Tag: #tiny-models
Writing on GPUs, LLMs, MLOps, Kubernetes — and mindset · 5 posts
AI for Everyone, Part 6 — Drawing Digits From Words With a 1.11M Diffusion Model
We built a conditional diffusion model with 1.11 million parameters that draws a 0 when you type "zero". The forward process that adds noise is a single formula; the reverse process that restores the image is that same l
2026-08-24 · 8 min read #ai#diffusion#ddpm#generative#pytorchAI for Everyone, Part 5 — Colourising Photos With a 0.47M U-Net, and Why the Colours Came Out Washed Out
The smallest model in this series — a 472K-parameter U-Net — restored colour to greyscale CIFAR-10 images. Shapes survived intact, but the colours came out noticeably washed out. That is not a capacity problem; it is a c
2026-08-23 · 7 min read #ai#computer-vision#unet#colorization#pytorchAI for Everyone, Part 4 — Captioning Images With 1.37M Parameters, and Why Part 3 Bug Was Absent Here
We joined a CNN encoder to a transformer decoder and captioned Fashion-MNIST images. With 1.37 million parameters and ten minutes of training, the label hit rate reached 91%. This part looks at what cross-attention does
2026-08-22 · 7 min read #ai#captioning#multimodal#transformer#pytorchAI for Everyone, Part 3 — Loss of 0.0017, Accuracy of 7.5%: The Culprit Was One Padding Slot
We built a VQA model — one that answers questions about an image — with 1.48 million parameters. Training loss fell to 0.0017 while accuracy sat at 7.5%, worse than guessing. The cause was not the model but a single line
2026-08-21 · 8 min read #ai#vqa#multimodal#debugging#pytorchAI for Everyone, Part 1 — Training a 16M-Parameter Language Model From Scratch in 15 Minutes
Training a language model from scratch on a single GPU. With the TinyStories dataset and a 16-million-parameter decoder-only transformer, we produced readable English fairy tales in 15 minutes. We look at why the causal
2026-08-19 · 9 min read #ai#llm#transformer#pytorch#hands-on