Ashish Patel 🇮🇳’s Post

🚀 Top 10 AI Coding Assistants Every Developer Should Know! In the digital era, AI is revolutionizing the way we write and debug code. Leveraging AI coding assistants can not only save you time but also enhance your coding skills. Here are the top 10 tools that are changing the game: 1. Tabnine : https://lnkd.in/dvbwYm57 Elevate your coding with AI-powered code completions, supporting over 20 programming languages. Its unique feature is adapting to your coding style, making code suggestions more relevant and efficient. 2. Codiga: https://lnkd.in/dpUnKPJV Real-time error checking and automated code fixes across multiple languages, Codiga is perfect for maintaining high-quality, secure codebases. Its comprehensive analysis rules help you stay ahead of potential issues. 3. Replit: https://lnkd.in/d2AkGQJY A collaborative platform with a focus on learning, Replit's Ghostwriter offers contextually relevant code suggestions to improve efficiency and code quality, ideal for both beginners and seasoned developers. 4. GitHub Copilot: https://lnkd.in/d6HCnfYr Favored by developers worldwide, GitHub Copilot writes high-quality code from natural language prompts and integrates smoothly across major IDEs. It's great for automating routine tasks and learning new languages. 5. Kite: https://lnkd.in/diN8JKc7 AI that integrates directly into your IDE, providing smart completions and documentation for Python and JavaScript. Its CoPilot feature helps you navigate complex codebases by suggesting relevant patterns. 6. DeepCode: https://lnkd.in/dHWh4mrb This tool uses AI to review your codebase, identifying bugs and security vulnerabilities before they become a problem, supporting a proactive approach to code quality. 7. IntelliCode: https://lnkd.in/d2B2H4Rj Microsoft's IntelliCode uses machine learning to provide intelligent suggestions that improve over time, adapting to how you write code in apps like Visual Studio. 8. Sourcegraph: https://lnkd.in/dCSZZfHA Navigate your codebase with ease using AI-powered search and intelligence, making it easy to understand complex systems and improve your workflow. 9. AI Codex by OpenAI: https://lnkd.in/dp24ziQT Provides context-aware code completions and explanations, helping you understand complex code and speeding up the development process. 10. Sourcery: https://lnkd.in/dSyPEAwM Instantly refactor your code to improve readability and efficiency with AI-driven recommendations tailored to your style. 👩💻 Whether you're debugging solo or building the next big thing, these tools are here to supercharge your coding mojo! Embrace AI, boost your productivity, and push the boundaries of what you can achieve.

MLOps Hands on Project Tutorials Session 1 : https://lnkd.in/dgwCSFdv Session 2 : https://lnkd.in/djtu_Xxu

Exploring 𝗬𝗢𝗖𝗢: A Novel Approach to Efficient Language Modeling 𝗧𝗵𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲: • As language models grow larger, managing memory demands and processing speeds has become a major challenge • This often hinders practical applications and scalability 𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: YOCO - A Revolutionary Decoder-Decoder Architecture • Redefines efficiency without sacrificing performance 🏆 𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: • Unique 'Cache Once' Strategy ➜ Caches key-value pairs just once, reducing redundancy ➜ Promotes computational efficiency • Dual Decoder Design ➜ Self-decoder efficiently encodes global KV caches ➜ Cross-decoder reuses pre-computed KV caches via cross-attention • Memory Mastery ➜ Substantially reduces GPU memory demands ➜ Retains global attention capability • Prefill Prowess ➜ Computation flow enables prefilling to early exit ➜ Significantly speeds up prefill stage for long input sequences 𝗧𝗵𝗲 𝗜𝗺𝗽𝗮𝗰𝘁: • Performance Powerhouse ➜ Favorable performance compared to Transformer models ➜ Scales up model size and training data effectively • Context Champion ➜ Extended to handle 1M context length ➜ Near-perfect needle retrieval accuracy • Efficiency Extraordinaire ➜ Orders of magnitude improvements in:     ➜ Inference memory     ➜ Prefill latency     ➜ Throughput across context lengths and model sizes 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: • From real-time language understanding to complex multi-modal tasks • Offers promising solution for scenarios with memory and speed bottlenecks • Paves way for more agile and cost-effective models 𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗕𝗲𝗰𝗸𝗼𝗻𝘀: • A valuable step towards sustainable and scalable AI technologies • Unique caching strategy could inspire:   ➜ Further research into memory-efficient designs   ➜ Specialized hardware implementations • Propels us into an era of optimized and efficient AI systems Paper: You Only Cache Once: Decoder-Decoder Architectures for Language Models   Code: https://lnkd.in/dJWQKEMy P.S. Get ready to embrace the YOCO revolution and join the journey towards creating more efficient, powerful, and practical AI solutions!

