Enhancing Language Generation and Natural Language Search with Better Llama 2 and Retrieval Augmented Generation (RAG)
Learn about Better Llama 2 and how to utilize Retrieval Augmented Generation (RAG) for improved language generation and natural language search.
00:00:00 Exploring retrieval augmented generation using the 13 billion parameter Llama 2 model, fitting it onto a single T4 GPU for easy access. This allows the LM to have knowledge beyond its training, resulting in improved language generation.
🦙 Retrieval augmented generation using the llama 2 model and a single T4 GPU.
💡 Lamas have limited knowledge and access to the outside world.
🔍 Retrieval augmented generation gives lamas access to a subset of the outside world through natural language searching.
00:03:01 This video explores the use of retrieval augmented generation (RAG) in the context of natural language search. It discusses the use of embedding models and open source libraries for creating document embeddings.
🔸 Using natural language for search and retrieval allows for accessing relevant information based on semantic meaning.
🔸 The embedding model translates human-readable text into machine-readable vectors for performing semantic-based searches.
🔸 The use of an open-source model, specifically the Sentence Transformers Library, enables efficient and accessible embedding creation.
00:06:03 Learn about Better Llama 2 and how to create a Vector database using Retrieval Augmented Generation (RAG).
📌 Performance of opening eye embeddings depends on the use case.
🔑 A Pinecone API key is needed to create a Vector database and index.
🗄️ Initializing the index to store vectors with the specified dimensionality and metric.
🔍 Populating the Vector database to enable retrieval of stored items.
00:09:06 This video showcases the process of creating a database using a small dataset and converting it into a pandas data frame. It also demonstrates how to add the Llama2 model using the text generation pipeline from Hugging Face.
📑 The video's transcription discusses the creation of a small dataset containing chunks of text from the Llama 2 paper and related papers.
📊 The dataset is converted into a pandas data frame and uploaded to Pinecone in batches of 32, with the option to increase the batch size.
🔎 The LM model, Llama2, is added to the database using the text generation pipeline from Hugging Face.
00:12:08 Learn how to utilize Better Llama 2 with Retrieval Augmented Generation (RAG) for natural language processing tasks with a step-by-step guide.
📚 Loading the model and getting the home face authentication token.
💻 Switching the model to evaluation mode and checking GPU usage.
💡 Initializing the retrieval QA chain for LMS and confirming its functionality.
00:15:09 A summary of the YouTube video 'Better Llama 2 with Retrieval Augmented Generation (RAG)' is that Llama 2 is a collection of pre-trained large language models optimized for dialogue and outperforming open source chat models on most benchmarks tested. The video also discusses the use of retrieval augmentation and safety measures in the development of Llama 2.
🔍 The retrieval augmented generation (RAG) pipeline improves document retrieval.
💡 Llama 2 is a collection of pre-trained large language models optimized for dialogue.
🔒 Safety measures in the development of Llama 2 include pre-training, fine-tuning, and model safety approaches.
00:18:12 Retrieval augmented generation is a powerful technique that improves the performance of local LMs like llama 2. It allows the LM to provide better answers and answer questions about up-to-date topics or internal documents.
🔍 Retrieval augmented pipeline improves performance and safety of Llama 2
🚀 Llama 2 outperforms other local language models in helpfulness and safety benchmarks
💡 Retrieval augmentation allows LM to answer questions on up-to-date topics and internal documents
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