Tutorials

These tutorials are task-oriented walkthroughs. Each one names the model shape, the inference route, and the point where the result should be inspected or validated.

Choose By Problem

If you need to

Walkthrough

Surface

model records with mixed field types

Fitting Heterogeneous Records

Stable core

express a model with the PPL layer

PPL Mixture Workflow

Active development

enumerate top-k support values

Enumeration and Ranking

Stable/evolving core

save, serve, and monitor model artifacts

Production Artifacts

Practical production helpers

replace repeated LLM calls with a calibrated local model

LLM Distillation Cascade

Active task workflow

decide when an LLM should abstain

LLM Uncertainty

Active reasoning workflow

build shared representations for multiple modalities

Representation And Model Families

Active representation workflow

combine distribution operations with structured decisions

Relations And Operations

Stable/evolving core

run an auditable model improvement loop

Evolution And Analysis

Active design/evolution workflow

What Each Tutorial Demonstrates

Tutorial

Main idea

Related guides

Fitting Heterogeneous Records

A tuple-shaped row becomes a composite estimator, and a mixture adds a latent cluster over the whole record.

Core Concepts, Structured Statistical Families

PPL Mixture Workflow

free parameters and Mix lower to the same estimator/distribution contract as the core API.

Probabilistic Programming, Automatic Inference

Enumeration and Ranking

A fitted model can expose ranked support traversal when the capability is available.

Enumeration and Ranking, Capabilities And Contracts

Production Artifacts

Fitted models need provenance, registry metadata, serving wrappers, and drift checks.

Production Workflows, Model Lifecycle

LLM Distillation Cascade

A teacher labels examples, a local model learns the task, and calibrated confidence decides whether to answer or escalate.

Task Distillation, Task Serving, Routing, And Edge Deployment

LLM Uncertainty

Repeated LLM samples become semantic entropy, answer confidence, and abstention decisions.

Uncertainty, Reasoning Systems

Representation And Model Families

Segmenters, embeddings, and vector quantizers turn heterogeneous modalities into a shared modeling stream.

Representation Layer, Model Families

Relations And Operations

Distribution operations and structured feasible-set solvers solve different parts of a decision workflow.

Operations, Relations

Evolution And Analysis

Diagnostics and objective-led search promote challengers only when they pass a verification gate.

Analysis Utilities, Evolution And Search