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Module tune

Module tune 

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§tune

LOC Complexity

Inference-tuning domain types: sweep a model’s sampling parameters (temperature, top-p, top-k, min-p, repeat-penalty) against an agentic tool-calling task suite to find the settings that make the model both accurate at tool calls and resistant to loop/stagnation guard triggers.

§Modules

§Task Categories

Four are modeled after the Berkeley Function Calling Leaderboard (BFCL): single_call, parallel_call, multi_turn, and irrelevance (correctly abstaining from a tool call). A fifth, long_context, is gglib-specific: it pre-fills TuneTask::history with a long simulated prior conversation before user_prompt, testing whether context degradation over a long session (the model’s attention fixating on stale context) causes it to loop or stagnate on a task it would otherwise handle cleanly from a cold start — the failure mode this whole feature exists to catch for agentic coding use.

§Task Schema

A TaskSuite::Custom is a JSON array of TuneTask values. The same schema is accepted from the CLI (--task-suite path.json, parsed locally) and the GUI (parsed client-side from an uploaded file, posted as the request body) — there is one shared Serde shape, not two divergent ingestion paths.

§Scoring Methodology

Tool-call matching follows the Berkeley Function Calling Leaderboard (BFCL) approach: structural (AST-style) comparison of the recorded call’s name and required arguments against ExpectedCall, not a string diff. Extra arguments the model supplies are ignored; argument order never matters; call order only matters when a task’s expected call sets ordered: true.

Modules

ModuleLOCComplexityCoverage
config.rs
result.rs
task.rs

Re-exports§

pub use config::ScoreWeights;
pub use config::SweepSpec;
pub use config::TuneConfig;
pub use result::CandidateSource;
pub use result::TuneCandidateResult;
pub use result::TuneTaskResult;
pub use task::ExpectedCall;
pub use task::ExpectedOutcome;
pub use task::TaskCategory;
pub use task::TaskSuite;
pub use task::TuneTask;

Modules§

config
Tune-run configuration: sweep specification and scoring weights.
result
Per-candidate and per-task results produced by a tune run.
task
Agentic tool-calling task schema for tune runs.