{"id":1507,"date":"2026-09-18T01:10:54","date_gmt":"2026-09-17T16:10:54","guid":{"rendered":"https:\/\/localmodelwatch.tsuchitsuchi.com\/2026\/09\/18\/tencent-releases-wevisdoc-document-parsing-models\/"},"modified":"2026-09-18T21:42:07","modified_gmt":"2026-09-18T12:42:07","slug":"tencent-releases-wevisdoc-document-parsing-models","status":"publish","type":"post","link":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/2026\/09\/18\/tencent-releases-wevisdoc-document-parsing-models\/","title":{"rendered":"Tencent Releases WeVisDoc-2B and WeVisDoc-4B Document Parsing Models"},"content":{"rendered":"<p><!-- lmw:facts --><\/p>\n<h2>At a Glance<\/h2>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Item<\/th>\n<th>Value<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Repository<\/td>\n<td><a href=\"https:\/\/huggingface.co\/tencent\/WeVisDoc-4B\">tencent\/WeVisDoc-4B<\/a><\/td>\n<\/tr>\n<tr>\n<td>Published<\/td>\n<td>2026-09-16<\/td>\n<\/tr>\n<tr>\n<td>License<\/td>\n<td>apache-2.0<\/td>\n<\/tr>\n<tr>\n<td>Formats<\/td>\n<td>safetensors<\/td>\n<\/tr>\n<tr>\n<td>Paper<\/td>\n<td><a href=\"https:\/\/arxiv.org\/abs\/2609.20423\">arXiv:2609.20423<\/a><\/td>\n<\/tr>\n<tr>\n<td>Source type<\/td>\n<td>Primary source (the publisher itself)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p><em>Values determined by this site&#8217;s code at collection time. Dates are JST.<\/em><\/p>\n<p><!-- \/lmw:facts --><\/p>\n<h2>Overview<\/h2>\n<p>Tencent has released &#8220;WeVisDoc-2B&#8221; and &#8220;WeVisDoc-4B&#8221;, end-to-end document parsing models that extract structured text from page images.<\/p>\n<p>These models are fine-tuned versions based on &#8220;Qwen3-VL-2B-Instruct&#8221; and &#8220;Qwen3-VL-4B-Instruct&#8221; respectively, taking page images as input and outputting structured Markdown containing LaTeX-format mathematical formulas and HTML-format tables.<\/p>\n<h2>Specifications<\/h2>\n<ul>\n<li>Architecture: Qwen3VLForConditionalGeneration (qwen3_vl)<\/li>\n<li>Base Models: Qwen3-VL-2B-Instruct, Qwen3-VL-4B-Instruct<\/li>\n<li>License: apache-2.0<\/li>\n<\/ul>\n<h2>Performance<\/h2>\n<p>The measurement results of document parsing benchmarks provided by the publisher are as follows. Evaluation values represent the average of three inference runs.<\/p>\n<h3>OmniDocBench v1.6<\/h3>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<p class=\"lmw-table-hint\" style=\"margin:0 0 4px;font-size:0.85em;opacity:0.7;\">\u2192 Scroll horizontally to see all columns<\/p>\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Model<\/th>\n<th style=\"text-align: right;\">Params<\/th>\n<th style=\"text-align: right;\">Overall \u2191<\/th>\n<th style=\"text-align: right;\">TextEdit \u2193<\/th>\n<th style=\"text-align: right;\">FormulaCDM \u2191<\/th>\n<th style=\"text-align: right;\">TableTEDS \u2191<\/th>\n<th style=\"text-align: right;\">TableTEDS_S \u2191<\/th>\n<th style=\"text-align: right;\">ROEdit \u2193<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Nanonets-OCR2*<\/td>\n<td style=\"text-align: right;\">3B<\/td>\n<td style=\"text-align: right;\">83.20<\/td>\n<td style=\"text-align: