Example code and guides for basic tasks with LLMs Hosted by Qianfan Platform (https://console.bce.baidu.com/qianfan). You'll need a qianfan account and asscociated API key to accomplish all examples. We will provide example files, prompts within a cookbook. You can run all examples by setting the QIANFAN_TOKEN environment variable.
Note that this is a python code only repository.
2026.03.12: Qianfan-OCR: A Unified End-to-End Model for Document Intelligence is released! Qianfan-OCR (4B+300M parameters) is now available on Baidu AI Cloud Open source weights coming soon!
Qianfan-OCR is a unified end-to-end document intelligence model, designed to help enterprises achieve digital transformation and move towards intelligent automation. Key highlights:
- Top Performance End-to-End OCR Model on OmniDocBench v1.5 and OlmOCRBench.
- General OCR: Top model performance on OCRBench and OCRBench v2.
- Document Understanding: Strong performance in document QA and information extraction.
- Layout-as-Thought: For documents with complex layouts and non-standard reading orders, Qianfan-OCR can perform layout-analysis-level reasoning via a novel Layout-as-Thought mechanism, achieving superior recognition results.
- Multilingual OCR: Supports up to 192 languages with top performance on CC-OCR.
2025.09.22: The Qianfan-VL Vision-Language model series from Baidu AI Cloud is now open source!
- Multimodal Large Language Models:
Designed for enterprise applications, these multimodal models combine excellent general capabilities with advanced performance in OCR and education. For more information, please refer to github repo: https://github.com/baidubce/Qianfan-VL
2025.06.06: QianfanHuijin and QianfanHuijin-Reason series financial augmented models have been added to ModelBuilder (Link to apply for a trial):
- Financial Knowledge Augmented Models:
- Financial Reasoning Augmented Models:
2025.04.25: Five new Qianfan series models have been added to ModelBuilder:
- Text Models:
- Distilled Reasoning Models:
All models feature a 32K context length. Please note that only model access is provided; open sourced model weights coming soon!