Highlights: Every modern LLM agent — from ChatGPT plugins to Claude tools — relies on a single learned skill: outputting a structured JSON function call instead of free text. In this post we teach that skill to a 3-billion parameter model using QLoRA on a free Google Colab T4. We start from the fundamentals — why fine-tuning, when LoRA, how quantization works — then build the full training pipeline from scratch. By the end, your…
Read more

Highlights: Stable Diffusion doesn’t paint an image in one shot — it sculpts one from static, guided by your words. In this post we disassemble the entire machine. We start with the VAE that compresses pixels into a tractable latent space, walk through the forward and reverse diffusion processes, open up the UNet to see how cross-attention physically connects text tokens to spatial regions, and finish with the complete Latent Diffusion architecture diagram that ties…
Read more

Highlights: In this post we build a complete understanding of diffusion models from the ground up — what they are, how images are represented, how the network is trained, what it geometrically learns, and finally how we turn that geometry into samples using DDIM and DDPM. Every formula is accompanied by concrete numbers you can verify by hand. So let’s begin! Tutorial Overview: What Are Diffusion Models? How Images Are Represented The Denoiser Network Noise…
Read more

In this post we build a complete diffusion model from scratch — training a UNet on a custom dataset, implementing the full DDPM pipeline, and understanding the math that makes iterative denoising work. We cover noise schedules, the reparameterization trick, FID evaluation, and three diffusion objectives (ε, x₀, v). By the end you’ll have generated novel images from pure Gaussian noise, and understand why diffusion models overtook GANs as the dominant paradigm for image generation.…
Read more