The first half collapses the diffusion ELBO into one expression: every time step is a squared error for guessing the noise that was added, which gives a five-step training procedure and a one-loop sampler. The second half opens the foundation-model unit: the two-stage paradigm of pretraining plus adaptation, linear probe, finetune, LP-FT, and why LoRA trains only 2dr parameters yet saves very little compute.
The CS231N Spring 2026 vision-and-language lecture replaces the "one model per task" approach of the first half of the course with foundation models: pre-train one model on a large, diverse dataset, then adapt it to many tasks through fine-tuning, zero-shot, or few-shot use. Three threads carry the lecture. First, CLIP: contrastive learning in both directions over 400 million image-text pairs scraped from the web, then writing class names as sentences to classify without any fine-tuning; it also has weak spots, such as failing to tell "a mug in some grass" from "some grass in a mug". Second, vision-language models from LLaVA and Flamingo to Qwen3-VL and Molmo, which feed image features into an LLM so it can look at an image and output text. Third, chaining: letting an LLM write descriptions or programs that string existing vision models together.
Apple is giving App Store Small Business Program developers free access to AFM 3 models on Private Cloud Compute if their apps have fewer than two million first-time downloads. The five-model family includes the sparse 20B-parameter AFM 3 Core Advanced, which activates only 1–4B parameters on-device, and AFM 3 Cloud Pro on Google Cloud NVIDIA GPUs, refined with outputs from Gemini.
Apple Foundation Models (AFM) is Apple's closed-ecosystem AI family. It evolved from a 3B dense model with LoRA adapters in 2024 into five models in 2026. AFM 3 Core Advanced runs a 20B IFP sparse architecture on phones while activating only 1–4B parameters; Cloud Pro runs on Google Cloud NVIDIA GPUs and is refined through Gemini distillation. There is no public API price or third-party benchmark, and access is limited to Apple's Foundation Models framework.