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2 | 2 | sections:
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3 | 3 | - local: index
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4 | 4 | title: Open-Source AI Cookbook
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5 |
| - - local: issues_in_text_dataset |
6 |
| - title: Detecting Issues in a Text Dataset with Cleanlab |
7 |
| - - local: stable_diffusion_interpolation |
8 |
| - title: Stable Diffusion Interpolation |
9 |
| - - local: rag_with_hugging_face_gemma_mongodb |
10 |
| - title: Building A RAG System with Gemma, MongoDB and Open Source Models |
11 |
| - - local: tgi_messages_api_demo |
12 |
| - title: Migrating from OpenAI to Open LLMs Using TGI's Messages API |
| 5 | + |
| 6 | +- title: LLM Recipes |
| 7 | + sections: |
13 | 8 | - local: automatic_embedding_tei_inference_endpoints
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14 | 9 | title: Automatic Embeddings with TEI through Inference Endpoints
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15 |
| - - local: faiss_with_hf_datasets_and_clip |
16 |
| - title: Embedding multimodal data for similarity search |
| 10 | + - local: tgi_messages_api_demo |
| 11 | + title: Migrating from OpenAI to Open LLMs Using TGI's Messages API |
| 12 | + - local: advanced_rag |
| 13 | + title: Advanced RAG on HuggingFace documentation using LangChain |
| 14 | + - local: labelling_feedback_setfit |
| 15 | + title: Suggestions for Data Annotation with SetFit in Zero-shot Text Classification |
17 | 16 | - local: fine_tuning_code_llm_on_single_gpu
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18 | 17 | title: Fine-tuning a Code LLM on Custom Code on a single GPU
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| 18 | + - local: prompt_tuning_peft |
| 19 | + title: Prompt tuning with PEFT |
| 20 | + - local: rag_evaluation |
| 21 | + title: RAG Evaluation |
| 22 | + - local: llm_judge |
| 23 | + title: Using LLM-as-a-judge for an automated and versatile evaluation |
| 24 | + |
| 25 | +- title: Diffusion Recipes |
| 26 | + sections: |
| 27 | + - local: stable_diffusion_interpolation |
| 28 | + title: Stable Diffusion Interpolation |
| 29 | + |
| 30 | +- title: Multimodal Recipes |
| 31 | + sections: |
| 32 | + - local: faiss_with_hf_datasets_and_clip |
| 33 | + title: Embedding multimodal data for similarity search |
| 34 | + |
| 35 | +- title: LLM and RAG recipes with other Libraries |
| 36 | + sections: |
| 37 | + - local: issues_in_text_dataset |
| 38 | + title: Detecting Issues in a Text Dataset with Cleanlab |
| 39 | + - local: annotate_text_data_transformers_via_active_learning |
| 40 | + title: Annotate text data using Active Learning with Cleanlab |
| 41 | + - local: rag_with_hugging_face_gemma_mongodb |
| 42 | + title: Building A RAG System with Gemma, MongoDB and Open Source Models |
19 | 43 | - local: rag_zephyr_langchain
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20 | 44 | title: Simple RAG using Hugging Face Zephyr and LangChain
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21 | 45 | - local: rag_llamaindex_librarian
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22 | 46 | title: RAG "Librarian" Using LlamaIndex
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23 |
| - - local: advanced_rag |
24 |
| - title: Advanced RAG on HuggingFace documentation using LangChain |
25 |
| - - local: rag_evaluation |
26 |
| - title: RAG Evaluation |
27 |
| - - local: prompt_tuning_peft |
28 |
| - title: Prompt tuning with PEFT |
29 |
| - - local: labelling_feedback_setfit |
30 |
| - title: Suggestions for Data Annotation with SetFit in Zero-shot Text Classification |
31 | 47 | - local: pipeline_notus_instructions_preferences_legal
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32 | 48 | title: Create a legal preference dataset
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33 | 49 | - local: semantic_cache_chroma_vector_database
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34 | 50 | title: Implementing semantic cache to improve a RAG system.
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35 |
| - - local: annotate_text_data_transformers_via_active_learning |
36 |
| - title: Annotate text data using Active Learning with Cleanlab |
37 |
| - - local: llm_judge |
38 |
| - title: Using LLM-as-a-judge for an automated and versatile evaluation |
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