right;\">0.108<\/td>\n<td style=\"text-align: right;\">80.35<\/td>\n<td style=\"text-align: right;\">80.10<\/td>\n<td style=\"text-align: right;\">85.26<\/td>\n<td style=\"text-align: right;\">0.211<\/td>\n<\/tr>\n<tr>\n<td>OCRFlux-3B*<\/td>\n<td style=\"text-align: right;\">3B<\/td>\n<td style=\"text-align: right;\">83.31<\/td>\n<td style=\"text-align: right;\">0.126<\/td>\n<td style=\"text-align: right;\">88.75<\/td>\n<td style=\"text-align: right;\">73.78<\/td>\n<td style=\"text-align: right;\">77.98<\/td>\n<td style=\"text-align: right;\">0.217<\/td>\n<\/tr>\n<tr>\n<td>POINTS-Reader<\/td>\n<td style=\"text-align: right;\">3B<\/td>\n<td style=\"text-align: right;\">83.37<\/td>\n<td style=\"text-align: right;\">0.096<\/td>\n<td style=\"text-align: right;\">85.72<\/td>\n<td style=\"text-align: right;\">73.98<\/td>\n<td style=\"text-align: right;\">77.40<\/td>\n<td style=\"text-align: right;\">0.198<\/td>\n<\/tr>\n<tr>\n<td>Nanonets-OCR-s<\/td>\n<td style=\"text-align: right;\">3B<\/td>\n<td style=\"text-align: right;\">83.61<\/td>\n<td style=\"text-align: right;\">0.108<\/td>\n<td style=\"text-align: right;\">81.46<\/td>\n<td style=\"text-align: right;\">80.18<\/td>\n<td style=\"text-align: right;\">84.51<\/td>\n<td style=\"text-align: right;\">0.213<\/td>\n<\/tr>\n<tr>\n<td>olmOCR-2-7B*<\/td>\n<td style=\"text-align: right;\">7B<\/td>\n<td style=\"text-align: right;\">85.51<\/td>\n<td style=\"text-align: right;\">0.106<\/td>\n<td style=\"text-align: right;\">88.84<\/td>\n<td style=\"text-align: right;\">78.32<\/td>\n<td style=\"text-align: right;\">82.81<\/td>\n<td style=\"text-align: right;\">0.223<\/td>\n<\/tr>\n<tr>\n<td>olmOCR<\/td>\n<td style=\"text-align: right;\">7B<\/td>\n<td style=\"text-align: right;\">85.74<\/td>\n<td style=\"text-align: right;\">0.139<\/td>\n<td style=\"text-align: right;\">88.10<\/td>\n<td style=\"text-align: right;\">83.00<\/td>\n<td style=\"text-align: right;\">87.17<\/td>\n<td style=\"text-align: right;\">0.216<\/td>\n<\/tr>\n<tr>\n<td>DeepSeek-OCR*<\/td>\n<td style=\"text-align: right;\">3B<\/td>\n<td style=\"text-align: right;\">86.31<\/td>\n<td style=\"text-align: right;\">0.077<\/td>\n<td style=\"text-align: right;\">84.71<\/td>\n<td style=\"text-align: right;\">81.87<\/td>\n<td style=\"text-align: right;\">86.07<\/td>\n<td style=\"text-align: right;\">0.171<\/td>\n<\/tr>\n<tr>\n<td>OCRVerse<\/td>\n<td style=\"text-align: right;\">4B<\/td>\n<td style=\"text-align: right;\">88.60<\/td>\n<td style=\"text-align: right;\">0.063<\/td>\n<td style=\"text-align: right;\">89.61<\/td>\n<td style=\"text-align: right;\">82.44<\/td>\n<td style=\"text-align: right;\">86.27<\/td>\n<td style=\"text-align: right;\">0.163<\/td>\n<\/tr>\n<tr>\n<td>UniRec-0.1B*<\/td>\n<td style=\"text-align: right;\">0.1B<\/td>\n<td style=\"text-align: right;\">88.91<\/td>\n<td style=\"text-align: right;\">0.088<\/td>\n<td style=\"text-align: right;\">92.14<\/td>\n<td style=\"text-align: right;\">83.40<\/td>\n<td style=\"text-align: right;\">86.79<\/td>\n<td style=\"text-align: right;\">0.146<\/td>\n<\/tr>\n<tr>\n<td>DeepSeek-OCR 2<\/td>\n<td style=\"text-align: right;\">3B<\/td>\n<td style=\"text-align: