import{s as J,n as Y,o as K}from"../chunks/scheduler.39a43cde.js";import{S as N,i as Q,g as s,s as i,r as I,A as V,h as r,f as a,c as o,j as Z,u as P,x as k,k as q,y as W,a as n,v as E,d as H,t as G,w as R}from"../chunks/index.23ec2204.js";import{H as O}from"../chunks/Heading.c42e7061.js";function X(U){let l,v,d,C,u,A,c,D=`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.`,$,m,L,f,F="Check out the recently added notebooks:",w,g,j='<li><a href="llm_judge">Using LLM-as-a-judge 🧑‍⚖️ for an automated and versatile evaluation</a></li> <li><a href="pipeline_notus_instructions_preferences_legal">Create a legal preference dataset</a></li> <li><a href="labelling_feedback_setfit">Suggestions for Data Annotation with SetFit in Zero-shot Text Classification</a></li> <li><a href="semantic_cache_chroma_vector_database">Implementing semantic cache to improve a RAG system</a></li> <li><a href="rag_llamaindex_librarian">Building A RAG Ebook “Librarian” Using LlamaIndex</a></li> <li><a href="stable_diffusion_interpolation">Stable Diffusion Interpolation</a></li> <li><a href="rag_with_hugging_face_gemma_mongodb">Building A RAG System with Gemma, MongoDB and Open Source Models</a></li> <li><a href="prompt_tuning_peft">Prompt Tuning with PEFT Library</a></li> <li><a href="tgi_messages_api_demo">Migrating from OpenAI to Open LLMs Using TGI’s Messages API</a></li> <li><a href="automatic_embedding_tei_inference_endpoints">Automatic Embeddings with TEI through Inference Endpoints</a></li> <li><a href="rag_zephyr_langchain">Simple RAG for GitHub issues using Hugging Face Zephyr and LangChain</a></li> <li><a href="faiss_with_hf_datasets_and_clip">Embedding multimodal data for similarity search using 🤗 transformers, 🤗 datasets and FAISS</a></li> <li><a href="fine_tuning_code_llm_on_single_gpu">Fine-tuning a Code LLM on Custom Code on a single GPU</a></li> <li><a href="rag_evaluation">RAG Evaluation Using Synthetic data and LLM-As-A-Judge</a></li> <li><a href="advanced_rag">Advanced RAG on HuggingFace documentation using LangChain</a></li> <li><a href="issues_in_text_dataset">Detecting Issues in a Text Dataset with Cleanlab</a></li> <li><a href="annotate_text_data_transformers_via_active_learning">Annotate text data using Active Learning with Cleanlab</a></li>',x,h,B='You can also check out the notebooks in the cookbook’s <a href="https://github.com/huggingface/cookbook" rel="nofollow">GitHub repo</a>.',T,p,y,_,z=`The Open-Source AI Cookbook is a community effort, and we welcome contributions from everyone!
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