Understanding the BART Model for Accurate Text Summarization
Explore BART (Bidirectional and Auto-Regressive Transformers), a powerful seq2seq model for NLP tasks like text summarization and generation.
Fine-Tuning Vision-Language-Action Models
‘Learn how to fine-tune vision-language-action models for robotics with this comprehensive tutorial covering dataset preparation, LoRA adaptation, training strategies, and deployment for natural language-controlled robots.’
Evaluating Reward Models with RewardBench 2
‘ RewardBench 2 seeks to evaluate reward models. In this article, we describe its relevance, conception, and how to get started with using it.’
What every ML/AI developer should know about ONNX
ONNX is a powerful and open standard for preventing framework lock-in and ensuring that you the models you develop will be usable in the long run.
PyTorch 101: Understanding Hooks
We cover debugging and visualization in PyTorch. We explore PyTorch hooks, how to use them, visualize activations and modify gradients.
A Deeper Dive into Graph Neural Networks
Learn the fundamentals of Graph Neural Networks, how they work, and how to implement them using PyTorch. Explore key concepts and examples.
How To Make Karaoke Videos using Whisper and Spleeter AI Tools
In this tutorial, you’ll see an example of using Whisper and Spleeter together that you can use to make your own karaoke selections, or integrate into another application stack.
Understanding Reasoning in Large Language Models: Overview of the paper “Towards Reasoning in Large Language Models: A Survey”
In this article, we break down the paper “Towards Reasoning in Large Language Models: A Survey” in an attempt to explain relevant reasoning concepts used by LLMs.
Scaling Gradient with GPU Droplets and the cloud provider Networking
Build Scalable and Secure Gradient with the cloud provider’s GPUs, VPC, and Global Load Balancers.
MobiLlama: Your Compact Language Companion
Explore MobiLlama, SLM essentially a scaled-down versions of Llama, featuring 0.5 billion parameters, in contrast to LLMs that boast hundreds of billions or even trillions of parameters.