right;\">90.25<\/td>\n<td style=\"text-align: right;\">0.050<\/td>\n<td style=\"text-align: right;\">91.84<\/td>\n<td style=\"text-align: right;\">83.89<\/td>\n<td style=\"text-align: right;\">87.75<\/td>\n<td style=\"text-align: right;\">0.144<\/td>\n<\/tr>\n<tr>\n<td>dots.ocr<\/td>\n<td style=\"text-align: right;\">3B<\/td>\n<td style=\"text-align: right;\">90.77<\/td>\n<td style=\"text-align: right;\">0.048<\/td>\n<td style=\"text-align: right;\">89.95<\/td>\n<td style=\"text-align: right;\">87.18<\/td>\n<td style=\"text-align: right;\">90.58<\/td>\n<td style=\"text-align: right;\">0.138<\/td>\n<\/tr>\n<tr>\n<td>FD-RL*<\/td>\n<td style=\"text-align: right;\">4B<\/td>\n<td style=\"text-align: right;\">91.21<\/td>\n<td style=\"text-align: right;\">0.055<\/td>\n<td style=\"text-align: right;\">92.92<\/td>\n<td style=\"text-align: right;\">86.22<\/td>\n<td style=\"text-align: right;\">90.92<\/td>\n<td style=\"text-align: right;\">0.145<\/td>\n<\/tr>\n<tr>\n<td>HunyuanOCR<\/td>\n<td style=\"text-align: right;\">1B<\/td>\n<td style=\"text-align: right;\">92.03<\/td>\n<td style=\"text-align: right;\">0.048<\/td>\n<td style=\"text-align: right;\">88.60<\/td>\n<td style=\"text-align: right;\">92.37<\/td>\n<td style=\"text-align: right;\">93.99<\/td>\n<td style=\"text-align: right;\">0.138<\/td>\n<\/tr>\n<tr>\n<td>dots.mocr*<\/td>\n<td style=\"text-align: right;\">3B<\/td>\n<td style=\"text-align: right;\">92.57<\/td>\n<td style=\"text-align: right;\">0.042<\/td>\n<td style=\"text-align: right;\">92.09<\/td>\n<td style=\"text-align: right;\">89.78<\/td>\n<td style=\"text-align: right;\">92.92<\/td>\n<td style=\"text-align: right;\">0.133<\/td>\n<\/tr>\n<tr>\n<td>FireRed-OCR<\/td>\n<td style=\"text-align: right;\">2B<\/td>\n<td style=\"text-align: right;\">93.26<\/td>\n<td style=\"text-align: right;\">0.037<\/td>\n<td style=\"text-align: right;\">95.44<\/td>\n<td style=\"text-align: right;\">88.04<\/td>\n<td style=\"text-align: right;\">91.06<\/td>\n<td style=\"text-align: right;\">0.131<\/td>\n<\/tr>\n<tr>\n<td>Logics-Parsing-v2<\/td>\n<td style=\"text-align: right;\">4B<\/td>\n<td style=\"text-align: right;\">93.33<\/td>\n<td style=\"text-align: right;\">0.041<\/td>\n<td style=\"text-align: right;\">95.65<\/td>\n<td style=\"text-align: right;\">88.42<\/td>\n<td style=\"text-align: right;\">91.98<\/td>\n<td style=\"text-align: right;\">0.137<\/td>\n<\/tr>\n<tr>\n<td>Qianfan-OCR<\/td>\n<td style=\"text-align: right;\">4B<\/td>\n<td style=\"text-align: right;\">93.90<\/td>\n<td style=\"text-align: right;\">0.040<\/td>\n<td style=\"text-align: right;\">95.08<\/td>\n<td style=\"text-align: right;\">90.53<\/td>\n<td style=\"text-align: right;\">93.31<\/td>\n<td style=\"text-align: right;\">0.130<\/td>\n<\/tr>\n<tr>\n<td>Unlimited-OCR<\/td>\n<td style=\"text-align: right;\">3B-A0.5B<\/td>\n<td style=\"text-align: right;\">93.92<\/td>\n<td style=\"text-align: right;\">0.042<\/td>\n<td style=\"text-align: right;\">95.79<\/td>\n<td style=\"text-align: right;\">90.16<\/td>\n<td style=\"text-align: right;\">93.32<\/td>\n<td style=\"text-align: right;\">0.129<\/td>\n<\/tr>\n<tr>\n<td>HunyuanOCR-1.5<\/td>\n<td style=\"text-align: