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turnercore/functiongemma-270m-automaticity-v9-lora

sourceHugging Facegemmaupdated 2mo agoView on Hugging Face

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FunctionGemma 270M + Automaticity V9 LoRA

Rank-16 response-only LoRA trained for one epoch on the private Automaticity V9 friendly direct-tool corpus. This is the retained validation candidate, not a production-promoted router.

The model routes one current thought to at most one available tool, or emits the native FunctionGemma no-tool response. Training used native FunctionGemma tool declarations and call tokens, a 768-token rendered-row budget, and loss only on the model turn.

Training

—Base: google/functiongemma-270m-it
—Base/tokenizer revision: 39eccb091651513a5dfb56892d3714c1b5b8276c
—Rows: 4,900
—Context: 768 tokens; no truncation; 100% training-gold retention
—LoRA: rank 16, alpha 16
—Epochs: 1
—Learning rate: 2e-4
—Effective batch: 16 (4 x 4 gradient accumulation)
—Seed: 3407
—Loss: native assistant response only
—Adapter SHA-256: 8e68ed2224f4d95d16a632dea2ceff35682f66c47fbbae128c14d56779155462

Frozen validation result

The evaluation used 1,050 private validation rows with normal five-tool retrieval, no gold injection, 100% action-gold retrieval recall, and no decoding constraint.

Metric	Result
End-to-end exact	89.71%
Routing	94.95%
Action exact	65.38%
No-tool precision	99.86%
No-tool recall	100%
Listed-tool rate	99.14%
Valid-call rate	99.90%
Latency average	1.630 s
Latency p50	0.527 s
Latency p95	6.883 s

Limitations

This adapter is not yet promoted for autonomous execution. Nine validation action rows produced plausible but unlisted aliases, one output was invalid, and action exact accuracy remains 65.38%. Use strict listed-name/schema validation or constrained decoding and reject invalid calls at runtime. Constraints cannot fix semantically wrong listed tools or schema-valid wrong arguments.

The private dataset and row-level evaluation repository is turnercore/automaticity-v9.