Instructions to use luispoveda93/sharky-0.5B-v3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use luispoveda93/sharky-0.5B-v3b with PEFT:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("openbmb/MiniCPM4-0.5B") model = PeftModel.from_pretrained(base_model, "luispoveda93/sharky-0.5B-v3b") - Notebooks
- Google Colab
- Kaggle
sharky-0.5B-v3b β archived phase9 ablation (REJECTED per pre-registered gate)
Delta fine-tune from sharky-0.5B-v3
(+15k CIC-IDS2017 benign states, replay, lr 2e-5, 4,126 steps / 66,000 records). Not
promoted β its purpose (repair S1 benign recall) moved the metric the wrong way:
0.36 β 0.24.
What this ablation established
- More same-corpus benign data at low LR does not fix the CIC-IDS2017 benign-web shortcut β the boundary is representational, not a data-quantity problem.
- kev's replay recipe preserved everything else (attacks improved: malicious recall 0.843β0.934, DDoS 0.230β0.676, locked 0.9836, S2b 0.9789, S4 0.9915) β perfect for adding domains, wrong tool for redrawing a boundary.
| Suite | v3 | v3b |
|---|---|---|
| S1 benign recall | 0.359 | 0.240 β |
| S1 malicious recall | 0.843 | 0.934 |
| S2b unseen-benign recall | 0.9718 | 0.9789 β |
| S4 Neris recall | 0.9899 | 0.9899 β |
| Locked | 0.9808 | 0.9836 β |
Full record: phase9/REJECTED_v3b.md Β· gate_v3b.json Β· next levers in phase9/README.md and CONTRIBUTING.md.
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Base model
openbmb/MiniCPM4-0.5B