diff --git a/notebooks/en/_toctree.yml b/notebooks/en/_toctree.yml index 63c706f0..72d9c2da 100644 --- a/notebooks/en/_toctree.yml +++ b/notebooks/en/_toctree.yml @@ -2,37 +2,49 @@ sections: - local: index title: Open-Source AI Cookbook - - local: issues_in_text_dataset - title: Detecting Issues in a Text Dataset with Cleanlab - - local: stable_diffusion_interpolation - title: Stable Diffusion Interpolation - - local: rag_with_hugging_face_gemma_mongodb - title: Building A RAG System with Gemma, MongoDB and Open Source Models - - local: tgi_messages_api_demo - title: Migrating from OpenAI to Open LLMs Using TGI's Messages API + +- title: LLM Recipes + sections: - local: automatic_embedding_tei_inference_endpoints title: Automatic Embeddings with TEI through Inference Endpoints - - local: faiss_with_hf_datasets_and_clip - title: Embedding multimodal data for similarity search + - local: tgi_messages_api_demo + title: Migrating from OpenAI to Open LLMs Using TGI's Messages API + - local: advanced_rag + title: Advanced RAG on HuggingFace documentation using LangChain + - local: labelling_feedback_setfit + title: Suggestions for Data Annotation with SetFit in Zero-shot Text Classification - local: fine_tuning_code_llm_on_single_gpu title: Fine-tuning a Code LLM on Custom Code on a single GPU + - local: prompt_tuning_peft + title: Prompt tuning with PEFT + - local: rag_evaluation + title: RAG Evaluation + - local: llm_judge + title: Using LLM-as-a-judge for an automated and versatile evaluation + +- title: Diffusion Recipes + sections: + - local: stable_diffusion_interpolation + title: Stable Diffusion Interpolation + +- title: Multimodal Recipes + sections: + - local: faiss_with_hf_datasets_and_clip + title: Embedding multimodal data for similarity search + +- title: LLM and RAG recipes with other Libraries + sections: + - local: issues_in_text_dataset + title: Detecting Issues in a Text Dataset with Cleanlab + - local: annotate_text_data_transformers_via_active_learning + title: Annotate text data using Active Learning with Cleanlab + - local: rag_with_hugging_face_gemma_mongodb + title: Building A RAG System with Gemma, MongoDB and Open Source Models - local: rag_zephyr_langchain title: Simple RAG using Hugging Face Zephyr and LangChain - local: rag_llamaindex_librarian title: RAG "Librarian" Using LlamaIndex - - local: advanced_rag - title: Advanced RAG on HuggingFace documentation using LangChain - - local: rag_evaluation - title: RAG Evaluation - - local: prompt_tuning_peft - title: Prompt tuning with PEFT - - local: labelling_feedback_setfit - title: Suggestions for Data Annotation with SetFit in Zero-shot Text Classification - local: pipeline_notus_instructions_preferences_legal title: Create a legal preference dataset - local: semantic_cache_chroma_vector_database title: Implementing semantic cache to improve a RAG system. - - local: annotate_text_data_transformers_via_active_learning - title: Annotate text data using Active Learning with Cleanlab - - local: llm_judge - title: Using LLM-as-a-judge for an automated and versatile evaluation diff --git a/notebooks/en/index.md b/notebooks/en/index.md index 4b2dae65..b22cc465 100644 --- a/notebooks/en/index.md +++ b/notebooks/en/index.md @@ -10,20 +10,6 @@ Check out the recently added notebooks: - [Using LLM-as-a-judge 🧑‍⚖️ for an automated and versatile evaluation](llm_judge) - [Create a legal preference dataset](pipeline_notus_instructions_preferences_legal) - [Suggestions for Data Annotation with SetFit in Zero-shot Text Classification](labelling_feedback_setfit) -- [Implementing semantic cache to improve a RAG system](semantic_cache_chroma_vector_database) -- [Building A RAG Ebook "Librarian" Using LlamaIndex](rag_llamaindex_librarian) -- [Stable Diffusion Interpolation](stable_diffusion_interpolation) -- [Building A RAG System with Gemma, MongoDB and Open Source Models](rag_with_hugging_face_gemma_mongodb) -- [Prompt Tuning with PEFT Library](prompt_tuning_peft) -- [Migrating from OpenAI to Open LLMs Using TGI's Messages API](tgi_messages_api_demo) -- [Automatic Embeddings with TEI through Inference Endpoints](automatic_embedding_tei_inference_endpoints) -- [Simple RAG for GitHub issues using Hugging Face Zephyr and LangChain](rag_zephyr_langchain) -- [Embedding multimodal data for similarity search using 🤗 transformers, 🤗 datasets and FAISS](faiss_with_hf_datasets_and_clip) -- [Fine-tuning a Code LLM on Custom Code on a single GPU](fine_tuning_code_llm_on_single_gpu) -- [RAG Evaluation Using Synthetic data and LLM-As-A-Judge](rag_evaluation) -- [Advanced RAG on HuggingFace documentation using LangChain](advanced_rag) -- [Detecting Issues in a Text Dataset with Cleanlab](issues_in_text_dataset) -- [Annotate text data using Active Learning with Cleanlab](annotate_text_data_transformers_via_active_learning) You can also check out the notebooks in the cookbook's [GitHub repo](https://github.com/huggingface/cookbook).