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Demystifying Llama 2: A Comprehensive Tutorial on Retrieval Augmented Generation (RAG)

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Demystifying Llama 2: A Comprehensive Tutorial on Retrieval Augmented Generation (RAG)

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If you are interested in learning more about how to use Llama 2, a large language model (LLM), for a simplified version of retrieval augmented generation (RAG). This guide will help you utilize the power of Meta’s open source Llama 2, a model that boasts an impressive 13 billion parameters. Retrieval Augmented Generation (RAG) is a technique for generating text that combines the strengths of two different approaches: information retrieval and text generation.

Information retrieval involves finding relevant documents from a large corpus of text. This can be done using a variety of techniques, such as keyword matching, semantic similarity, and machine learning. Text generation involves creating new text, such as answering questions, writing stories, or generating creative text formats. This can be done using a variety of techniques, such as statistical language models, neural networks, and rule-based systems.

Retrieval Augmented Generation

RAG combines these two approaches by first using information retrieval to find relevant documents. These documents are then used to augment the text generation process, providing the model with additional context and information. This can help to improve the quality of the generated text, making it more factual, informative, and consistent.

Large language models are incredibly powerful tools. However, they can be somewhat limiting, as they only have access to the knowledge they learned during their training phase. This limitation can sometimes result in inaccurate information or ‘hallucinations’, as they are sometimes called.

Llama 2 RAG setup

To overcome these constraints, the implementing retrieval augmented generation (RAG). RAG essentially provides a window to the outside world for the LLM, making it more accurate and versatile. This is achieved by using natural language search, allowing the LLM to access relevant information about a question from external sources.

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A detailed walkthrough is provided in the video below on how to set up and use an embedding model. An embedding model is used to translate human-readable text into machine-readable vectors – a necessary step for implementing RAG. The tutorial will guide you on how to use the Sentence Transformers Library and the Hugging Face pipeline to initialize and load the embedding model. It also discusses creating a Vector database (or Vector index) using Pinecone, a vector database platform.

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Once you have your Vector database set up, the video shows how to populate it with data from papers related to Llama 2. After populating the Vector database, we proceed to load the LLM using the text generation pipeline from Hugging Face. Learn how to initialize a simple retrieval QA chain using Vector search and the loaded LLM. Upon completion of this step, you will have your Llama 2 model set up with retrieval augmented generation.

RAG has been shown to be effective for a variety of tasks, including question answering, summarization, and creative writing. It is a promising technique for generating text that is both informative and engaging.

Benefits of using RAG

Improved factual consistency: RAG can help to improve the factual consistency of generated text by providing the model with access to external knowledge sources. This is especially important for tasks that require factual accuracy, such as question answering.
Increased reliability: RAG can help to increase the reliability of generated text by providing the model with multiple sources of information. This can help to mitigate the problem of “hallucination,” where the model generates text that is not supported by any of the available sources.
More creative text: RAG can help to generate more creative text by providing the model with a wider range of possibilities. This is because the model can draw on both the retrieved documents and its own internal knowledge to generate text.

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Overall, RAG is a promising technique for generating text that is both informative and engaging. It is a valuable tool for a variety of tasks, and it is likely to become even more powerful as the underlying technologies continue to improve.

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