Zack Akil

Cerebras and Neural Magic have reached a significant milestone in the realm of large language models (LLMs). Through innovative techniques and purpose-built hardware, we achieved up to 70% parameter reduction in LLMs without sacrificing accuracy. This breakthrough opens doors for more efficient training and deployment, democratizing access to LLMs across various sectors. #AI #MachineLearning #Innovation

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Machine Learning Engineer - Founding Team - London £200k If you answer yes to 4/5 of these questions, then you want to check out this job (link in comments) 1️⃣ Do you have experience with quantization techniques (e.g., PEFT, DeepSpeed, TensorRT) to optimize ML models? 2️⃣  Have you ever managed or worked with a multi-node HPC cluster? 3️⃣ Are you familiar with Deep Learning using PyTorch Library? 4️⃣ Do you have experience with Kubernetes container orchestration? 5️⃣  Have you worked with some of the recent Large Language Models (e.g., OpenAI, LLaMA)? #LLM #ArtificialIntelligence #Quantization

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Here’s a summary of "The Super Weight in Large Language Models": Definition of Super Weight: In large language models (LLMs), "super-weights" refer to extremely large parameters or learned values that significantly influence the model's performance and output. Role of Weights in LLMs: Weights in LLMs are the parameters that are learned during training, determining how input data (like text) is transformed into output (like predictions or generated text). Significance of Super Weights: Some weights are disproportionately influential, contributing more to the model’s accuracy and ability to generate coherent and contextually relevant text. Weight Distribution: LLMs often exhibit an uneven distribution of importance among weights, where a small subset of super weights has a major impact on model behavior. Training Challenges: Identifying and optimizing these super weights during training can be computationally demanding, as it involves fine-tuning billions of parameters to maximize the model’s performance. Sparse Activation: In some models, only a fraction of weights (including super weights) are actively involved in each decision or prediction, leading to more efficient computation but also a focus on key weights. Weight Pruning: Techniques like weight pruning aim to reduce the model’s size and complexity by removing less important weights while retaining the super weights that have the most influence on outcomes. Interpretability: Super weights are central to understanding LLM behavior, as they can explain why certain outputs are generated. Analyzing them is crucial for model interpretability and debugging. Transfer Learning and Super Weights: When using pre-trained models, super weights play a crucial role in adapting the model to specific tasks, as they hold learned representations that transfer across domains. Impact on Efficiency: Efficient management of super weights is key to improving LLM performance, reducing training costs, and enhancing real-time inference, particularly for large-scale applications.

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Choosing the Right Batch Size for Optimal Large Language Model Training Performance. Selecting the right batch size is critical for optimizing the training of large language models (LLMs). Here's a quick guide on how to make that choice, especially when dealing with fully connected layers and utilizing GPU hardware like NVIDIA. Matrix Multiplication in LLMs: In large language models, matrix dimensions are defined by: - Batch Size (B) - Input Shape (I) - Output Shape (O) Efficient training occurs when these matrix dimensions align with your GPU's architecture, allowing for faster training and better memory utilization. Key Considerations: 1. GEMM (General Matrix Multiply) Optimization: GPUs are optimized for matrix multiplications when sizes are powers of 2 or multiples of 8, 16, 32, or 64. Aligning batch size and neuron counts with these multiples can lead to significant performance gains, particularly with FP16 precision on Tensor Cores for large models like transformers. 2. Batch Size Calculation for LLMs: Choose batch sizes such that B×I and B×O are multiples of the optimal tile sizes (powers of 2 or multiples of 8, 16, 32, or 64, depending on the hardware). B×IB \times I B×OB \times O For example, if you have 128 input neurons and 64 output neurons, you might start with batch sizes like 32, 64, or 128 to ensure that the resulting matrix dimensions (B × 128 and B × 64) align with hardware-optimized dimensions. 3. Avoid Dimension Quantization Effects: Using smaller batch sizes or neuron counts that don’t align with optimized sizes can lead to inefficiencies known as dimension quantization effects, leading to underutilization of your GPU's capabilities. Why Powers of 2 and Multiples of 8? Many hardware architectures, especially GPUs, are optimized to process data in sizes that are powers of 2 or multiples of 8, 16, 32, or 64. This is because: GPUs process data in chunks (or "tiles"), and these sizes fit neatly into their memory structure and processing cores. Aligning your batch size and matrix dimensions to these powers improves memory access patterns, reduces padding, and maximizes throughput during matrix multiplication. Optimizing batch size is key to scaling and efficiently training large language models. Experimenting with batch sizes and adjusting to hardware architecture will help you strike the right balance between performance and memory efficiency. #AI #DeepLearning #LargeLanguageModels #MachineLearning #GPUs #Optimization #BatchSize #GEMM #NVIDIA #DataScience