Microsoft has release the Visualizing the Future: How VoT(Visulization of thought) Prompting is Revolutionizing AI's Spatial Reasoning Skills! 🌌 This groundbreaking approach aims to provide large language models (LLMs) with a cognitive "mind's eye," allowing them to visualize and manipulate spatial information akin to human thought processes. 🔍 The Fundamentals ✨ VoT prompting equips LLMs with a "visuospatial sketchpad" concept, mirroring how humans process and utilize spatial data. ✨ It enhances LLMs' abilities to create and manipulate mental images for complex spatial reasoning tasks.   ✨ Early results show VoT prompting outperforming traditional methods in challenges like natural language navigation and visual tiling in synthetic environments. 🔬 Advanced Capabilities ✨ VoT has demonstrated superior performance compared to multimodal LLMs in navigating 2D grids and solving spatial puzzles. ✨ Empirical validations highlight LLMs' newfound skills in visual state tracking and adapting to inconsistencies when using VoT prompting. ✨ This approach focuses on leveraging LLMs' potential to develop cognitive-like visualization abilities for spatial reasoning. 🤯 Surprising Implications ✨ Despite being an early-stage technique, VoT prompting has already showcased how AI can develop human-like cognitive capabilities. ✨ Insights suggest LLMs using VoT can autonomously refine their reasoning approaches, hinting at the emergence of self-awareness in LLMs systems. The VoT prompting breakthrough presents a unique opportunity to integrate cognitive-inspired processes into LLMs, unlocking advanced spatial reasoning skills across various applications. As this technology evolves, we may witness AI systems developing increasingly human-like reasoning and decision-making abilities. P.S 🔥 Explore how your organization can leverage the power of VoT prompting to push the boundaries of spatial reasoning in your LLMs solutions.

Visualizing the Future: How VoT Prompting is Revolutionizing AI's Spatial Reasoning Skills! 🌌 An innovative method called Visualization-of-Thought (VoT) prompting is setting the stage for a revolution in AI's ability to reason spatially, akin to human cognitive processes. Let's explore how! 🌱 THE BASICS → VoT prompting enhances LLMs by enabling a 'mind's eye' visualization, drastically improving spatial reasoning. → It uses a 'visuospatial sketchpad' concept, mirroring how humans process and utilize spatial information. → VoT has outperformed traditional methods in tasks like natural-language navigation and visual tiling in synthetic environments. 🤓 THE ADVANCED → The approach focuses on LLMs' ability to create and manipulate mental images for complex spatial reasoning. → It shows superior performance in navigating 2D grids and solving spatial puzzles over multimodal LLMs (MLLMs). → Empirical validations highlight LLMs’ capabilities in visual state tracking and adapting to inconsistencies. 🥷 THE SURPRISING → Despite being in nascent stages, VoT prompting has already shown how AI can develop cognitive-like capabilities. → Insights suggest that LLMs can refine their reasoning approaches autonomously, hinting at emerging self-awareness. 👇 ACT NOW! 👇 Consider the potential of integrating VoT techniques into your AI systems to unlock advanced spatial reasoning capabilities. Start by rethinking how AI can benefit from cognitive-like processes to enhance performance across various applications. And don’t forget to like this post ❤️ if you found it useful. I do appreciate it! 🥐

