A panoramic view of open source large model tools! Hugging Face, OlmOCR, Dify, a guide to core tools that developers must keep

In recent work, large model related tools or platforms are often used. Now the open source large model ecological tools are sorted out and divided into systems based on technical positioning and core functions:

1. Open Source Community

1. Hugging Face

   Positioning : The world's largest AI open source community, covering more than 400,000 pre-trained models (such as Llama3, Qwen2, DeepSeek) and data sets

• Core features :

• Model hosting and inference service (Inference API)
• Transformers library quickly loads models
• Spaces function supports application deployment

• Applicable scenarios : rapid prototyping, multi-language model experiments
• Link : https://huggingface.co

• Positioning : The largest open source community in China, launched by Alibaba Damo Academy, integrating domestic models such as Tongyi Qianwen and ChatGLM

• Core features :
• One-stop MaaS service (Model as a Service)
• Studio supports multi-model combination applications (such as MinerU knowledge base tool)
• Industry Datasets and Chinese Optimization Models
• Applicable scenarios : enterprise-level AI development, Chinese scene adaptation
• Link : https://modelscope.cn

2. Model-Based Tools

1. MinerU (Magic Creation Space)

• Core models and technologies :
• Formula detection : YOLO architecture model, the training set contains 24,000 inline formulas and 1,829 displayed formulas.
• Formula recognition : Self-developed UniMERNet model, trained on the UniMER-1M dataset, with performance comparable to the commercial software MathPix.
• Layout analysis : Based on the layout detection model in PDF-Extract-Kit, it is built through a diverse training set and supports the recognition of areas such as titles, text, images, and tables.
• Formula processing :
• Table recognition : Combining TableMaster (PubTabNet dataset) and StructEqTable (DocGenome dataset).
• OCR : Integrate PaddleOCR to extract text in reading order based on layout analysis results.
• Features : Outstanding multi-modal analysis capabilities, enterprise-level security compliance, and support for APIs and local clients.
• Link : https://modelscope.cn/studios

2. QAnything (NetEase Youdao)

• Core models and technologies :
• Semantic retrieval : Self-developed BCEmbedding model, supports cross-language retrieval in Chinese and English, and combines BM25 and vector hybrid retrieval strategy.
• Reranking optimization : The two-stage Reranker model solves the problem of large-scale data retrieval degradation and improves the accuracy of question answering.
• OCR parsing : Based on the PyMuPDF library, it supports efficient text extraction from PDF/image and other formats.
• Large model integration : Supports local models such as Qwen-7B and OpenAI API compatible interfaces for answer generation.
• Features : Pure local deployment, privacy and security, lightweight design (CPU/GPU dual mode).
• Link : https://github.com/netease-youdao/QAnything

3. olmOCR

• Core models and technologies :
• Visual Language Model (VLM) : Fine-tuned based on Qwen2-VL-7B-Instruct, supports complex document parsing (tables/formulas/multi-column layouts).
• Document anchoring technology : Combines PDF metadata (text block coordinates, image position) with page image input to reduce hallucinations and improve structured output accuracy.
• Distributed processing : Integrates sglang and vLLM inference engines, supports expansion from a single GPU to multiple nodes, and costs approximately $190 to process a million pages.
• Features : Open source full-stack solution (including model weights and training code), Markdown output adapted to large model training needs.
• Link : https://github.com/allenai/olmocr

Comparison summary

Extension suggestions :

• Enterprise-level requirements : MinerU (security and compliance) or QAnything (local deployment) is preferred.
• Academic/large-scale processing : olmOCR is cost-effective and suitable for cleaning large amounts of PDFs.
• Technology selection : It needs to be combined with hardware resources (such as GPU requirements) and output format requirements (such as Markdown compatibility).

3. AI Engine Platform

1. dify

• Positioning : Low-code LLM application development platform, supporting RAG and Agent workflow orchestration

• Core features :
• Visual Prompt Engineering and Multi-Model API Management
  
• Observability tools (Token consumption monitoring)
• Applicable scenarios : intelligent customer service system, enterprise-level LLM gateway
• Link : https://github.com/langgenius/dify

• RAGFlow

• Positioning : Enterprise-level RAG engine, supporting complex format document parsing and reference tracing
• Core features :
• Dynamic block segmentation and multi-way recall algorithm (BM25+ semantic retrieval)
  
• Industry template library (legal contracts, financial reports)
• Applicable scenarios : financial research report analysis, medical record processing
• Link : https://github.com/infiniflow/ragflow

• OpenWebUI

• Positioning : Self-hosted Web interactive platform, integrating models such as Ollama and OpenAI
• Core features :
• Multi-model competition comparison (Llama3 vs Qwen2)
  
• RBAC permission control and offline deployment
• Applicable scenario : Private LLM application development
• Link : https://github.com/open-webui/open-webui

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4. Extended Classification

Development Framework

1. LangChain

• Positioning : LLM application development framework, supporting Agent and complex process orchestration
• Link : https://github.com/langchain-ai/langchain

• DeepSpeed ​​(Microsoft)

• Positioning : A distributed training framework for hundreds of billions of models, supporting ZeRO graphics memory optimization
• Link : https://github.com/microsoft/DeepSpeed

Multimodal Generation Tools

1. Step-Video-T2V
• Positioning : 30 billion parameter video generation model, supporting 204-frame HD synthesis
• Link : https://modelscope.cn/models/step-video

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5. Summary and selection suggestions

Requirement Type	Recommended Tools	Core Advantages
Rapid prototyping	Dify + Hugging Face Model Library	Low-code, multi-model API integration
Enterprise-level knowledge base	RAGFlow + QAnything	Complex document analysis and result tracing
Multimodal Generation	Step Series + Magic Creation Space	Video/speech generation and industry adaptation
Local deployment	OpenWebUI + Ollama	Privacy security, multi-model collaboration

All of the above tools support open source protocols, and developers can choose according to computing resources (such as the 70B model requires an A100 cluster) and scenario requirements. For a complete list of projects, please refer to the model libraries of the MoDa community and Hugging Face .