import{s as z,n as K,o as Y}from"../chunks/scheduler.65852ee5.js";import{S as W,i as X,g as r,s as i,r as H,A as Z,h as s,f as a,c as o,j as N,u as S,x as $,k as Q,y as ee,a as n,v as R,d as D,t as I,w as F}from"../chunks/index.aa74147d.js";import{H as O,E as te}from"../chunks/EditOnGithub.4eda6a96.js";function ae(j){let l,w,b,v,u,C,c,q=`The Open-Source AI Cookbook is a collection of notebooks illustrating practical aspects of building AI
applications and solving various machine learning tasks using open-source tools and models.`,y,m,x,h,U="Check out the recently added notebooks:",L,g,V='<li><a href="phoenix_observability_on_hf_spaces">Phoenix Observability Dashboard on HF Spaces</a></li> <li><a href="multiagent_web_assistant">Have several agents collaborate in a multi-agent hierarchy 🤖🤝🤖</a></li> <li><a href="semantic_reranking_elasticsearch">Semantic reranking with Elasticsearch</a></li> <li><a href="benchmarking_tgi">Benchmarking TGI</a></li> <li><a href="generate_preference_dataset_distilabel">Generate a Preference Dataset with distilabel</a></li> <li><a href="clean_dataset_judges_distilabel">Clean an Existing Preference Dataset with LLMs as Judges</a></li> <li><a href="rag_with_unstructured_data">Building RAG with Custom Unstructured Data</a></li> <li><a href="agent_rag">Agentic RAG: turbocharge your RAG with query reformulation and self-query! 🚀</a></li> <li><a href="agent_change_llm">Create a Transformers Agent from any LLM inference provider</a></li> <li><a href="fine_tuning_llm_to_generate_persian_product_catalogs_in_json_format">Fine-tuning LLM to Generate Persian Product Catalogs in JSON Format</a></li> <li><a href="agent_text_to_sql">Agent for text-to-SQL with automatic error correction</a></li> <li><a href="information_extraction_haystack_nuextract">Information Extraction with Haystack and NuExtract</a></li> <li><a href="rag_with_hf_and_milvus">RAG with Hugging Face and Milvus</a></li> <li><a href="agent_data_analyst">Data analyst agent: get your data’s insights in the blink of an eye ✨</a></li> <li><a href="rag_with_knowledge_graphs_neo4j">Enhancing RAG Reasoning with Knowledge Graphs</a></li> <li><a href="fine_tuning_detr_custom_dataset">Fine-Tuning Object Detection on a Custom Dataset 🖼, Deployment in Spaces, and Gradio API Integration</a></li> <li><a href="semantic_segmentation_fine_tuning_inference">Fine-Tuning a Semantic Segmentation Model on a Custom Dataset and Usage via the Inference API</a></li> <li><a href="multimodal_rag_using_document_retrieval_and_vlms">Multimodal Retrieval-Augmented Generation (RAG) with Document Retrieval (ColPali) and Vision Language Models (VLMs)</a></li> <li><a href="fine_tuning_vlm_trl">Fine-Tuning a Vision Language Model (Qwen2-VL-7B) with the Hugging Face Ecosystem (TRL)</a></li>',A,f,B='You can also check out the notebooks in the cookbook’s <a href="https://github.com/huggingface/cookbook" rel="nofollow">GitHub repo</a>.',M,_,P,p,J=`The Open-Source AI Cookbook is a community effort, and we welcome contributions from everyone!
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