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BertilBraun/qwen3-1.7b-voice-light-tool-use-lora

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Model Card

Voice Light Qwen3 1.7B Tool-Use LoRA

This is the Voice Light conversational tool-use adapter for Qwen/Qwen3-1.7B. It teaches the model to preserve conversational context, speak a natural bridge before a tool call, emit Hermes-style structured calls, continue after tool results, and avoid tools when they are not needed.

The published adapter is the logical epoch 4 checkpoint at optimizer step 265. It was selected by comparing the base model, all eight epoch checkpoints, and the previously deployed adapter on held-out conversations.

Training

—Source dataset: BertilBraun/voice-light-tool-use-synthetic
—Source project commit: e17b27a
—Base revision: 70d244cc86ccca08cf5af4e1e306ecf908b1ad5e
—LoRA rank / alpha: 16 / 32
—Learning rate: 5e-5
—Training schedule: 8 logical epochs with epoch checkpoints
—Selected checkpoint: epoch 4, optimizer step 265
—Training hardware: NVIDIA GeForce RTX 4090

training-config.json and training-summary.json contain the complete run configuration and summary.

Data reproducibility

The source dataset publishes the exact teacher-led v22 generator snapshot under `reproducibility/source-8c32f70`. It includes the pinned source commit, prompt implementation, scenario sampler, rollout controller, deterministic tool simulators, vLLM client, typed schemas, validators, locked Python environment, tests, teacher-server command, and an end-to-end reproduction launcher.

The 2,400 tool-rich and 1,600 no-tool scenario plans were independently regenerated from seeds 20260723 and 20260724; both matched the published plans byte for byte. The dataset card documents the teacher revision, FP8 runtime, fixed time anchor, sampling configuration, stable per-request seeds, generation manifests, source hashes, and reproducibility limits.

Evaluation

The checkpoint evaluation uses 240 conversation states sampled from the 511 held-out test records. It balances required search, calculate, and get_time calls, no-tool responses, and post-tool continuations. Every state is sampled with seeds 17, 29, and 43 using the production Voice Light system prompt.

Metric	Epoch 4	Previous adapter
Parser-valid output	100.0%	100.0%
Required tool emitted	77.2%	27.2%
Correct tool selected	61.7%	27.2%
Schema-valid required call	75.6%	27.2%
Bridge present before required call	100.0%	100.0%
Correctly avoided a tool	93.3%	100.0%
Nonempty post-tool continuation	83.9%	100.0%

The full eight-checkpoint report is included as checkpoint-evaluation.json. The adapter is intentionally a behavioral nudge rather than a general-purpose replacement for the base model.

Usage

Load this repository as a PEFT adapter on the exact pinned base revision. Voice Light disables Qwen thinking mode and supplies tools through the native chat template.

Limitations

This is a small conversational model. It can still choose the wrong tool, omit a required call, over-call, or produce a bridge longer than requested. Applications must validate tool names and arguments, execute tools outside the model, and never treat generated factual claims as verified tool results.