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📢 DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming Heads🚀 In this work, they tackle the significant memory and computational challenges associated with deploying long-context large language models (LLMs). The proposed framework, DuoAttention, introduces a novel distinction between Retrieval Heads and Streaming Heads to optimize the use of the KV cache, reducing memory usage by up to 2.55× and improving decoding speeds by up to 2.18×, all while maintaining high accuracy. Technical Highlights: 1. Retrieval Heads vs. Streaming Heads: DuoAttention divides attention heads into two categories. Retrieval Heads are crucial for processing long contexts, whereas Streaming Heads focus on recent tokens, allowing for a more efficient use of memory resources. This division helps in reducing the computational load without compromising model accuracy. 2. KV Cache Optimizatio: The framework uses selective caching strategies, applying full KV cache to Retrieval Heads while maintaining a constant-length cache for Streaming Heads. This results in significant reductions in both memory usage and latency during long-context inference. 3. Scalable Long-Context Inference: With DuoAttention, models like Llama-3-8B can handle up to 3.3 million tokens on a single A100 GPU. This is made possible through careful optimization of memory allocation and the use of lightweight algorithms to identify the critical heads. 4. Performance Improvements: DuoAttention achieves up to 2.55× memory reduction for multi-head attention (MHA) models and up to 1.67× for grouped-query attention (GQA) models. It also speeds up decoding by up to 2.18× for MHA models and up to 1.50× for GQA models, providing significant benefits for real-world applications involving large language models. 5. Compatibility with Quantization: The method is fully compatible with quantization techniques, such as 8-bit weight and 4-bit KV cache quantization. This compatibility further boosts efficiency, enabling the model to handle larger contexts with reduced hardware requirements. With DuoAttention, the team has achieved long-context capabilities for models like Llama-3-8B, enabling handling of up to 3.3 million tokens on a single A100 GPU. This advancement marks a significant step towards more efficient, scalable LLM deployments! 🔍 Paper: https://lnkd.in/dDFHimSA #LLMs #MachineLearning # #EfficientInference

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Tomas Vondra: [PATCH IDEA] parallel pgbench -i Hey PostgreSQL enthusiasts! 🚀 Ever sat waiting for pgbench to load its dataset, wondering if you’ll grow old before the benchmarks even start? Yeah, me too. Tomas Vondra gets it – he's about to become our time-saving hero with his new patch idea. The man’s on a mission to extend pgbench -i for parallel data loading. Imagine multiple clients generating and sending data simultaneously. It's like turning a slow-moving train into a bullet train. 🔥 In Tomas’s words, pgbench is a great tool, but let’s be honest, the initial data loading is painfully serial. Like, 1990s dial-up internet kind of slow. This tweak would cut that wait time and get you straight into the juicy benchmarking stuff faster than you can say "VACUUM FULL." So shoutout to Tomas Vondra for steering this ship towards faster horizons. If you’re keen to geek out more, hit up the link in the description above. May your queries be efficient and your loading times brief! 💪 #PostgreSQL #pgbench #DatabasePerformance #DataNerd ✌️, [Your Name] You can find the full article here: https://postgr.es/p/6E3

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Ready to harmonize with the future of music ? I'm excited to introduce MelodyNet-AI-Music-Composer, an open-source project that unleashes the creative potential of Artificial Intelligence (AI) in music composition! _Project Description:_ MelodyNet-AI-Music-Composer is an innovative project that harnesses the power of AI to generate musical compositions. This repository includes a set of algorithms and tools that use machine learning techniques to create original music pieces. The project aims to: - Explore the intersection of AI and music - Develop novel music generation algorithms - Create a community-driven platform for music composition _Key Features:_ - AI-powered music composition - Algorithmic music generation - Open-source and community-driven - Modular architecture for easy contributions _Contribute:_ 1. Star the repository to show your support 2. Fork the repository to make your own changes 3. Submit a pull request to merge your changes 4. Report any issues or bugs _Repository link:_ https://lnkd.in/dwykvw38 Join the melody revolution and let's create something amazing together! #OpenSource #AImusic #MusicComposition #Collaboration #Innovation #GitHub #Contribute #MusicTech #ArtificialIntelligence #MachineLearning"

