helvia.ai’s Post

Are bigger models always better? - Not necessarily! What are SLMs? They challenge the 'bigger is better' idea in NLP Range from a few million to a few billion parameters Highly effective in specialized tasks and resource-constrained environments   🛠️ What Can SLMs Do? Versatile: From sentiment analysis to code generation. Perfect for edge devices and mobile apps. Think Google's Gemini Nano and Microsoft's Orca series!   💡 Why Choose SLMs Over LLMs? Specialization: Tailored to specific domains or tasks. Faster Processing: Ideal for real-time applications. Cost-effective: More accessible for smaller organizations and research groups.   🌟 Popular SLMs to Watch: · Llama 2 by Meta AI · Mistral and Mixtral by Mistral AI · Microsoft's Phi and Orca · Alpaca 7B by Stanford researchers · StableLM series by Stability AI Uncover a wealth of information at https://lnkd.in/e4PDC2DK and broaden your understanding. #SLMs #AI #Innovation #SmallLanguageModels #NaturalLanguageProcessing #SpecializedTasks #ResourceConstrained #EdgeDevices #MobileApplications #CostEffective #TailoredPerformance #RealTimeApplications #CompactAI #sundarinfographicanalytics

#LLMOpsSeries #Day9 🔍 LLMOps Unveiled: Feature Engineering from Text Data 🔍 In today's LLMOps post, we’ll explore various methods for feature engineering from text data. These techniques are essential for extracting meaningful features that enhance the performance of large language models. TF-IDF (Term Frequency-Inverse Document Frequency): Measuring the importance of words in a document relative to a corpus. This helps in identifying key terms that are significant within the text. Word Embeddings: Representing words as dense vectors using techniques like Word2Vec, GloVe, or FastText. These embeddings capture semantic relationships between words. N-grams: Extracting contiguous sequences of n words to capture context. This technique helps in understanding the sequence and co-occurrence of words. POS Tagging (Part of Speech Tagging): Identifying parts of speech to enrich text data with syntactic information. This aids in understanding the grammatical structure of the text. Named Entity Recognition (NER): Identifying and categorizing entities such as names, dates, and locations. This helps in extracting and utilizing specific information from the text. By applying these feature engineering techniques, you can enhance the input data’s expressiveness, leading to better performance and insights from your large language models. Stay tuned for our next post, where we’ll discuss the main challenges when developing LLMs for production. #LLMOps #AI #MachineLearning #DataScience #ArtificialIntelligence

Are bigger models always better? - Not necessarily! What are SLMs? They challenge the 'bigger is better' idea in NLP Range from a few million to a few billion parameters Highly effective in specialized tasks and resource-constrained environments   🛠️ What Can SLMs Do? Versatile: From sentiment analysis to code generation. Perfect for edge devices and mobile apps. Think Google's Gemini Nano and Microsoft's Orca series!   💡 Why Choose SLMs Over LLMs? Specialization: Tailored to specific domains or tasks. Faster Processing: Ideal for real-time applications. Cost-effective: More accessible for smaller organizations and research groups.   🌟 Popular SLMs to Watch: ·        Llama 2 by Meta AI ·        Mistral and Mixtral by Mistral AI ·        Microsoft's Phi and Orca ·        Alpaca 7B by Stanford researchers ·        StableLM series by Stability AI Uncover a wealth of information at https://lnkd.in/e8Z7ZnmF and broaden your understanding. #SLMs #AI #Innovation #SmallLanguageModels #NaturalLanguageprocessing #Specializedtasks #Resourceconstrained #Edgeddevices #Mobileapplications #Costeffective #Tailoredperformance #Realtimeapplications #CompactAI #sundarinfographicanalytics

Graph RAG — A Conceptual Introduction Retrieval Augmented Generation (RAG) has dominated the discussion around making Gen AI applications useful since ChatGPT’s advent exploded the AI hype. The idea is simple. LLMs become especially useful once we connect them to our private data. A foundational model, that everyone has access to, combined with our domain-specific data as the secret sauce results in a potent, unique tool. Just like in the human world, AI systems seem to develop into an economy of experts. General knowledge is a useful base, but expert knowledge will work out your AI system’s unique selling proposition. RAG itself does not yet describe any specific architecture or method. It only depicts the augmentation of a given generation task with an arbitrary retrieval method. The original RAG paper (Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks by Lewis et. al.) compares a two-tower embedding approach with bag-of-words retrieval. A Graph RAG pipeline will usually follow the following steps: * Graph Extraction * Graph Storage * Community detection * Community report generation * Map Reduce for final context building Jakob Pörschmann provides a helpful conceptual introduction to Graph RAG. Link in comments. For the latest applications and innovation around #KnowledgeGraph #GraphDB Graph #AI #DataScience #MachineLearning #SemTech #EmergingTech #GenAI #LLM #RAG, #CDL24 is your go-to event, community and knowledge hub: https://lnkd.in/gYR7Z9gf

DocuTalk AI is a cutting-edge RAG (Retrieval-Augmented Generation) engine designed for deep document comprehension. It has two main functions: 1. Key value extractor - Extracting all the relevant information in form of key value phraser for quick document over 2. Question Answering - Question answering with Vector DB with Large Language Models. Here is a Gradio app for its demo. Here are the wonderful courses, that helped me to build DocuTalk AI. 1. Building Generative AI Applications with @Gradio in partnership with Hugging Face by Apolinário Passos (Poli) https://lnkd.in/g7gBJnZW 2. Building Applications with Vector Databases by Tim Tully https://lnkd.in/g26Vtu4F Finally, Heart full thanks to Andrew Ng and team of DeepLearning.AI for wonderful courses. I have attached the git repo. So please feel to checkout and enhance it. https://lnkd.in/g8xwEmsV #generativeai #ai #rag #agi

