Chuhan Wu and  Zhou, Xiao and Yu, Yang and Zhou, Jie},
+  journal={},
+  year={2025}
+}
+
+@article{liu2024points1,
+  title={POINTS1. 5: Building a Vision-Language Model towards Real World Applications},
+  author={Liu, Yuan and Tian, Le and Zhou, Xiao and Gao, Xinyu and Yu, Kavio and Yu, Yang and Zhou, Jie},
+  journal={arXiv preprint arXiv:2412.08443},
+  year={2024}
+}
+
+@article{liu2024points,
+  title={POINTS: Improving Your Vision-language Model with Affordable Strategies},
+  author={Liu, Yuan and Zhao, Zhongyin and Zhuang, Ziyuan and Tian, Le and Zhou, Xiao and Zhou, Jie},
+  journal={arXiv preprint arXiv:2409.04828},
+  year={2024}
+}
+
+@article{liu2024rethinking,
+  title={Rethinking Overlooked Aspects in Vision-Language Models},
+  author={Liu, Yuan and Tian, Le and Zhou, Xiao and Zhou, Jie},
+  journal={arXiv preprint arXiv:2405.11850},
+  year={2024}
+}
+```

config.json

@@ -0,0 +1,172 @@
+{
+    "_commit_hash": null,
+    "architectures": [
+        "POINTSV15ChatModel"
+    ],
+    "auto_map": {
+        "AutoConfig": "configuration_pointsv15_chat.POINTSV15ChatConfig",
+        "AutoModelForCausalLM": "modeling_pointsv15_chat.POINTSV15ChatModel"
+    },
+    "llm_config": {
+        "_attn_implementation_autoset": true,
+        "_name_or_path": "",
+        "add_cross_attention": false,
+        "architectures": null,
+        "attention_dropout": 0.0,
+        "bad_words_ids": null,
+        "begin_suppress_tokens": null,
+        "bos_token_id": null,
+        "chunk_size_feed_forward": 0,
+        "cross_attention_hidden_size": null,
+        "decoder_start_token_id": null,
+        "diversity_penalty": 0.0,
+        "do_sample": false,
+        "early_stopping": false,
+        "encoder_no_repeat_ngram_size": 0,
+        "eos_token_id": null,
+        "exponential_decay_length_penalty": null,
+        "finetuning_task": null,
+        "forced_bos_token_id": null,
+        "forced_eos_token_id": null,
+        "hidden_act": "silu",
+        "hidden_size": 2048,
+        "id2label": {
+            "0": "LABEL_0",
+            "1": "LABEL_1"
+        },
+        "initializer_range": 0.02,
+        "intermediate_size": 11008,
+        "is_decoder": false,
+        "is_encoder_decoder": false,
+        "label2id": {
+            "LABEL_0": 0,
+            "LABEL_1": 1
+        },
+        "length_penalty": 1.0,
+        "max_length": 20,
+        "max_position_embeddings": 32768,
+        "max_window_layers": 28,
+        "min_length": 0,
+        "model_type": "qwen2",
+        "no_repeat_ngram_size": 0,
+        "num_attention_heads": 16,
+        "num_beam_groups": 1,
+        "num_beams": 1,
+        "num_hidden_layers": 36,
+        "num_key_value_heads": 2,
+        "num_return_sequences": 1,
+        "output_attentions": false,
+        "output_hidden_states": false,
+        "output_scores": false,
+        "pad_token_id": null,
+        "prefix": null,
+        "problem_type": null,
+        "pruned_heads": {},
+        "remove_invalid_values": false,
+        "repetition_penalty": 1.0,
+        "return_dict": true,
+        "return_dict_in_generate": false,
+        "rms_norm_eps": 1e-06,
+        "rope_scaling": null,
+        "rope_theta": 1000000.0,
+        "sep_token_id": null,
+        "sliding_window": null,
+        "suppress_tokens": null,
+        "task_specific_params": null,
+        "temperature": 1.0,
+        "tf_legacy_loss": false,
+        "tie_encoder_decoder": false,
+        "tie_word_embeddings": false,
+        "tokenizer_class": null,
+        "top_k": 50,
+        "top_p": 1.0,
+        "torch_dtype": null,
+        "torchscript": false,
+        "transformers_version": "4.46.1",
+        "typical_p": 1.0,
+        "use_bfloat16": false,
+        "use_cache": true,
+        "use_sliding_window": false,
+        "vocab_size": 151936
+    },
+    "torch_dtype": "bfloat16",
+    "transformers_version": null,
+    "vision_config": {
+        "_attn_implementation_autoset": false,
+        "_name_or_path": "",
+        "add_cross_attention": false,
+        "architectures": null,
+        "bad_words_ids": null,
+        "begin_suppress_tokens": null,
+        "bos_token_id": null,
+        "chunk_size_feed_forward": 0,
+        "cross_attention_hidden_size": null,
+        "decoder_start_token_id": null,
+        "depth": 32,
+        "diversity_penalty": 0.0,
+        "do_sample": false,
+        "early_stopping": false,
+        "embed_dim": 1280,
+        "encoder_no_repeat_ngram_size": 0,
+        "eos_token_id": null,
+        "exponential_decay_length_penalty": null,
+        "finetuning_task": null,
+        "forced_bos_token_id": null,
+        "forced_eos_token_id": null,
+        "hidden_act": "quick_gelu",
+        "hidden_size": 3584,
+        "id2label": {
+            "0": "LABEL_0",
+            "1": "LABEL_1"
+        },
+        "in_channels": 3,
+        "in_chans": 3,
+        "is_decoder": false,
+        "is_encoder_decoder": false,
+        "label2id": {
+            "LABEL_0": 0,
+            "LABEL_1": 1
+        },
+        "length_penalty": 1.0,
+        "max_length": 20,
+        "min_length": 0,
+        "mlp_ratio": 4,
+        "model_type": "qwen2_vl",
+        "no_repeat_ngram_size": 0,
+        "num_beam_groups": 1,
+        "num_beams": 1,
+        "num_heads": 16,
+        "num_return_sequences": 1,
+        "output_attentions": false,
+        "output_hidden_states": false,
+        "output_scores": false,
+        "pad_token_id": null,
+        "patch_size": 14,
+        "prefix": null,
+        "problem_type": null,
+        "pruned_heads": {},
+        "remove_invalid_values": false,
+        "repetition_penalty": 1.0,
+        "return_dict": true,
+        "return_dict_in_generate": false,
+        "sep_token_id": null,
+        "spatial_merge_size": 2,
+        "spatial_patch_size": 14,
+        "suppress_tokens": null,
+        "task_specific_params": null,
+        "temperature": 1.0,
+        "temporal_patch_size": 2,
+        "tf_legacy_loss": false,
+        "tie_encoder_decoder": false,
+        "tie_word_embeddings": true,
+        "tokenizer_class": null,
+        "top_k": 50,
+        "top_p": 1.0,
+        "torch_dtype": "bfloat16",
+        "torchscript": false,
+        "transformers_version": "4.46.1",
+        "typical_p": 1.0,
+        "use_bfloat16": false
+    },
+    "image_token_id": 151655
+}

