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 |
Stable core |
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express a model with the PPL layer |
Active development |
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enumerate top-k support values |
Stable/evolving core |
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save, serve, and monitor model artifacts |
Practical production helpers |
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replace repeated LLM calls with a calibrated local model |
Active task workflow |
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decide when an LLM should abstain |
Active reasoning workflow |
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build shared representations for multiple modalities |
Active representation workflow |
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combine distribution operations with structured decisions |
Stable/evolving core |
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run an auditable model improvement loop |
Active design/evolution workflow |
What Each Tutorial Demonstrates¶
Tutorial |
Main idea |
Related guides |
|---|---|---|
A tuple-shaped row becomes a composite estimator, and a mixture adds a latent cluster over the whole record. |
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A fitted model can expose ranked support traversal when the capability is available. |
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Fitted models need provenance, registry metadata, serving wrappers, and drift checks. |
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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 |
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Repeated LLM samples become semantic entropy, answer confidence, and abstention decisions. |
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Segmenters, embeddings, and vector quantizers turn heterogeneous modalities into a shared modeling stream. |
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Distribution operations and structured feasible-set solvers solve different parts of a decision workflow. |
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Diagnostics and objective-led search promote challengers only when they pass a verification gate. |