3+ mon, 2+ week ago   (900+ words) Researchers from Sakana AI and the University of Tokyo propose DiffusionBlocks. It trains transformer-based networks one block at a time. Training memory is reduced by a factor of B, where B is the number of blocks. Performance is maintained across…...

3+ mon, 2+ week ago   (628+ words) In this tutorial, we build an advanced federated learning experiment with NVIDIA FLARE. We compare FedAvg and FedProx on a non-IID CIFAR-10 setup, where client data is split using a Dirichlet distribution to simulate realistic label imbalance across federated sites....

3+ mon, 4+ week ago   (698+ words) The two-phase training technique — validated across four model scales from 270M to 10B parameters — modifies only the training loop, leaving the inference-time architecture completely untouched. Pre-training large language models is expensive enough that even modest efficiency improvements can translate into meaningful cost…...

4+ mon, 1+ week ago   (555+ words) In this tutorial, we walk through an end-to-end implementation of an advanced machine learning pipeline using ZenML. We begin by setting up the environment and initializing a ZenML project, then define a custom materializer that enables seamless serialization and metadata…...

4+ mon, 1+ week ago   (575+ words) In this tutorial, we walk through a complete, hands-on journey of post-training large language models using the powerful TRL (Transformer Reinforcement Learning) library ecosystem. We start from a lightweight base model and progressively apply four key techniques: Supervised Fine-Tuning (SFT),…...

5+ mon, 1+ week ago   (334+ words) In the current landscape of generative AI, the ‘scaling laws’ have generally dictated that more parameters equal more intelligence. However, Liquid AI is challenging this convention with the release of LFM2.5-350M. This model is actually a technical case study in intelligence…...

5+ mon, 3+ week ago   (232+ words) We install the required libraries and import all the modules needed for the reinforcement learning pipeline. We initialize the environment, define the neural network architecture using Haiku, and set up the Q-network that predicts action values. We also initialize the…...

7+ mon, 1+ day ago   (262+ words) We install the required libraries and import all the necessary modules for training and evaluation. We set a deterministic seed, so our sampling and training behavior stay consistent across runs. We also ensure PyTorch and CUDA RNGs are aligned when…...

7+ mon, 1+ week ago   (268+ words) We set up the environment by installing dependencies, importing libraries, and fixing random seeds for reproducibility. We detect and configure the computation device to ensure consistent execution across systems. We also define a utility to create configuration objects safely across…...

7+ mon, 2+ week ago   (623+ words) In this tutorial, we implement Tree-KG, an advanced hierarchical knowledge graph system that goes beyond traditional retrieval-augmented generation by combining semantic embeddings with explicit graph structure. We show how we can organize knowledge in a tree-like hierarchy that mirrors how…...