New research from Meta AI (FAIR) introduces RLEF! 💡 This new approach teaches code LLMs to use execution feedback, boosting their performance significantly. Here's how RLEF works: 🔄 Iterative Code Synthesis: The LLM is prompted repeatedly to generate code based on a problem description. 🧪 Public and Private Tests: Each code generation is evaluated on public tests, and feedback (error messages, test results) is provided to the LLM. If the code passes public tests, it's evaluated on private, held-out tests for reward calculation. 🧠 Reinforcement Learning: The LLM learns to leverage execution feedback and improve its code over multiple turns using Proximal Policy Optimization (PPO). Key Findings: 🏆 State-of-the-art results: RLEF achieved new SOTA results on the CodeContests benchmark using Llama 3.1 models (8B and 70B parameters), outperforming previous methods like AlphaCode, AlphaCodium and MapCoder. 📉 Improved Sample Efficiency: RLEF reduced the required number of LLM generations by an order of magnitude, making it more computationally efficient. 🧠 Effective Feedback Utilization: Analysis shows that RLEF-trained LLMs effectively leverage feedback to fix errors and generate more diverse and targeted code edits. Paper: https://lnkd.in/gMJnrGJ5 #LLM #CodeGeneration #ReinforcementLearning #AI #CodeSynthesis #SoftwareEngineering #MachineLearning #DeepLearning #RLEF #Llama #CodeContests #RLHF #Lllama3

🔍 Dive into the Future of Search with Building RAG Applications!🔍 led by Chris Sanchez, this hands-on, project-based course will immerse you in the cutting-edge world of building vector-search applications integrated with Generative Large Language Models (LLMs). Learn the best practices for text preprocessing, vectorization, indexing, and reranking using Weaviate. You'll also explore keyword-based vs. semantic-based search and use industry-standard metrics for benchmarking. By the end, you'll integrate everything with ChatGPT-Turbo-3.5 for advanced Question Answering, all wrapped up in a sleek Streamlit interface. Ready to take your search skills to the next level? Enroll now! 🚀 #VectorSearch #LLM #GenerativeAI #TechCourses #AI #MachineLearning #SearchTechnology

⏳ 𝐒𝐚𝐯𝐞 𝐇𝐨𝐮𝐫𝐬 𝐰𝐢𝐭𝐡 𝐀𝐈 in AI Bytes Issue 31! ⚡💡 Kickstart your workflow with our 𝐀𝐈 𝐓𝐢𝐩 𝐨𝐟 𝐭𝐡𝐞 𝐖𝐞𝐞𝐤—five hacks to 𝐬𝐮𝐩𝐞𝐫𝐜𝐡𝐚𝐫𝐠𝐞 𝐲𝐨𝐮𝐫 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲! Then, dive into the cutting-edge of AI: 𝐐𝐰𝐞𝐧𝟐-𝐌𝐚𝐭𝐡 is redefining mathematical reasoning, 𝐀𝐧𝐭𝐡𝐫𝐨𝐩𝐢𝐜 is raising the bar with a new model safety bug bounty, and 𝐑𝐞𝐧𝐝𝐨𝐫𝐚’𝐬 text-to-3D video platform is pushing creative boundaries. Plus, Rico explores OpenAI's voice tech that’s blurring the lines between human and machine. Whether you’re an AI enthusiast or just curious, this issue is packed with insights you can’t afford to miss. Let’s shape the future together! Check it out: https://lnkd.in/gW46yxBi #AIBytes #Qwen2Math #AI #Innovation #Anthropic #Rendora #AIConferences #AITips #AIDevelopment #MachineLearning #ArtificialIntelligence #TechNews #ProductivityHacks

🌟 Exploring the Power of Finetuning in Large Language Models (LLMs) 🌟 In the world of Generative AI and machine learning, finetuning is an essential step that takes a general-purpose, pre-trained model and refines it for specific tasks or industries. 🚀 What is Finetuning? LLMs are initially trained on vast datasets (web, wiki, books, etc.), which give them a broad understanding of language. However, to make them more accurate and effective for specific needs, finetuning comes into play. It’s the process of training these models further on domain-specific data, enabling them to provide more precise and context-aware responses. 🤖 How does it work? A base LLM is pre-trained on a massive dataset, making it good at general knowledge tasks. Through finetuning with an organization’s or domain-specific dataset, the LLM is further optimized for specific needs. The resulting finetuned LLM becomes more effective at understanding and generating responses tailored to that particular domain. 🔍 Whether you're working with legal documents, medical data, or customer service queries, finetuning can make AI-powered tools much more relevant and powerful. Feel free to share your thoughts on the significance of finetuning or how it's benefiting your field! #AI #MachineLearning #LLM #Finetuning #DataScience #GenerativeAI #Innovation #Technology

Once you've worked with traditional Machine Learning, you won't be a part of Gen AI and LLM Rat Race. I feel every problem doesn't require LLM's at the back few things could be solved using traditional approches as well. In many cases, traditional approaches can be just as effective, if not others. Recently, I came across a codebase where an LLM-based approach was used to analyze textual data and assign concise tags based on the title and description of various documents. While LLMs can be incredibly powerful, this solution seemed over-engineered for the tasks in hand. To address this, I decided to take a step back and apply some classic machine learning techniques. By using a combination of traditional NLP with NLTK for text processing, TF-IDF for vectorization, and DBSCAN for clustering, I was able to simplify the solution while maintaining high accuracy and significantly improving computational efficiency. This experience reminded me that while cutting-edge technologies are impressive, sometimes the traditional methods are more suitable. It's all about using the right tool for the right job! What are your thoughts on it? #genai #llms #ratrace #machinelearning