trace-model

Validate per-layer AI model inference outputs against traces.safetensors.

1.7k|68|Updated Jun 23, 2025
One-click install
npx skills add https://github.com/trymirai/uzu --skill trace-model
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: trace-model
Source: https://github.com/trymirai/uzu/tree/main/agents/skills/trace-model
Command: npx skills add https://github.com/trymirai/uzu --skill trace-model

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Validate intermediate calculations during AI model inference against a source of truth traces to ensure numerical correctness across layers.

Core Features & Use Cases

  • Compare per-layer outputs to traces.safetensors to detect drift
  • Integrate with existing CI/tests to catch regressions in model inference
  • Provide traceability by storing layer-wise results for audits

Quick Start

Run the tracer test to verify model layer outputs against the source-of-truth traces.

Frequently Asked Questions about trace-model

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I validate intermediate calculations during AI model inference?

Validate intermediate calculations by running a tracer test that compares per-layer outputs against source-of-truth traces stored in traces.safetensors to detect numerical drift across layers.

How do I detect numerical drift in model inference layers?

Detect numerical drift by comparing per-layer outputs against source-of-truth traces stored in traces.safetensors to ensure numerical correctness across layers.

How do I integrate trace validation into my project test suite?

Integrate trace validation by running the tracer test within your project test suite, requiring access to traces.safetensors stored alongside the model to compare per-layer results after each inference stage.

Do I need traces.safetensors available to validate intermediate inference calculations?

Yes, you need traces.safetensors stored alongside the model to validate intermediate inference calculations and compare per-layer results after each inference stage.

How do I catch regressions in model inference within CI pipelines?

Catch regressions in model inference within CI pipelines by integrating the tracer test to compare layer-wise results against traces.safetensors, providing traceability for audits.

What is the best way to ensure numerical correctness across AI model layers?

Ensure numerical correctness across AI model layers by validating intermediate calculations against source-of-truth traces using traces.safetensors to detect drift after each inference stage.