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jatshi
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Model Details
Model Provider
jatshi
Model Tree
Qwen/Qwen2.5-1.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
The v4.0 GRPO run collapsed because every candidate in every group received zero reward. Version 4.1 starts from the verified SFT adapter, keeps an SFT anchor, rejects saturated pre-training canaries, and continuously calibrates otherwise-valid action parameters against the deterministic synthetic-degradation target. The target is data-derived; no LLM judge is used.
| Promotion evidence | Result |
|---|---|
| Pre-training canary | 48/48 valid JSON; 12/12 non-saturated groups |
| Training | 300 optimizer steps; 1,200 groups |
| Sampled valid JSON | 99.1458% |
| Non-saturated groups | 99.8333% |
| Held-out predictions | 1,416/1,416 successful |
| Placeholder rate | 0% |
| SFT mean verifiable reward | 0.953821 |
| GRPO mean verifiable reward | 0.976320 |
| Promotion gate | passed |
All 1,416 held-out predictions were executed by the bounded DSP engine. Candidates that failed the SI-SDR safety condition were rolled back to the original noisy waveform.
| Metric | Result |
|---|---|
| Safety accept / rollback | 68.15% / 31.85% |
| Mean SI-SDR gain | +0.35468 dB |
| Mean PESQ gain | +0.04056 |
| Mean STOI gain | +0.00260 |
| Mean / P95 prescription latency | 786.15 / 873.03 ms |
All four SNR buckets (<5, 5–10, 10–20, and >=20 dB) have positive mean gains for all three
metrics. These are objective, reference-based benchmark measurements rather than listening-test
claims.
adapter/: PEFT LoRA adapter and tokenizer files;audio_projector.pt: Whisper-hidden-state to 16-token prefix projection;reports/: canary, training, prediction, generalization, acceptance, and environment evidence;README.md: this model card.The base Qwen and Whisper weights are not redistributed.
Use configs/native_audio_autodl_grpo_recovery_12gb.json from the source repository. The verified
runtime used Python 3.10.8, PyTorch 2.5.1, Transformers 4.48.3, PEFT 0.14.0, Accelerate 1.2.1, and
DeepSpeed 0.16.3 on one RTX 3080 Ti 12GB. DeepSpeed ZeRO-2 at world_size=1 is runtime integration
evidence, not a multi-GPU scaling claim.
The source code is MIT licensed. Use of the adapter and reconstructed data remains subject to the Qwen, Whisper, LibriSpeech, and ESC-50 terms.