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People rightly state that there is a lot of hype surrounding #AI. This reflects our obsession with applying new tools to *every* problem, fit for purpose or not. 😄 Here's the good part, though: when applied properly, AI is a huge force multiplier. Imagine a lever ^ 1000000. I've been sharing, any time I have the opportunity, the "good parts": areas in which AI can have real utility now. Not someday. Not maybe. Absolutely yes, and now. One area in particular is #RAG, Retrieval Augmented Generation. RAG == Turning an AI loose on YOUR DATA. Imagine all of those documents in which vital information is squirreled away, hard to search, even harder to correlate *across* those myriad documents. What a treasure trove if only you could leverage it! With RAG, you can...and pretty easily, too. Using AI and RAG, 1+1 pretty quickly adds up to 500. Or more. Even now, the power of your AI-enabled application is more limited by your imagination than the capabilities of the tools. If you want to know more, get just a taste of what you can do with AI using RAG, join me this Monday at #RAGHack. See below for more details and to save your spot! https://lnkd.in/gYAsiSP4

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💡 Introducing Pype's Prompt Optimizer Check out the demo video to build leaner and effective prompts in minutes using Pype. 2 months back, when we started building Pype, we knew that Test-Driven Development (TDD) can be a game-changer for building reliable AI agents. At Pype, we've developed a Prompt Optimizer to streamline this TDD approach. How Pype helps you? 📝 Draft and Test Prompts 🤖 Auto-Enhance Without Overfitting 📊 Version and Compare Prompts 🚢 Deploy and track performance live With a step by step approach, it's intuitive and systematic. Plus, you can: ➕ Add Context Blocks for clarity 🌡 Experiment with Advanced Prompting Techniques 🎛 Switch between Different LLM Models 📨 Generate Test Cases even without production data 🏷 Annotate samples and add feedbacks Ready to build killer LLM apps? Visit www.pypeai.com to try it out. It's free to use. For complex use cases, we offer personalized onboarding support. 💼 Who's Using It? Companies are already leveraging Pype for tasks like RFP triaging, content moderation, and AI tutoring. If you are building an AI agent, give Pype a try 🚀 We would love to hear back from you.

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Machine Learning Engineer - Founding Team - London £200k If you answer yes to 4/5 of these questions, then you want to check out this job (link in comments) 1️⃣ Do you have experience with quantization techniques (e.g., PEFT, DeepSpeed, TensorRT) to optimize ML models? 2️⃣  Have you ever managed or worked with a multi-node HPC cluster? 3️⃣ Are you familiar with Deep Learning using PyTorch Library? 4️⃣ Do you have experience with Kubernetes container orchestration? 5️⃣  Have you worked with some of the recent Large Language Models (e.g., OpenAI, LLaMA)? #LLM #ArtificialIntelligence #Quantization

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I got a cool question on GitHub today. Last week, I published "Helibrunna", a Hugging Face compatible trainer for xLSTM. I got the question: "Can be used for time series forecasting?" Let me answer this here too, because the question is cool. Helibrunna is made for Causal Language Modeling, aka next-token prediction a la GPT. Thus it is in the field of NLP. It cannot be used for time-series forecasting. The language model in Helibrunna is fairly close to the GPT architecture we so love. But instead of multi-head attention decoder blocks it uses xLSTM blocks. Like decoders, those xLSTM blocks can be applied to anything that looks like a multivariate time series. TL;DR: While Helibrunna is not built for time-series forecasting, xLSTM can be used to solve that problem.

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🚀 Just spent some time diving deep into Retrieval-Augmented Generation (RAG) for LLMs, and it’s been eye-opening! For those who aren’t familiar, RAG combines the strengths of traditional information retrieval with generative capabilities, allowing models to “look up” relevant information in a large database before crafting a response. This means we’re not just relying on the model’s pre-trained “memory,” but also dynamically fetching information to enrich responses. The result? Way more relevant and informed outputs, especially for specialized or up-to-date topics. Here’s where I see some real potential: 1. Customer Support Automation: Imagine a chatbot that can instantly access a company’s entire support knowledge base, user manuals, and past customer interactions. Instead of relying on generic, canned answers, it could generate responses tailored to the user’s specific question and product version—leading to faster, more accurate support. 2. Personalized Education: In the field of e-learning, RAG can pull from vast educational content databases, offering tailored explanations based on a learner’s background. Whether it’s math, language learning, or science, this could help create a personalized tutor experience that adapts in real-time to each student’s needs and knowledge gaps. 3. Legal & Compliance Research: For industries like law or finance, where accurate referencing is crucial, a RAG-powered LLM could retrieve and cite relevant cases, regulations, or compliance guidelines while drafting responses or reports. It could save hours of manual research and improve accuracy by drawing on real-time, verified data sources. 4. Healthcare Support: A tool for healthcare professionals where RAG enables retrieval of the latest medical studies, treatment protocols, or drug interactions. Imagine a doctor getting instant recommendations based on the most recent research without having to sift through mountains of publications. 5. Content Creation & Ideation: Need fresh ideas? RAG can look up trending topics, recent publications, and customer insights to help marketers and content creators generate ideas that are relevant to current audience interests, backed up by solid references. Why this is so exciting… RAG’s ability to bridge generative AI with real-time, relevant information retrieval feels like the next step in making LLMs truly practical and innovative. It opens up possibilities for high-stakes applications where accuracy and context are critical—and where generative models alone may fall short. Really looking forward to experimenting further with RAG in my projects. The future of LLMs is looking more dynamic than ever! 🔥