right;\">1B<\/td>\n<td style=\"text-align: right;\">94.74<\/td>\n<td style=\"text-align: right;\">0.039<\/td>\n<td style=\"text-align: right;\">94.50<\/td>\n<td style=\"text-align: right;\">93.67<\/td>\n<td style=\"text-align: right;\">94.71<\/td>\n<td style=\"text-align: right;\">0.129<\/td>\n<\/tr>\n<tr>\n<td>WeVisDoc-2B<\/td>\n<td style=\"text-align: right;\">2B<\/td>\n<td style=\"text-align: right;\">95.06<\/td>\n<td style=\"text-align: right;\">0.038<\/td>\n<td style=\"text-align: right;\">95.94<\/td>\n<td style=\"text-align: right;\">93.03<\/td>\n<td style=\"text-align: right;\">95.26<\/td>\n<td style=\"text-align: right;\">0.130<\/td>\n<\/tr>\n<tr>\n<td>WeVisDoc-4B<\/td>\n<td style=\"text-align: right;\">4B<\/td>\n<td style=\"text-align: right;\">95.38<\/td>\n<td style=\"text-align: right;\">0.036<\/td>\n<td style=\"text-align: right;\">96.81<\/td>\n<td style=\"text-align: right;\">92.95<\/td>\n<td style=\"text-align: right;\">95.34<\/td>\n<td style=\"text-align: right;\">0.125<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<h3>PureDocBench<\/h3>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<p class=\"lmw-table-hint\" style=\"margin:0 0 4px;font-size:0.85em;opacity:0.7;\">\u2192 Scroll horizontally to see all columns<\/p>\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Model<\/th>\n<th style=\"text-align: right;\">Params<\/th>\n<th style=\"text-align: right;\">Avg\u2083 \u2191<\/th>\n<th style=\"text-align: right;\">Clean Overall \u2191<\/th>\n<th style=\"text-align: right;\">Digital Degraded Overall \u2191<\/th>\n<th style=\"text-align: right;\">Real Degraded Overall \u2191<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>OCRFlux-3B<\/td>\n<td style=\"text-align: right;\">3B<\/td>\n<td style=\"text-align: right;\">42.06<\/td>\n<td style=\"text-align: right;\">47.14<\/td>\n<td style=\"text-align: right;\">41.82<\/td>\n<td style=\"text-align: right;\">37.21<\/td>\n<\/tr>\n<tr>\n<td>DeepSeek-OCR<\/td>\n<td style=\"text-align: right;\">3B<\/td>\n<td style=\"text-align: right;\">46.98<\/td>\n<td style=\"text-align: right;\">53.50<\/td>\n<td style=\"text-align: right;\">46.95<\/td>\n<td style=\"text-align: right;\">40.48<\/td>\n<\/tr>\n<tr>\n<td>UniRec-0.1B<\/td>\n<td style=\"text-align: right;\">0.1B<\/td>\n<td style=\"text-align: right;\">48.59<\/td>\n<td style=\"text-align: right;\">58.91<\/td>\n<td style=\"text-align: right;\">52.42<\/td>\n<td style=\"text-align: right;\">34.44<\/td>\n<\/tr>\n<tr>\n<td>POINTS-Reader*<\/td>\n<td style=\"text-align: right;\">3B<\/td>\n<td style=\"text-align: right;\">49.24<\/td>\n<td style=\"text-align: right;\">53.78<\/td>\n<td style=\"text-align: right;\">51.24<\/td>\n<td style=\"text-align: right;\">42.69<\/td>\n<\/tr>\n<tr>\n<td>DeepSeek-OCR-2<\/td>\n<td style=\"text-align: right;\">3B<\/td>\n<td style=\"text-align: right;\">49.51<\/td>\n<td style=\"text-align: right;\">55.53<\/td>\n<td style=\"text-align: right;\">49.41<\/td>\n<td style=\"text-align: right;\">43.60<\/td>\n<\/tr>\n<tr>\n<td>Qianfan-OCR<\/td>\n<td style=\"text-align: right;\">4B<\/td>\n<td style=\"text-align: right;\">51.04<\/td>\n<td style=\"text-align: right;\">57.22<\/td>\n<td