Sebastian Raschka guides users in fine-tuning a small GPT model to classify SPAM messages with 96% accuracy. ScrapeGraphAI is a Python library that automates data extraction from websites using LLMs. And Sepp Hochreiter’s xLSTM architecture extends traditional LSTMs to compete with state-of-the-art Transformers. These innovations are making AI more accessible and efficient! 🚀🤖📚
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In this video, I break down the code behind designing an RL agent with an Actor-Critic architecture using a prioritized replay buffer! 🤖💻 Discover how to tackle sparse rewards, optimize training efficiency, and boost your model’s performance with practical tips and WandB tracking. If you want to go beyond theory and see how to implement these concepts in code, this is the video for you! Check it out and level up your RL skills today!
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https://www.youtube.com/watch?v=iodNcqh3B-M
Struggling with sparse rewards in your RL projects? Discover how we used the ACER algorithm, reward shaping, and prioritized experience replay to boost our agent’s performance. 🚀 Watch my latest video to learn how to prevent policy collapse and achieve stable learning in complex environments. #ReinforcementLearning #AI #SparseRewards”
Meet DrEureka, an LLM agent that trains robots in simulation, and Nvidia’s Llama3-ChatQA-1.5, excelling in conversational question answering. Also, Maxime Labonne’s Meta-Llama-3-120B-Instruct merges multiple instances to enhance model capabilities. These innovations are shaping the future of AI! 🚀🤖
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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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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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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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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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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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In this post, we will cover how to fine-tune Llama 3 70B with PyTorch FSDP, Q-Lora, and Flash Attention 2 (SDPA) using Hugging Face build for consumer-size GPUs (4x 24GB).
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