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Little know gem: the Open-source Cookbook
Little know gem: the Open-source Cookbook
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A collection of notebooks for building practical AI applications using open-source tools and models: 
A collection of notebooks for building practical AI applications using open-source tools and models: 
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https://lnkd.in/e6m6Jmwu
https://lnkd.in/e6m6Jmwu
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Doc: 
Doc: 
link
https://lnkd.in/e3FE6TUq
https://lnkd.in/e3FE6TUq
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Currently contains 16 notebooks in English (and some in Chinese):
Currently contains 16 notebooks in English (and some in Chinese):
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	1.	Using LLM-as-a-judge 🧑‍⚖️ for an automated and versatile evaluation
	1.	Using LLM-as-a-judge 🧑‍⚖️ for an automated and versatile evaluation
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	2.	Create a legal preference dataset
	2.	Create a legal preference dataset
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	3.	Suggestions for Data Annotation with SetFit in Zero-shot Text Classification
	3.	Suggestions for Data Annotation with SetFit in Zero-shot Text Classification
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	4.	Implementing semantic cache to improve a RAG system
	4.	Implementing semantic cache to improve a RAG system
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	5.	Building A RAG Ebook “Librarian” Using LlamaIndex
	5.	Building A RAG Ebook “Librarian” Using LlamaIndex
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	6.	Stable Diffusion Interpolation
	6.	Stable Diffusion Interpolation
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	7.	Building A RAG System with Gemma, MongoDB and Open Source Models
	7.	Building A RAG System with Gemma, MongoDB and Open Source Models
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	8.	Prompt Tuning with PEFT Library
	8.	Prompt Tuning with PEFT Library
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	9.	Migrating from OpenAI to Open LLMs Using TGI’s Messages API
	9.	Migrating from OpenAI to Open LLMs Using TGI’s Messages API
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	10.	Automatic Embeddings with TEI through Inference Endpoints
	10.	Automatic Embeddings with TEI through Inference Endpoints
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	11.	Simple RAG for GitHub issues using Hugging Face Zephyr and LangChain
	11.	Simple RAG for GitHub issues using Hugging Face Zephyr and LangChain
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	12.	Embedding multimodal data for similarity search using 🤗 transformers, 🤗 datasets and FAISS
	12.	Embedding multimodal data for similarity search using 🤗 transformers, 🤗 datasets and FAISS
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	13.	Fine-tuning a Code LLM on Custom Code on a single GPU
	13.	Fine-tuning a Code LLM on Custom Code on a single GPU
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	14.	RAG Evaluation Using Synthetic data and LLM-As-A-Judge
	14.	RAG Evaluation Using Synthetic data and LLM-As-A-Judge
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	15.	Advanced RAG on HuggingFace documentation using LangChain
	15.	Advanced RAG on HuggingFace documentation using LangChain
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	16.	Detecting Issues in a Text Dataset with Cleanlab
	16.	Detecting Issues in a Text Dataset with Cleanlab
Little know gem: the Open-source Cookbook

A collection of notebooks for building practical AI applications using open-source tools and models: https://lnkd.in/e6m6Jmwu

Doc: https://lnkd.in/e3FE6TUq

Currently contains 16 notebooks in English (and some in Chinese):
	1.	Using LLM-as-a-judge 🧑‍⚖️ for an automated and versatile evaluation
	2.	Create a legal preference dataset
	3.	Suggestions for Data Annotation with SetFit in Zero-shot Text Classification
	4.	Implementing semantic cache to improve a RAG system
	5.	Building A RAG Ebook “Librarian” Using LlamaIndex
	6.	Stable Diffusion Interpolation
	7.	Building A RAG System with Gemma, MongoDB and Open Source Models
	8.	Prompt Tuning with PEFT Library
	9.	Migrating from OpenAI to Open LLMs Using TGI’s Messages API
	10.	Automatic Embeddings with TEI through Inference Endpoints
	11.	Simple RAG for GitHub issues using Hugging Face Zephyr and LangChain
	12.	Embedding multimodal data for similarity search using 🤗 transformers, 🤗 datasets and FAISS
	13.	Fine-tuning a Code LLM on Custom Code on a single GPU
	14.	RAG Evaluation Using Synthetic data and LLM-As-A-Judge
	15.	Advanced RAG on HuggingFace documentation using LangChain
	16.	Detecting Issues in a Text Dataset with Cleanlab
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