style=\"text-align: right;\">50.85<\/td>\n<td style=\"text-align: right;\">45.06<\/td>\n<\/tr>\n<tr>\n<td>olmOCR-7B<\/td>\n<td style=\"text-align: right;\">7B<\/td>\n<td style=\"text-align: right;\">55.90<\/td>\n<td style=\"text-align: right;\">62.56<\/td>\n<td style=\"text-align: right;\">57.84<\/td>\n<td style=\"text-align: right;\">47.30<\/td>\n<\/tr>\n<tr>\n<td>Nanonets-OCR2<\/td>\n<td style=\"text-align: right;\">3B<\/td>\n<td style=\"text-align: right;\">58.36<\/td>\n<td style=\"text-align: right;\">64.83<\/td>\n<td style=\"text-align: right;\">61.23<\/td>\n<td style=\"text-align: right;\">49.03<\/td>\n<\/tr>\n<tr>\n<td>HunyuanOCR<\/td>\n<td style=\"text-align: right;\">1B<\/td>\n<td style=\"text-align: right;\">60.56<\/td>\n<td style=\"text-align: right;\">65.61<\/td>\n<td style=\"text-align: right;\">61.49<\/td>\n<td style=\"text-align: right;\">54.58<\/td>\n<\/tr>\n<tr>\n<td>Unlimited-OCR*<\/td>\n<td style=\"text-align: right;\">3B-A0.5B<\/td>\n<td style=\"text-align: right;\">62.76<\/td>\n<td style=\"text-align: right;\">71.28<\/td>\n<td style=\"text-align: right;\">63.62<\/td>\n<td style=\"text-align: right;\">53.39<\/td>\n<\/tr>\n<tr>\n<td>olmOCR-2-7B<\/td>\n<td style=\"text-align: right;\">7B<\/td>\n<td style=\"text-align: right;\">63.78<\/td>\n<td style=\"text-align: right;\">69.36<\/td>\n<td style=\"text-align: right;\">65.87<\/td>\n<td style=\"text-align: right;\">56.10<\/td>\n<\/tr>\n<tr>\n<td>dots.ocr<\/td>\n<td style=\"text-align: right;\">3B<\/td>\n<td style=\"text-align: right;\">64.55<\/td>\n<td style=\"text-align: right;\">72.01<\/td>\n<td style=\"text-align: right;\">65.95<\/td>\n<td style=\"text-align: right;\">55.68<\/td>\n<\/tr>\n<tr>\n<td>Nanonets-OCR-s*<\/td>\n<td style=\"text-align: right;\">3B<\/td>\n<td style=\"text-align: right;\">65.37<\/td>\n<td style=\"text-align: right;\">71.26<\/td>\n<td style=\"text-align: right;\">66.56<\/td>\n<td style=\"text-align: right;\">58.28<\/td>\n<\/tr>\n<tr>\n<td>FireRed-OCR<\/td>\n<td style=\"text-align: right;\">2B<\/td>\n<td style=\"text-align: right;\">65.57<\/td>\n<td style=\"text-align: right;\">70.81<\/td>\n<td style=\"text-align: right;\">68.49<\/td>\n<td style=\"text-align: right;\">57.42<\/td>\n<\/tr>\n<tr>\n<td>HunyuanOCR-1.5*<\/td>\n<td style=\"text-align: right;\">1B<\/td>\n<td style=\"text-align: right;\">68.79<\/td>\n<td style=\"text-align: right;\">73.98<\/td>\n<td style=\"text-align: right;\">70.81<\/td>\n<td style=\"text-align: right;\">61.59<\/td>\n<\/tr>\n<tr>\n<td>OCRVerse<\/td>\n<td style=\"text-align: right;\">4B<\/td>\n<td style=\"text-align: right;\">69.40<\/td>\n<td style=\"text-align: right;\">73.18<\/td>\n<td style=\"text-align: right;\">71.36<\/td>\n<td style=\"text-align: right;\">63.66<\/td>\n<\/tr>\n<tr>\n<td>dots.mocr<\/td>\n<td style=\"text-align: right;\">3B<\/td>\n<td style=\"text-align: right;\">70.39<\/td>\n<td style=\"text-align: right;\">76.27<\/td>\n<td style=\"text-align: right;\">73.16<\/td>\n<td style=\"text-align: right;\">61.73<\/td>\n<\/tr>\n<tr>\n<td>Logics-Parsing-v2<\/td>\n<td style=\"text-align: right;\">4B<\/td>\n<td style=\"text-align: right;\">72.61<\/td>\n<td style=\"text-align: