Deep dive: Multi-Token by meta, HyperSD, KANs
Meta’s New Groundbreaking Paper on Multi-Token Prediction for Better and Faster LLMs Most current large language models are trained with
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Academic
Score Prediction from User Log with BERT
Discover latest project: predicting user scores from interaction logs with a quiz system! By analyzing user behavior and leveraging cutting-edge ML techniques like DistilBERT, curriculum learning, and LoRA, we’ve crafted a robust model to infer scores without needing correct answers or user choices. Dive into our innovative approach and see how we’re revolutionizing educational tools. #MachineLearning #AI #EdTech #Robotics #DistilBERT #CurriculumLearning #LoRA
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Deepdive: pytorch profiler, standford transformer, XTuner, Luminal, DeepFaceLive
The PyTorch Profiler analyzes deep learning models’ performance by collecting timing and resource usage stats, helping identify bottlenecks and optimize memory and execution. Stanford’s CS25 lecture series, “Transformers United V4,” covers state-of-the-art transformer research and applications. XTuner offers a flexible toolkit for fine-tuning large models, supporting various algorithms and high training throughput. Luminal optimizes deep learning performance with ahead-of-time compilation and efficient execution on CUDA/Metal APIs. DeepFaceLive allows real-time face swaps from video streams, with options to train custom models and animate static faces.
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TOP 15 local LLM: ranking and comparison
Curious about running AI locally? Discover 12 powerful tools to bring AI directly to your device. Whether you’re looking for speed, ease of setup, or open-source options, I’ve got you covered. Watch now to find the perfect LLM tool for you! 🚀 #AI #Tech #LLM #MachineLearning #Privacy #DIYAI
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Technow: Phi-3, Open LLM IoS app, Mini-Gemini, LLaMa-Factory
Microsoft is shaking things up with Phi-3, a series of open-source large language models (LLMs) designed for accessibility and performance. Available in mini, small, and medium sizes, these models run efficiently on both mobile devices and PCs. Let’s explore what makes Phi-3 unique!
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Technow: torchtune, Boston Dynamics Atlas robot, Reka Core
Easily fine-tune large language models (LLMs) with torchtune, PyTorch’s new library. Torchtune offers modular and customizable fine-tuning recipes, supporting efficient workflows from data preparation to model evaluation. It integrates with popular tools like Hugging Face Hub and Weights & Biases and excels in memory efficiency, making it ideal for users with limited hardware. Designed for simplicity and extensibility, torchtune supports models like Llama 2 and Mistral, with future plans for models up to 70 billion parameters.
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Deepdive: Many-Shot learning, AutoCrawler, Megalodon
Large language models (LLMs) are revolutionizing various fields, but their capabilities can be limited. This is where exciting new research comes in! By tackling challenges like limited learning from examples and information processing constraints, these advancements are making LLMs more adaptable, efficient, and powerful. Let’s dive into three key breakthroughs: Many-Shot In-Context Learning, AutoCrawler, and Megalodon.
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Deepdive: Custom Collate in DataLoader, Gemini cookbook, Build LLM from scratch
Optimize your large language model (LLM) training with PyTorch by using a custom collate function in DataLoader. This technique dynamically pads text sequences in each batch to match the longest sequence, reducing wasted computation from unnecessary padding. Learn how to implement a custom collate function using pad_sequence from torch.nn.utils.rnn and integrate it with DataLoader for efficient handling of variable-length sequences in your LLM training workflow.
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LLMs in Sequential Settings: Can They Predict? Step-by-Step (Part One)
In this video, we explore the potential of foundation models like BERT to predict sequences of event-based data. Using user interaction logs from a quiz system as a test case, we demonstrate how BERT can be applied to predict quiz scores based on sequences of user actions.
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Deep Dive: Multiprocessing with Pool, Pure C Implementation of GPT-2, Attention in Transformers
Unlock the power of Python’s multiprocessing library to execute tasks in parallel, effectively utilizing multiple CPU cores and bypassing the Global Interpreter Lock (GIL). By leveraging the Process and Pool classes, you can significantly reduce execution times for parallelizable tasks. Learn how to manage processes and facilitate inter-process communication through practical examples. Discover how multiprocessing can enhance your Python applications’ performance with this comprehensive guide.
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