configuration_pointsv15_chat.py

@@ -0,0 +1,35 @@
+import copy
+from typing import Any, Dict
+
+from transformers import PretrainedConfig, Qwen2Config
+
+try:
+    from transformers.models.qwen2_vl.configuration_qwen2_vl import Qwen2VLVisionConfig
+except ImportError:
+    print('Please upgrade transformers to version 4.46.3 or higher')
+
+
+class POINTSV15ChatConfig(PretrainedConfig):
+    model_type = "pointsv1.5_chat"
+    is_composition = True
+    """Configuration class for `POINTSV1.5`."""
+
+    def __init__(self,
+                 **kwargs) -> None:
+        super().__init__(**kwargs)
+        vision_config = kwargs.pop("vision_config", None)
+        llm_config = kwargs.pop("llm_config", None)
+        if isinstance(vision_config, dict):
+            self.vision_config = Qwen2VLVisionConfig(**vision_config)
+        else:
+            self.vision_config = vision_config
+        if isinstance(llm_config, dict):
+            self.llm_config = Qwen2Config(**llm_config)
+        else:
+            self.llm_config = llm_config
+
+    def to_dict(self) -> Dict[str, Any]:
+        output = copy.deepcopy(self.__dict__)
+        output["vision_config"] = self.vision_config.to_dict()
+        output["llm_config"] = self.llm_config.to_dict()
+        return output

generation_config.json

@@ -0,0 +1,4 @@
+{
+  "_from_model_config": true,
+  "transformers_version": "4.46.1"
+}

model-00001-of-00002.safetensors

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model-00002-of-00002.safetensors