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How C++, Java, and Python Work Under the Hood? Compiled languages like C++ and Go transform source code into machine code using a compiler before execution. The compiled machine code can then be directly executed by the CPU. Java first compiles source code into bytecode, which is platform-independent and executed by the Java Virtual Machine (JVM). The JVM can further boost performance using Just-In-Time (JIT) compilation to convert bytecode to machine code at runtime. Interpreted languages like Javascript and Ruby don't undergo compilation. Instead, their code is processed line-by-line by an interpreter during execution. However, modern JavaScript engines like V8 also utilize JIT compilation for enhanced performance. Python is a mix of both worlds. It first compiles source code into platform-independent bytecode, which is then executed line-by-line by the platform-dependent interpreter. In addition, implementations like PyPy use JIT compilation for a speed boost. Generally, compiled languages can offer speed advantages, but the line between compiled and interpreted languages is increasingly blurred thanks to modern optimization techniques. – Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): https://bit.ly/496keA7

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How C++, Java, and Python Work Under the Hood? Compiled languages like C++ and Go transform source code into machine code using a compiler before execution. The compiled machine code can then be directly executed by the CPU. Java first compiles source code into bytecode, which is platform-independent and executed by the Java Virtual Machine (JVM). The JVM can further boost performance using Just-In-Time (JIT) compilation to convert bytecode to machine code at runtime. Interpreted languages like Javascript and Ruby don't undergo compilation. Instead, their code is processed line-by-line by an interpreter during execution. However, modern JavaScript engines like V8 also utilize JIT compilation for enhanced performance. Python is a mix of both worlds. It first compiles source code into platform-independent bytecode, which is then executed line-by-line by the platform-dependent interpreter. In addition, implementations like PyPy use JIT compilation for a speed boost. Generally, compiled languages can offer speed advantages, but the line between compiled and interpreted languages is increasingly blurred thanks to modern optimization techniques. -- Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): https://bit.ly/496keA7

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Google has launched Gemini 2.0 to attempt to build a 'universal assistant'. "With new advances in multimodality — like native image and audio output — and native tool use, it will enable building new AI agents that bring us closer to our vision of a universal assistant." https://lnkd.in/eFZQFu5i

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🤗 Hugging Face: Powering the Future of AI 🤗 🌟 Hugging Face has become a game-changer in open-source AI, making complex machine learning accessible to everyone. 🖋️ Hugging Face Hub is a platform that hosts publicly available and open-source models, datasets, and apps. ML Researchers and Devs collaborate on models, datasets and applications. What sets it apart is its vibrant community, driving collaboration and innovation in AI. With its comprehensive ecosystem—including the Transformers library, Datasets, Spaces, and Inference API—Hugging Face empowers developers to leverage cutting-edge NLP models and tools seamlessly. The Hugging Face community is breaking new ground, democratizing AI, and creating endless possibilities for the future. Photo source: Hugging Face 🤗 #MachineLearning #AI #HuggingFace

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Nice to see Generative AI moving rapidly into the video space with OpenAI's #Sora making news with Hollywood creatives and now Microsoft's VASA being able to generate lifelike talking faces and natural head motions from a single image. This is approximating a solution for a very ill posed problem and the pace of progress is amazing. Excited to see what we can do at #Adobe with this new progress to elevate the expression of creativity. Find the paper here: https://lnkd.in/gECGCqj2 #GenerativeAI #VASA #Microsoft #OpenAI #GenAI

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🚀 ViTPose Joins Hugging Face Transformers Transform your projects with the best open-source model for human pose estimation. ➡ Why ViTPose is a Game-Changer: - Easy integration into Hugging Face Transformers. - Start using it with just a few lines of code. - Proven performance in real-world demos. 📹 See it in action!: Check out the videos created by Sora from OpenAI. What are your thoughts on this? Comment down below 👇 #ai #transformers #genai #opensource #tech #trend

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