Skill Suites
Code-first guides on AI, DevOps, and shipping software
Real proof-of-concepts you can run today. No fluff, no filler, just working examples.
Latest articles
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Preference Tuning: RLHF, DPO, and the Verifiable-Reward Branch
Preference tuning teaches better versus worse, which SFT cannot. RLHF, DPO and its beta, the KTO and ORPO and SimPO family, and…
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QLoRA Explained: A 4-Bit Base, NF4, and What It Really Costs
QLoRA puts a 7B to 30B fine-tune on one card. NF4, double quantisation, paged optimizers, the throughput tax nobody mentions, and the…
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LoRA Explained: Freeze the Model, Learn a Low-Rank Patch
LoRA fine-tuning from the mechanic up: the low-rank update, the zero initialisation, rank and alpha, the honest comparison, and merging versus adapter…
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Data Is the Actual Job: Formats, Quality, Splits and Synthetic Data
Your fine-tuning dataset decides everything the loop does not. Formats, deduplication, decontamination, honest splits, synthetic data and model collapse.
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Supervised Fine-Tuning End to End: The First Real Run
A full supervised fine-tune twice over: a bare PyTorch loop and the same job through a trainer. Hyperparameters that work, plus how…
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Choosing a Base Model and Building a Fine-Tuning Bench
How to pick a base model for fine-tuning, three candidates read as datasheets, where to run the bench, and the toolchain stack…
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Masking in LLM Training: Loss, Causal and Padding
Three things share the name masking. Loss masking with -100, the causal mask the architecture applies, and the padding mask you build.…
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AdamW Explained, Line by Line
AdamW dissected: where it runs, the lineage behind the name, the four update lines, why decoupled weight decay was a real fix,…
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Gradients and Optimizers: From SGD to Adam, and Why m and v Cost 8 Bytes
What a gradient is, what an optimizer decides, and why Adam’s two running statistics per weight are worth 8 bytes each on…
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