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Table of contents Pre-Training Supervised Finetuning LoRA QLoRA RLHF Reasoning (GRPO) Deployment Training a modern large language model (LLM) is not a single step but a carefully orchestrated pipeline that transforms raw data into a reliable, aligned, and deployable intelligent system. At its core lies pretraining, the foundational phase where models learn general language patterns, reasoning structures, and world knowledge from massive text corpora. This is followed by supervised fine-tuning (SFT), where curated datasets shape the model’s behavior […]

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Fireside chat with Vincent Granville, CAIO & Co-Founder at BondingAI.io. Hosted by Benjamin Johnson, CEO & Co-Founder at Particle41. Dive into AI advancements with Vincent Granville, as he reveals breakthrough methods in hallucination-free AI models and synthetic data innovations. Join us in this episode of the Particle Accelerator Podcast as we welcome Vincent Granville, Co-Founder at BondingAI. With over 25 years of experience in statistics and AI, Vincent Granville shares valuable insights on building hallucination-free AI models and […]

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Release: datasette 1.0a27 Two major changes in this new Datasette alpha. I covered the first of those in detail yesterday – Datasette no longer uses Django-style CSRF form tokens, instead using modern browser headers as described by Filippo Valsorda. The second big change is that Datasette now fires a new RenameTableEvent any time a table is renamed during a SQLite transaction. This is useful because some plugins (like datasette-comments) attach additional data to table records by name, so […]

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Most practical engineering design problems involve nonlinear spatio-temporal dynamical systems. Multi-physics simulations are often performed to capture the fine spatio-temporal scales which govern the evolution of these systems. However, these simulations are often high-fidelity in nature, and can be computationally very expensive. Hence, generating data from these expensive simulations becomes a bottleneck in an end-to-end engineering design process. Spatio-temporal surrogate modeling of these dynamical systems has been a popular data-driven solution to tackle this computational bottleneck. This is […]

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Rotating detonation engines (RDEs) are a promising propulsion concept that may offer higher thermodynamic efficiency and specific impulse than conventional systems, but nonlinear phenomena, including transitions to oscillatory or chaotic propagation modes, can hinder practical operation. Deep Reinforcement Learning (DRL) has emerged as a promising method for controlling complex nonlinear dynamics such as those observed in RDEs. However, the multi-timescale nature of the RDE system makes direct application of DRL challenging. We address this challenge by reformulating the […]

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Catastrophic forgetting remains a primary hurdle in sequential task learning for artificial neural networks. We propose a silicon-native modular architecture that achieves structural parameter isolation using Task-Specific Experts and a distributed, outlier-based Gatekeeper. Moving beyond traditional sequential consolidation, our framework utilizes a Simultaneous Pipeline where Teacher learning, Student distillation, and Router manifold acquisition occur in parallel while raw data is present in a localized training session. This approach ensures computational efficiency and complies with privacy mandates like GDPR […]

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