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At scale, your training efficiency is determined by a single metric: "goodput", the...
A practical walkthrough of supervised fine-tuning a multimodal Mixture-of-Experts model on a four-node DGX Spark cluster — decoded from…Continue reading on Medium »
The advanced side of supervised fine-tuning data prep. This second post in a two-part series covers evaluating data readiness with learning curves, selecting high-value data subset...
The math behind reinforcement learning (RL) post-training for large language models (LLMs) is notoriously unforgiving. As frontier AI labs push the boundaries of reasoning and codi...
Abstract. We’ve now built four ways to shape a base model — SFT, DPO, PPO, GRPO — and proven each on real numbers. This finale ties them…Continue reading on Medium »
Three conditions that must hold before splitting prefill from decode pays off, and why chunked prefill is the right default below that threshold. The post Disaggregation Is a Thous...
Build a custom LLM post-training pipeline using AllenAI’s Open Instruct framework. This comprehensive guide walks through Supervised Fine-Tuning (SFT), Direct Preference Optimizati...
Veri Bilimi Bootcamp yolculuğumuzda, pandas ile verileri temizlemek ve scikit-learn ile temel makine öğrenmesi modelleri kurmak işin…Continue reading on Medium »
In this tutorial, we explore NVIDIA’s srt-slurm framework and learn how we use srtctl to convert declarative YAML configurations into reproducible SLURM benchmark workflows for dis...
Convolutional neural network workloads rarely fail because the forward pass is mathematically difficult. They fail because modern training and inference pipelines are distributed s...
Learn how DSpark speculative decoding can improve local LLM generation speed using the same GPU, with Qwen3-8B, llama.cpp, and CUDA.
Learn how DSpark speculative decoding can improve local LLM generation speed using the same GPU, with Qwen3-8B, llama.cpp, and CUDA.
MindLab releases Macaron-V1: Mixture-of-LoRA post-training on GLM 5.2 with 4 specialized 1B-parameter expert adapters, 2M token context extension, and 748B Venti variant trained on...
Наконец-то мы добрались непосредственно до того, как тренировать трансформер, не просто тренировать, а делать это эффективно и масштабируемо.Как мы уже знаем из прошлых глав, транс...
Explains test-time training through the analogy of a GPS learning a persistent shortcut around daily traffic rather than a one-time reroute: the model takes a gradient step on the...
Explains test-time training through the analogy of a GPS learning a persistent shortcut around daily traffic rather than a one-time reroute: the model takes a gradient step on the...
Data preparation determines the ceiling of any supervised fine-tuning project. This first post in a two-part series covers the foundations of SFT data prep: quality checks, convers...
Poolside just made a serious open-weight coding model plausible on hardware a small team can actually own. Laguna S 2.1 is a 118B…Continue reading on CodeToDeploy »
Peking University, StepFun, and Beijing University of Posts and Telecommunications propose TensorCast, a unified programmable tensor lifecycle management abstraction for large mode...
Cloud TPU v6e-1 (ct6e-standard-1t, one v6e chip, 32 GB HBM), GCE flex-start, europe-west4-a. vLLM baseline measured 2026-07-21. The workload nobody benchmarks Serving b...
Pathway's Baby Dragon Hatchling (BDH) is a brain-inspired, post-transformer architecture that reasons in latent space instead of emitting chain-of-thought tokens. See how Pathway d...
In this article, you will learn how static, dynamic, and continuous batching work in LLM inference, and why the differences between them matter at production...
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Five resources covering SLM architecture, fine-tuning, agentic workflows, and local deployment for data professionals.
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