IBM Launches Granite - A Game-Changing Family of Open Source Code Models 🚀 IBM is releasing its powerful Granite code models to the open source community, aiming to make coding accessible to as many developers as possible. Here are the key highlights: 𝗪𝗵𝗮𝘁'𝘀 𝗡𝗲𝘄? ⤷ Granite is a family of high-performing code language models ranging from 3B to 34B parameters ⤷ Models cover 116 programming languages and excel at code generation, fixing bugs, explaining code, and more ⤷ Available in base and instruction-following variants for different use cases 𝗞𝗲𝘆 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀: ✅ Outperforms existing open source models on major coding benchmarks  ✅ Optimized for enterprise software development workflows ✅ Offers cost-efficient options ideal for commercial use cases ✅ Trained on high-quality data adhering to IBM's AI ethics principles 𝗣𝗼𝘄𝗲𝗿𝗶𝗻𝗴 𝗮 𝗡𝗲𝘄 𝗘𝗿𝗮 𝗼𝗳 𝗖𝗼𝗱𝗶𝗻𝗴 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆 With Granite models, developers can build innovative tools to streamline their workflow: 🛠 Automated code writing from plain English instructions 🐞 Code debugging and issue fixing assistants  🔄 Legacy application modernization and code translation 📝 Automated documentation and unit test generation ☁️ And much more, limited only by our creativity! IBM believes open innovation is key to realizing the full potential of generative AI for coding. By open sourcing Granite, IBM are empowering the global community to build transformative solutions. 🔗 Source: https://lnkd.in/dcdj2gGR P.S. The future of software development is here - let's embrace the Granite revolution! 🌍💻

🌟 Taxonomy of LLM Compression: A Simple Guide to Making AI Models Smaller and Faster 🚀 Ever wondered how we make those massive AI models like GPT-4 smaller and faster without losing much of their smarts? Let's break down some key techniques in simple English: ✨ Quantization Imagine you're trying to save space by packing your clothes more efficiently. Quantization does something similar with LLMs models, making them smaller without losing too much detail. ★ PTQ (Post-Training Quantization): Like cleaning out your closet after you've moved, PTQ reduces the size of the model after it's been trained. ★ QAT (Quantization-Aware Training): This is like training your model to expect a smaller wardrobe. It prepares the model for quantization during training. ★ AWQ (Adaptive Weight Quantization): Think of this as choosing which clothes to keep based on how often you wear them. AWQ focuses on the most important parts of the model. ★ GGUF (Grouped Generalized Unstructured Floating Point): Imagine grouping similar clothes together. GGUF does something similar with LLMs model weights, making storage more efficient. ★ GGML (Grouped Generalized Mixed Precision): This is like having a mix of clothing sizes. GGML allows for a blend of precision levels, giving more control over storage. 🌳 Pruning Now, imagine you're deciding which clothes to give away because you don't wear them anymore. Pruning does something similar with AI models, removing unnecessary parts. ★ Knowledge Distillation: Like teaching your younger sibling everything you know, this technique transfers knowledge from a larger model to a smaller one. ★ Filter Pruning: Imagine removing filters from your camera lens to make it lighter. Filter pruning removes unnecessary filters from the model. Activation Pruning: This is like deciding which photos to keep based on their quality. Activation pruning removes less important activations from the model. 🎨 Model Simplification Imagine simplifying your life by doing fewer things. ★ Model simplification does something similar with AI models, making them simpler and faster. ★ Model Parallelism: Like splitting tasks among family members, model parallelism splits the model's work across multiple devices. ★ Model Compression: This is like packing your suitcase more efficiently. Model compression reduces the model's size without losing too much functionality. 🛠️ Other Techniques There are also other cool tricks to make AI models more efficient: ★ Transformer Pruning: Imagine removing unnecessary parts from a transformer robot. This technique removes parts of transformer models that aren't needed. ★ QAF (Quantization-Aware Fine-Tuning): Like fine-tuning your wardrobe based on the latest fashion trends, QAF adjusts the model after quantization to ensure it still performs well. P.S These techniques help us make LLMs models more efficient, saving space and speeding up processing times. It's like upgrading your server without buying a new one 🚀

Optimisation is biggest problem in Large Language Model when we want to use that for real-world condition So I found one interesting book that help us to understand how we can do mathematical optimization with python. Learning Resource: https://lnkd.in/dFji3W5D P.S Learning this will help you to understand the concept like how quantization, PEFT, LORA all are contributing.