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View details ↗DISCOVER & COMPARE
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View details ↗ERNIE-4.5-0.3B-PT
baidu> [!NOTE] > Note: "**-Paddle**" models use PaddlePaddle weights, while "**-PT**" models use Transformer-style PyTorch weights. The advanced capabilities of the ERNIE 4.5 models, particularly the MoE-based A47B and A3B series, are underpinned by several key technical innovations: 1. **Multimodal Heterogeneous MoE Pre-Training:** Our models are jointly trained on both textual and visual modalities to better capture the nuances of multimodal information and improve performance on tasks involving text understanding and generation, image understanding, and cross-modal reasoning. To achieve this without one modality hindering the learning of another, we designed a *heterogeneous MoE structure*, incorporated *modality-isolated routing*, and employed *router orthogonal loss* and *multimodal token-balanced loss*. These architectural choices ensure that both modalities are effectively represented, allowing for mutual reinforcement during training.
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View details ↗ERNIE-4.5-21B-A3B-PT
baidu> [!NOTE] > Note: "**-Paddle**" models use PaddlePaddle weights, while "**-PT**" models use Transformer-style PyTorch weights. The advanced capabilities of the ERNIE 4.5 models, particularly the MoE-based A47B and A3B series, are underpinned by several key technical innovations: 1. **Multimodal Heterogeneous MoE Pre-Training:** Our models are jointly trained on both textual and visual modalities to better capture the nuances of multimodal information and improve performance on tasks involving text understanding and generation, image understanding, and cross-modal reasoning. To achieve this without one modality hindering the learning of another, we designed a *heterogeneous MoE structure*, incorporated *modality-isolated routing*, and employed *router orthogonal loss* and *multimodal token-balanced loss*. These architectural choices ensure that both modalities are effectively represented, allowing for mutual reinforcement during training.
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View details ↗Over the past three months, we have continued to scale the **thinking capability** of ERNIE-4.5-21B-A3B, improving both the **quality and depth** of reasoning, thereby advancing the competitiveness of ERNIE **lightweight models** in complex reasoning tasks. We are pleased to introduce **ERNIE-4.5-21B-A3B-Thinking**, featuring the following key enhancements: * **Significantly improved performance** on reasoning tasks, including logical reasoning, mathematics, science, coding, text generation, and academic benchmarks that typically require human expertise. * **Efficient tool usage** capabilities. * **Enhanced 128K long-context understanding** capabilities. > [!NOTE] > Note: This version has an increased thinking length. We strongly recommend its use in highly complex reasoning tasks.
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View details ↗> [!NOTE] > Note: "**-Paddle**" models use PaddlePaddle weights, while "**-PT**" models use Transformer-style PyTorch weights. The advanced capabilities of the ERNIE 4.5 models, particularly the MoE-based A47B and A3B series, are underpinned by several key technical innovations: 1. **Multimodal Heterogeneous MoE Pre-Training:** Our models are jointly trained on both textual and visual modalities to better capture the nuances of multimodal information and improve performance on tasks involving text understanding and generation, image understanding, and cross-modal reasoning. To achieve this without one modality hindering the learning of another, we designed a *heterogeneous MoE structure*, incorporated *modality-isolated routing*, and employed *router orthogonal loss* and *multimodal token-balanced loss*. These architectural choices ensure that both modalities are effectively represented, allowing for mutual reinforcement during training.
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View details ↗ERNIE-Image
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View details ↗ERNIE-Image-Aes
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View details ↗ERNIE-Image-Turbo
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View details ↗Qianfan-OCR
baidu**Qianfan-OCR** is a **4B-parameter end-to-end document intelligence model** developed by the Baidu Qianfan Team. It unifies document parsing, layout analysis, and document understanding within a single vision-language architecture. Unlike traditional multi-stage OCR pipelines that chain separate layout detection, text recognition, and language comprehension modules, Qianfan-OCR performs **direct image-to-Markdown conversion** and supports a broad range of prompt-driven tasks — from structured document parsing and table extraction to chart understanding, document question answering, and key information extraction — all within one model. - 🏆 **#1 End-to-End Model on OmniDocBench v1.5** — Achieves **93.12** overall score, surpassing DeepSeek-OCR-v2 (91.09), Gemini-3 Pro (90.33), and all other end-to-end models - 🏆 **#1 End-to-End Model on OlmOCR Bench** — Scores **79.8** - 🏆 **#1 on Key Information Extraction** — Overall mean score of **87.9** across five public KIE benchmarks, surpassing Gemini-3.1-Pro, Gemini-3-Pro, Seed-2.0, and Qwen3-VL-235B-A22B - 🧠 **Layout-as-Thought** — An innovative optional thinking phase that recovers explicit layout analysis within the end-to-end paradigm via `⟨think⟩` tokens - 🌍 **192 Languages** — Multilingual OCR support across diverse scripts - ⚡ **Efficient Deployment** — Achieves **1.024 PPS** (pages per second) with W8A8 quantization on a single A100 GPU
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View details ↗Unlimited-OCR
baidu- [2026/07/21] 🤝 Thanks to the ms-swift community for their support, our model now supports training with ms-swift. - [2026/07/03] 🤝 Thanks to the Baidu Cloud team for their support. Our model is now available on Baidu Cloud. - [2026/06/28] 🤝 Thanks to the vLLM community and Tianyu Guo for their support, our model now supports vLLM inference. - [2026/06/24] 🤝 Thanks to AK for creating a demo for us. It is now available at Hugging Face Spaces. - [2026/06/23] 📄 Our paper is now available on arXiv. - [2026/06/23] 🤝 Thanks to the ModelScope community for their support. Our model is now available at ModelScope. - [2026/06/22] 🚀 We present Unlimited-OCR, aiming to push Deepseek-OCR one step further. Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.12.3 + CUDA12.9： **Recipe:** https://recipes.vllm.ai/baidu/Unlimited-OCR
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View details ↗Unlimited-OCR-2
baidu> Unlimited OCR 2 supports **Transformers** (native, for quick tests) and **SGLang** (recommended for serving). Tested with Python 3.10, `torch==2.13.0`, `transformers==4.52.1`, `huggingface-hub==0.36.0`, `tokenizers==0.21.4`, on an NVIDIA A800-80GB: **Multi-page inference** (`infer_multi`). All pages of a document are parsed in one request, so the model can use cross-page context. Pass the page images in page order:
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