Instructions to use Ailiance-fr/qwen3-4b-mascarade-dsp-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ailiance-fr/qwen3-4b-mascarade-dsp-lora with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Ailiance-fr/qwen3-4b-mascarade-dsp-lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
docs: Phase 9 cross-domain forgetting matrix (dsp)
Browse files
README.md
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@@ -139,3 +139,22 @@ No baseline bench yet for Qwen3-4B-Instruct-2507 in our pipeline.
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Will include iact-bench Docker validators per domain + N3 5-axis kicad-cli.
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Production usage: compiled to Tower Ollama Modelfile, served via gateway alias
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`ailiance-<domain>` (kicad/spice/stm32/emc/embedded/platformio/freecad/dsp/iot/power).
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Will include iact-bench Docker validators per domain + N3 5-axis kicad-cli.
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Production usage: compiled to Tower Ollama Modelfile, served via gateway alias
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`ailiance-<domain>` (kicad/spice/stm32/emc/embedded/platformio/freecad/dsp/iot/power).
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## Cross-domain forgetting check (Phase 9, 2026-05-11)
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For each domain's eval set (seed=101, n samples held-out), compare this LoRA's Jaccard token-overlap vs the Qwen3-4B-Instruct-2507 **baseline (no adapter)** on the SAME prompts. Negative Δ = the LoRA degrades base behaviour on that domain.
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| Eval domain | LoRA Jaccard | Δ vs base |
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| `kicad` | 0.088 | +0.001 |
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| `spice` | 0.01 | +0.005 |
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| `stm32` | 0.054 | +0.004 |
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| `emc` | 0.067 | +0.001 |
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| `embedded` | 0.075 | +0.001 |
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| `platformio` | 0.038 | -0.004 |
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| `freecad` | 0.028 | +0.007 |
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| `dsp` | 0.094 | -0.007 ⬅ in-domain |
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| `iot` | 0.065 | -0.003 |
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| `power` | 0.069 | +0.001 |
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**In-domain Δ**: -0.007 **Out-of-domain mean Δ**: 0.001
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