LILT’s Post

Unleashing document intelligence: your search sidekick! 🚀   Dive into the world of document intelligence that decodes language, tailors results, and turbocharges your search game. Check out our latest blog, " How does document intelligence augments human capabilities?” and join the adventure! 🔗Find the link in the comments. #documentintelligence #searchsuperpowers #AIadventures 

How can you speed up the post editing process without compromising on quality? Learn in this upcoming webinar. Thu, 7/25, 8 AM PDT https://buff.ly/3RKxNOm

📊 Website Improvement Tip 👏 Optimize your website for voice search by using natural language and answering common questions.

pub.towardsai.net: The content discusses the challenge of managing the overwhelming amount of text in today's fast-paced world, which leaves little time for reading. It addresses the need for effective strategies to navigate through the abundance of textual information.

Not so advanced concept of RAG: The LongContextReorder is a postprocessor that aims to improve the performance of Retrieval Augmented Generation (RAG) models by reordering the retrieved context. This advanced RAG technique positions crucial information at the start or end of the input context, as language models tend to struggle accessing significant details in the middle of extended passages. The LongContextReorder postprocessor leverages this insight to enhance the context provided to the language model during generation. By strategically reordering the retrieved nodes, it helps ensure that the most relevant information is more accessible to the model, leading to more accurate and coherent responses. (This also concludes my week long exploration of advanced RAG techniques.) Code: https://lnkd.in/g53U8RDX #rag #genai #ragconcepts

You know that brainstorming part when you have to work on a video? It's not rocket science! Here my latest video on how work on a video before I press record. https://lnkd.in/e3Bds6sf

Semantic Chunking. Kids are so smart. I do my best to keep pace with them, but they continually impress me with their innovative ideas, such as Semantic Chunking—a concept I wish I had thought of myself. Semantic Chunking involves dividing text into segments that are relevant to each other. This method serves as a preprocessing step for creating a more effective and efficient Retrieval-Augmented Generation (RAG) system. I recently incorporated this technique into our system, and it has significantly enhanced our search results. Surprisingly, the method is not complex. I'm truly appreciative of the brilliant young minds who keep developing such innovative solutions.

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🔍 **Exploring Mistral 7B-v0.3: An In-Depth Tutorial** A new video delves into the functionalities and features of the Mistral 7B-v0.3 model. The video serves as both an overview and a hands-on tutorial. ### Highlights: - **Performance Benchmarks:** The video starts with a thorough analysis of Mistral 7B-v0.3 benchmarks, illustrating its capabilities. - **New Features:** Viewers can learn about the latest updates and enhancements in this version, making it more robust and efficient. - **Hugging Face Integration:** Detailed instructions are provided for using the Mistral AI model via the Hugging Face platform. - **Practical Implementation:** The 'Code Time' segment is dedicated to actual coding, followed by a step-by-step guide on running Mistral 7B-v0.3 as a pipeline. - **Inference Package:** Comprehensive details on Mistral's inference package for better model deployment and usage. For those interested in advanced implementations, additional resources include Colab code and links to GitHub resources featuring LangChain and LLM tutorials. #AI #MachineLearning #DataScience #Mistral7B #HuggingFace ---------------------- Learn more here: https://lnkd.in/eSjRNP96

MultiRay was presented at the PyTorch Conference 2023! Here's a video of Michael delivered a compelling lightning talk: https://lnkd.in/daG2Khyk MultiRay has successfully democratized state-of-the-art content understanding models at Meta, making them cost-effective and accessible. Proud to have collaborated with an exceptional team to bring this project to life. Quick recap: By converting inputs into a highly useful and substantial embedding, MultiRay allows for cost-sharing across multiple downstream tasks. This approach significantly reduces computation costs, enabling the development of smaller, budget-friendly task specific models. For more details, read our blog post: https://lnkd.in/dsJuY5ea