MistyWest’s Post

Here is a visualization of #LoRA that I made a few weeks ago for our knowledge sharing at K2 AI. LoRA (𝘓𝘰𝘸-𝘙𝘢𝘯𝘬 𝘈𝘥𝘢𝘱𝘵𝘢𝘵𝘪𝘰𝘯 𝘰𝘧 𝘓𝘢𝘳𝘨𝘦 𝘓𝘢𝘯𝘨𝘶𝘢𝘨𝘦 𝘔𝘰𝘥𝘦𝘭𝘴) is quite a simple but very powerful method for efficient fine-tuning of #LLMs that enables fine-tuning models like #LLama3 on customer-grade GPU Here is how LoRA works in 4 simple steps: 1. 𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐋𝐚𝐲𝐞𝐫𝐬 𝐟𝐨𝐫 𝐀𝐝𝐚𝐩𝐭𝐚𝐭𝐢𝐨𝐧: Choose specific layers in a pre-trained model (e.g., attention layers in Transformers) that are crucial for the new task. 2. 𝐃𝐞𝐜𝐨𝐦𝐩𝐨𝐬𝐞 𝐖𝐞𝐢𝐠𝐡𝐭𝐬: Rather than adjusting the entire weight matrix, LoRA decomposes weight changes into two smaller matrices, A and B. The change in weights is represented as the product of A and the transpose of B. 3. 𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐢𝐳𝐚𝐭𝐢𝐨𝐧 & 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠: Only matrices A and B are trained, keeping the original weights fixed. This dramatically reduces the number of trainable parameters. 4. 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧: Post-training, matrices A and B are used to compute the updates to the weights, adjusting the original weights to better suit the new task. If you want more visual explanations of LoRa and other AI papers I very much recommed this post https://lnkd.in/dUniajah by Tom Yeh and his One Takeaway by Hand ✍️series

In the fast-paced realm of AI and machine learning, deploying models efficiently is as crucial as their development. As a deployment engineer, I've had firsthand experience with the power of TensorRT in streamlining this process. Here’s a glimpse into how TensorRT revolutionizes AI inferencing, based on experiment outcomes.   ✨ Key Highlights:   •Framework Compatibility: TensorRT enhances our ability to deploy models from diverse frameworks like TensorFlow, Keras and Pytorch, thanks to its support for ONNX, facilitating a smoother transition from training to deployment.   •Optimized Performance: Our tests with the COCO dataset on an A2000 GPU showcased TensorRT's ability to significantly boost inferencing speed, nearly doubling FPS compared to baseline ONNX models. This performance leap is a game-changer for real-time AI applications.      •Deep Dive into Efficiency: Analysis revealed that despite TensorRT engines requiring more time to load due to their larger size—attributable to advanced optimizations such as layer fusion and precision calibration—the resultant speed in inferencing is unmatched. These engines are fine-tuned for specific GPU architectures, ensuring peak performance.   •Precision vs. Speed: Exploring the trade-offs between FP16 and FP32 precision, we observed that while FP16 might slightly reduce accuracy, the impact is minimal. However, the efficiency gains in terms of FPS are substantial, making FP16 a viable option for many applications prioritizing speed.   •Practical Insights: Through benchmarking YOLO models, we've learned the importance of GPU-specific engine creation and version compatibility. These insights are invaluable for anyone looking to optimize their AI deployments for specific hardware.   💡 Final Thoughts: TensorRT isn't merely a tool but a gateway to AI's future. Embracing it enables unlocking model potential, pushing boundaries, and enhancing AI's accessibility, efficiency, and potency.   Let's continue to innovate and transform the landscape of AI together.   #AI #Deployment #TensorRT #MachineLearning #DeepLearning #Technology #Innovation

🤔 🚗 Curious about the future of driver monitoring technology? Watch this driver monitoring AI/CV processing pipeline demo using the Lattice Avant-E development kit: https://bit.ly/3vPJ88h

How to identify defects in panel-level packages, and why that's needed for generative AI in data centers

AI curious? Me too! This deck from John Bailey is by far the most useful thing I've found for wrapping my head around current AI tools and how one might use them in your everyday life. Maybe you will find it helpful too? https://lnkd.in/dRaie6HA

🚀 Exploring AI Optimization: Beyond Model Quantization In the realm of AI (LLM) model training, quantizing models is often heralded as a solution to save memory and enhance efficiency. But is it enough? 🤔 Understanding the Memory Challenge During typical backpropagation, both model weights and input tensors are stored as Float16 or BFloat16, which are manageable during the forward pass. However, the real challenge arises during the backward pass, where gradients also need to be stored in Float16 or BFloat16. 🔍 Memory Dynamics During Optimization Optimization steps bring additional complexities: 1. Model parameters in Float16 2. Gradients in Float16 and Float32 3. Momentum and variance in Float32 4. Model parameters again in Float32 As Float32 consumes twice as much memory as Float16, the optimizer state requires 8X more memory than the model parameters themselves! Even with quantized parameters, memory requirements stay high due to the need for dequantization during forward and backward passes and optimization computations in Float32. 💡 The Big Question: How can we minimize the optimizer memory spike when quantizing isn’t enough? 🌟 Solution: QLoRA Introducing QLoRA: QLoRA is an enhanced version of LoRA that quantizes the weight parameters of pre-trained LLMs to 4-bit precision. While trained model parameters are usually stored in a 32-bit format, QLoRA compresses them to a 4-bit format. This drastically reduces the memory footprint of the LLM, enabling fine-tuning on a single GPU. This approach significantly lowers the memory requirements, making it feasible to run LLM models on less powerful hardware, including consumer GPUs. QLoRA is a game-changer in minimizing optimizer memory spikes. By ensuring gradient updates occur only on LoRA adapters and buffering the optimizer state to CPU RAM if necessary, QLoRA significantly reduces memory usage. Join the discussion on how QLoRA and other innovations can revolutionize AI optimization! What are your thoughts on this approach? Have you encountered similar challenges? Let's connect and explore the future of AI together. #AI #MachineLearning #DeepLearning #AIOptimization #ModelTraining #QLoRA #TechInnovation #LLM

How to identify defects in panel-level packages, and why that’s needed for generative AI in data centers.

Wondering what to do with the data you collect as you progress along your #digitalfactory journey? Get a hold of World Wide Technology and see what is possible for your #data with our AI Proving Grounds. World Wide Technology Manufacturing Solutions #wwtmfg #manufacturing #industry #smartfactory #ai #digitalmanufacturing https://lnkd.in/eq_EwFyP

NVIDIA has just unveiled an incredible advancement in Generative AI with a groundbreaking new tool called LATTE3D! 🚀 This revolutionary AI model can swiftly turn basic text descriptions into top-notch 3D models with remarkable speed and quality. 💣 Key Features: 📐 Text-to-3D conversion ⚡ Lightning-fast performance (even for complex models in under 1 second!) 🌟 High visual fidelity 💾 Standardized output format LATTE3D represents a significant breakthrough, making it possible for anyone to effortlessly bring their 3D ideas to life without the need for specialized modeling skills. 🎨 The future of 3D creation is truly being democratized with this game-changing innovation! 🌌

Take a look at our recent Blog post on the limits of AI current hardware and how Non-Von is part of the solution: https://lnkd.in/erXwxcVD While there join our newsletter! #AI #innovation #AIchips #Defensetech