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+}

modeling_pointsv15_chat.py

@@ -0,0 +1,230 @@
+import math
+from typing import List, Optional, Tuple
+
+import numpy as np
+import torch
+from PIL import Image
+from transformers import (
+    GenerationMixin,
+    PreTrainedModel,
+    PreTrainedTokenizer,
+    Qwen2ForCausalLM,
+)
+
+try:
+    from transformers.models.qwen2_vl.image_processing_qwen2_vl import (  # noqa
+        Qwen2VLImageProcessor,
+    )
+    from transformers.models.qwen2_vl.modeling_qwen2_vl import PatchMerger
+except ImportError:
+    print('Please upgrade transformers to version 4.46.3 or higher')
+
+from .configuration_pointsv15_chat import POINTSV15ChatConfig
+
+try:
+    from wepoints.models import Qwen2VisionTransformerForNavitPOINTS
+except ImportError:
+    print('Please install WePOINTS, and refer to https://github.com/WePOINTS/WePOINTS')
+
+
+class POINTSV15ChatModel(PreTrainedModel, GenerationMixin):
+    config_class = POINTSV15ChatConfig
+    _no_split_modules = ["CustomLlamaLayer",
+                         "Qwen2VisionTransformerPretrainedModel"]
+
+    """Chat model for POINTSv1.5.
+    
+    Args:
+        config (POINTSChatConfigV15): The model config.
+    """
+
+    def __init__(self, config: POINTSV15ChatConfig) -> None:
+        super().__init__(config)
+        config.llm_config._attn_implementation = "flash_attention_2"
+        config._attn_implementation_autoset = False
+        self.llm = Qwen2ForCausalLM(config.llm_config)
+        self.vision_encoder = Qwen2VisionTransformerForNavitPOINTS._from_config(  # noqa
+            config.vision_config, attn_implementation="flash_attention_2"
+        )
+        self.vision_projector = PatchMerger(config.llm_config.hidden_size,
+                                            context_dim=1280).to(torch.bfloat16)
+
+    def process_images(self, images: torch.Tensor, 
+                       image_grid_thws: List[list]) -> torch.Tensor:
+        """Obtain image features from the vision encoder.
+        
+        Args:
+            images (torch.Tensor): The input images.
+            image_grid_thws (List[list]): The grid thresholds for the images.
+
+        Returns:
+            torch.Tensor: The image features.
+        """
+        image_features = self.vision_encoder(images, grid_thw=image_grid_thws)
+        image_features = self.vision_projector(image_features)
+        return image_features
+    
+    def construct_prompt(self, messages: List[dict],
+                         image_processor: Qwen2VLImageProcessor) -> Tuple[str, List[Image.Image], List[list]]: # noqa
+        """Construct the prompt for the chat model.
+
+        Args:
+            messages (List[dict]): The input messages.
+        
+        Returns:
+            Tuple[str, List[Image.Image], List[list]]: 
+                The prompt, images, and image grid shape.
+        """
+        images = []
+        image_grid_thws = []
+        reconstructed_messages = []
+        for message in messages:
+            role = message['role']
+            content_from_role = ''
+            for item in message['content']:
+                if item['type'] == 'text':
+                    content_from_role += item['text']
+                elif item['type'] == 'image':
+                    image_path = item['image']
+                    max_pixels = item['max_pixels'] if 'max_pixels' in item else None
+                    image = Image.open(image_path).convert('RGB')
+                    if max_pixels is not None:
+                        # obtain image size
+                        width, height = image.size
+                        cur_image_pixels = width * height
+                        if cur_image_pixels > max_pixels:
+                            beta = math.sqrt((height * width) / max_pixels)
+                            new_width = math.floor(width / beta)
+                            new_height = math.floor(height / beta)
+                            image = image.resize((new_width, new_height))
+                    image_data = image_processor(images=image)
+                    pixel_values = image_data['pixel_values']
+                    image_grid_thw = image_data['image_grid_thw']
+                    images.extend(pixel_values)
+                    image_grid_thws.append(image_grid_thw)
+                    seq_len = int(image_grid_thw[0][1] * image_grid_thw[0][2] / 4) # noqa
+                    content_from_role += '<|vision_start|>' + '<|image_pad|>' * seq_len + '<|vision_end|>' + '\n' # noqa
+            reconstructed_messages.append({
+                'role': role,
+                'content': content_from_role
+            })
+        prompt = self.apply_chat_template(reconstructed_messages)
+        return prompt, images, image_grid_thws
+    
+    def apply_chat_template(self, messages: List[dict]) -> str:
+        """Apply the chat template to the input messages.
+
+        Args:
+            messages (List[dict]): The input messages.
+        
+        Returns:
+            str: The prompt.
+        """
+        role_prefix_mapping = {
+            'user': '<|im_start|>user\n',
