Instructions to use luispoveda93/sharky-0.5B-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use luispoveda93/sharky-0.5B-v3 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-v3") - Notebooks
- Google Colab
- Kaggle
sharky-0.5B-v3 β archived phase9 candidate (REJECTED per pre-registered gate)
Mixed-corpus retrain (UNSW-NB15 + DAPT-2020 + CIC-IDS2017 60.5k + CTU-13 10k states,
from base, 18,208 steps / 291,308 records, lr 5e-5). Not promoted β the production
model remains luispoveda93/sharky-0.5B.
Results (phase9 5-gate battery β 3/5)
| Suite | v2 production | v3 (this) |
|---|---|---|
| CTU-13 Botnet-42 (real malware) recall | 7.4% | 99.0% β |
| Unseen 2018 benign recall (false-flag) | 58% (34% FF) | 97.2% (2.8%) β |
| Locked (in-distribution) | 0.9865 | 0.9808 β |
| CIC-IDS2017 macro-F1 | 0.173 | 0.706 (target 0.85) β |
| OOD ECE | 0.33β0.51 | S1 0.19 β, S2b/S4 β€0.015 β |
Why rejected: benign recall on CIC-IDS2017 is 0.36 β diagnosed as a benign-web shortcut (phase9/diag_s1_benign.json): web-destination benign flows are false-flagged 85%.
Full record: phase9/REJECTED.md Β· gate_v3.json Β· CONTRIBUTING.md
Serve exactly like the production model (POST /v1/systemone via the same shim); the
same prefill-only battery applies. Useful as a real-malware detector today; not
production-safe on benign web traffic.
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Base model
openbmb/MiniCPM4-0.5B