right;\">76.35<\/td>\n<td style=\"text-align: right;\">73.85<\/td>\n<td style=\"text-align: right;\">67.64<\/td>\n<\/tr>\n<tr>\n<td>FD-RL<\/td>\n<td style=\"text-align: right;\">4B<\/td>\n<td style=\"text-align: right;\">73.92<\/td>\n<td style=\"text-align: right;\">78.38<\/td>\n<td style=\"text-align: right;\">76.33<\/td>\n<td style=\"text-align: right;\">67.04<\/td>\n<\/tr>\n<tr>\n<td>WeVisDoc-2B<\/td>\n<td style=\"text-align: right;\">2B<\/td>\n<td style=\"text-align: right;\">73.86<\/td>\n<td style=\"text-align: right;\">79.36<\/td>\n<td style=\"text-align: right;\">76.62<\/td>\n<td style=\"text-align: right;\">65.60<\/td>\n<\/tr>\n<tr>\n<td>WeVisDoc-4B<\/td>\n<td style=\"text-align: right;\">4B<\/td>\n<td style=\"text-align: right;\">75.54<\/td>\n<td style=\"text-align: right;\">79.81<\/td>\n<td style=\"text-align: right;\">77.74<\/td>\n<td style=\"text-align: right;\">69.08<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>According to the measurement results stated in the model card, WeVisDoc-4B achieves the highest scores among compared end-to-end parsers in both the OmniDocBench v1.6 Overall (95.38) and the PureDocBench 3-track average Avg\u2083 (75.54). It exhibits particularly high figures in OmniDocBench v1.6 FormulaCDM (96.81) and PureDocBench Clean Overall (79.81), demonstrating superior parsing accuracy for mathematical formulas and standard document images. The smaller WeVisDoc-2B is also remarkably capable, recording 95.06 in OmniDocBench v1.6 Overall and outperforming larger and other models such as HunyuanOCR-1.5 and Logics-Parsing-v2. On the other hand, in PureDocBench&#8217;s &#8220;Real Degraded Overall&#8221;, WeVisDoc-2B stays at 65.60, trailing behind FD-RL (67.04) and Logics-Parsing-v2 (67.64), showing a difference in robustness toward real photographed degraded images compared to the larger 4B version.<\/p>\n<h2>Strengths and Use Cases<\/h2>\n<p>WeVisDoc specializes in generating structured Markdown from page images (PNG, JPEG, WebP) of books, research papers, and various documents. It excels not only in converting body text but also in extracting mathematical formulas using LaTeX format and analyzing table structures using HTML format. English (en) and Chinese (zh) are set as tag information, making it suitable for OCR and paper parsing use cases involving multilingual documents.<\/p>\n<p><!-- lmw:hardware --><\/p>\n<h2>Hardware Requirements<\/h2>\n<p><strong>Estimated requirements (calculated by Local Model Watch)<\/strong> \u2014 4.4B parameters<\/p>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Your VRAM<\/th>\n<th>Quantization<\/th>\n<th>File size<\/th>\n<th>Est. memory needed<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>12GB (RTX 4070 \/ 3060 12GB, etc.)<\/td>\n<td>BF16<\/td>\n<td>9.0GB<\/td>\n<td>10.8GB<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p><strong>Inference engine support<\/strong> (architecture name matched against each project&#8217;s own model registry in its source code, checked 2026-09-18): llama.cpp: registered, vLLM: registered, MLX (mlx-lm): registered.