cshores
Joined 2 years ago
Joined 2 years ago
Inference is mostly matrix math + a few standard ops, and the behavior isn’t hand-coded rule-by-rule. The “algorithm” part is more like instincts in animals: it sets up the learning dynamics and some biases, but it doesn’t get you very far without what’s learned from experience/data.
Also, most “knowledge” comes from pretraining; RL-style fine-tuning mostly nudges behavior (helpfulness/safety/preferences) rather than creating the base capabilities.
Heres my script if anyone is interested in as I find it to be incredibly useful.
find . -type f \( -name ".tf" -o -name ".tfvars" -o -name ".json" -o -name ".hcl" -o -name ".sh" -o -name ".tpl" -o -name ".yml" -o -name ".yaml" -o -name ".py" -o -name ".md" \) -exec sh -c 'for f; do echo "### FILE: $f ###"; cat "$f"; echo; done' sh {} +