Instructions to use nur-dev/frozen-stress-lora-r4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nur-dev/frozen-stress-lora-r4 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B") model = PeftModel.from_pretrained(base_model, "nur-dev/frozen-stress-lora-r4") - Notebooks
- Google Colab
- Kaggle
| license: cc-by-nc-4.0 | |
| tags: | |
| - lora | |
| - peft | |
| - speech-language-models | |
| - frozen-adapter | |
| base_model: Qwen/Qwen3-8B | |
| # BTA — Stage 6 LoRA r=4 | |
| Minimal-LoRA upper-bound experiment: rank 4, α=16 (α/r=4) on | |
| Qwen3-8B `q_proj`/`v_proj` across all 36 transformer blocks (72 LoRA | |
| modules total, ~1.92M LoRA parameters). The R1.8 adapter and | |
| $C_\phi$ heads are FROZEN; only the LoRA matrices update. | |
| **Pre-registered oracle-stability gate:** Probe-G-oracle re-confirm | |
| within ±2pp of the Stage 1a anchor 0.7871. Result: 0.7970 (+0.99pp drift) → PASS. | |
| **Outcome branch:** weak-negative. Probe-G$_{\mathrm{neutral}}$ = 0.4926 | |
| (R0 - 0.0196), 0.4pp below the H2 NULL band on the negative side. | |
| Minimal LoRA at this rank preserves the oracle but does not recover | |
| audio-conditional consumer use of the R1.8 representation. | |
| **Files:** PEFT LoRA weights (`adapter_config.json`, `adapter_model.safetensors`). | |
| Load via `PeftModel.from_pretrained` on top of base `Qwen/Qwen3-8B`. | |
| **Files / metrics:** | |
| | | Value | | |
| |---|---| | |
| | Oracle re-confirm | 0.7970 | | |
| | Probe-G$_{\mathrm{neutral}}$ | 0.4926 | | |
| | Probe-G$_{\mathrm{total}}$ | 0.6277 | | |
| | Probe-G$_{\mathrm{explicit}}$ | 0.7629 | | |
| | Probe-K linear (R1.8 frozen) | 0.2265 | | |
| | Probe-K MLP-2 (R1.8 frozen) | 0.3120 | | |
| **Code / paper:** <https://github.com/Nurgali-Kadyrbek/frozen-speech-llm-stress> | |
| License: CC-BY-NC-4.0. | |