<\/p>\n<p><em>Memory estimates add a 20% runtime overhead (KV cache, etc.) to the actual size of the distributed files. Actual usage varies with context length, batch size and inference engine. These figures are computed by this site from file sizes, not published by the model&#8217;s authors. Compare with other models in our <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/vram-guide-en\/\">VRAM quick reference<\/a>. What the quantization names mean: <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/glossary-quantization-en\/\">glossary<\/a>.<\/em><\/p>\n<p><!-- \/lmw:hardware --><\/p>\n<h2>How to Get It<\/h2>\n<p>Model files are distributed in safetensors format and can be obtained directly from Hugging Face.<\/p>\n<p>An example script execution for starting a server using vLLM (&gt;=0.11.1) is as follows:<\/p>\n<pre><code class=\"language-bash\">bash scripts\/serve_vllm.sh Tencent\/WeVisDoc-2B\n<\/code><\/pre>\n<p>*When using the 4B model, specify <code>Tencent\/WeVisDoc-4B<\/code>.<\/p>\n<p>After starting the service, here is an example command to parse an image using the included client:<\/p>\n<pre><code class=\"language-bash\">python -m wevisdoc.client --image page.png --output results\/page.md\n<\/code><\/pre>\n<p>It can also be executed directly in a local inference environment using Transformers.<\/p>\n<pre><code class=\"language-bash\">python -m wevisdoc.local --model Tencent\/WeVisDoc-2B --image page.png --output results\/page.md\n<\/code><\/pre>\n<p><!-- lmw:related --><\/p>\n<h2>Related Articles<\/h2>\n<ul>\n<li><a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/2026\/09\/14\/tencent-simple-attention-sparsification-qwen3\/\">Tencent Releases Simple Attention Sparsification for Qwen3<\/a><\/li>\n<\/ul>\n<p><!-- \/lmw:related --><\/p>\n<h2>Sources<\/h2>\n<ul>\n<li><a href=\"https:\/\/huggingface.co\/tencent\/WeVisDoc-4B\">https:\/\/huggingface.co\/tencent\/WeVisDoc-4B<\/a><\/li>\n<li><a href=\"https:\/\/huggingface.co\/tencent\/WeVisDoc-2B\">https:\/\/huggingface.co\/tencent\/WeVisDoc-2B<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Tencent has released WeVisDoc-2B and WeVisDoc-4B, end-to-end document parsing models based on Qwen3-VL that extract structured text from page images.<\/p>\n","protected":false},"author":1,"featured_media":1506,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[1490,1492,1494,1172,1496,117],"class_list":["post-1507","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-document-parsing-en","tag-ocr-en","tag-qwen3-vl-en","tag-tencent-en","tag-wevisdoc-en","tag--en"],"lang":"en","translations":{"en":1507,"ja":1505},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1507","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1507"}],"version-history":[{"count":1,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1507\/revisions"}],"predecessor-version":[{"id":1690,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1507\/revisions\/1690"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/1506"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1507"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1507"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1507"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}