+            'assistant': '<|im_start|>assistant\n'
+        }
+        role = 'user'
+        prompt = ''
+        for message in messages:
+            role = message['role']
+            content = message['content']
+            prompt += role_prefix_mapping[role] + content + '<|im_end|>\n'
+        if role == 'user':
+            prompt += '<|im_start|>assistant\n'
+        return prompt
+
+    @torch.no_grad()
+    def chat(self, 
+             messages: List[dict],
+             tokenizer: PreTrainedTokenizer,
+             image_processor: object,
+             generation_config: dict = None) -> str:
+        """Generate a response to the input prompt.
+
+        Args:
+            messages (List[dict]): The input messages.
+            tokenizer (PreTrainedTokenizer): The tokenizer to use.
+            image_processor (object): The image processor to use.
+            generation_config (dict, optional): The generation config. 
+                Defaults to None.
+        Returns:
+            str: The generated response.
+        """
+        prompt, images, image_grid_thws = self.construct_prompt(
+            messages, image_processor
+        )
+        images = np.array(images)
+        images = torch.from_numpy(images).to(self.vision_encoder.device).to(self.vision_encoder.dtype) # noqa
+        image_grid_thws = np.concatenate(image_grid_thws, axis=0)
+        image_grid_thws = (
+            torch.from_numpy(image_grid_thws)
+            .cuda()
+            .long()
+        )
+        image_features = self.vision_encoder(images, grid_thw=image_grid_thws)
+        image_features = self.vision_projector(image_features.to(torch.bfloat16))
+        model_inputs = tokenizer(prompt, return_tensors='pt')
+        input_ids = model_inputs['input_ids'].to(self.device)
+        attention_mask = model_inputs['attention_mask'].to(self.device)
+        # stop token
+        eos_token_id = tokenizer.convert_tokens_to_ids("<|im_end|>")
+        # image token
+        image_token_id = tokenizer.convert_tokens_to_ids("<|image_pad|>")
+        generation_config.update(
+            {
+                'eos_token_id': eos_token_id,
+            }
+        )
+        outputs = self.generate(
+            input_ids=input_ids,
+            image_grid_thws=image_grid_thws,
+            attention_mask=attention_mask,
+            image_features=[image_features],
+            image_token_id=image_token_id,
+            **generation_config
+        )
+        response = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
+        return response
+      
+    def _split_input_ids(self, input_ids, special_token):
+        special_pos = input_ids == special_token
+        pos = (special_pos[:-1] != special_pos[1:]).nonzero() + 1
+        if pos.shape[0] % 2 != 0:
+            pos = torch.cat([torch.tensor([[0]]).to(pos.device), pos])
+        pos = pos.reshape(-1, 2).tolist()
+        return pos
+
+    def generate(self,
+                 input_ids: torch.LongTensor,
+                 image_grid_thws: torch.LongTensor,
+                 attention_mask: torch.LongTensor,
+                 image_features: List[torch.Tensor],
+                 image_token_id: int,
+                 generation_config: Optional[dict] = None,
+                 output_hidden_states: Optional[bool] = None,
+                 **generate_kwargs) -> torch.LongTensor:
+        input_embeddings = self.llm.model.embed_tokens(input_ids)
+        batch_size = input_ids.shape[0]
+        assert len(image_features) == batch_size
+        for i in range(batch_size):
+            pos = self._split_input_ids(input_ids[i], image_token_id)
+            assert len(pos) == len(image_grid_thws)
+            image_pos = [
+                int(image_grid_thw[1] * image_grid_thw[2] / 4)
+                for image_grid_thw in image_grid_thws
+            ]
+            image_pos.insert(0, 0)
+            image_pos = np.cumsum(image_pos)
+            for j, (start, end) in enumerate(pos):
+                input_embeddings[i, start:end] = \
+                    image_features[i][image_pos[j]:image_pos[j+1]]
+        outputs = self.llm.generate(
+            inputs_embeds=input_embeddings,
+            attention_mask=attention_mask,
+            generation_config=generation_config,
+            output_hidden_states=output_hidden_states,
+            use_cache=True,
+            **generate_kwargs
+        )
+        return outputs

preprocessor_config.json

@@ -0,0 +1,29 @@
+{
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "image_mean": [
+        0.48145466,
+        0.4578275,
+        0.40821073
+    ],
+    "image_processor_type": "Qwen2VLImageProcessor",
+    "image_std": [
+        0.26862954,
+        0.26130258,
+        0.27577711
+    ],
+    "max_pixels": 12845056,
+    "merge_size": 2,
+    "min_pixels": 3136,
+    "patch_size": 14,
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "size": {
+        "max_pixels": 12845056,
+        "min_pixels": 3136
+    },
+    "temporal_patch_size": 2,
+    "processor_class": "Qwen2VLProcessor"
+}

tokenizer_config.json

@@ -0,0 +1,207 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}