Add v8 model card

MODEL_CARD_MINICPM5_LORA_V8.md

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+---
+license: mit
+base_model: openbmb/MiniCPM5-1B
+library_name: peft
+tags:
+- build-small-hackathon
+- openbmb
+- minicpm
+- minicpm5
+- peft
+- lora
+- scam-defense
+- tiny-titan
+- well-tuned
+datasets:
+- build-small-hackathon/jawbreaker-scam-defense-data
+---
+
+# Jawbreaker MiniCPM5-1B LoRA v8
+
+Jawbreaker MiniCPM5-1B LoRA v8 is the final small-model adapter used by the Jawbreaker Hugging Face Space.
+
+- Base model: `openbmb/MiniCPM5-1B`
+- Adapter: `build-small-hackathon/jawbreaker-minicpm5-1b-lora-v8`
+- App: https://huggingface.co/spaces/build-small-hackathon/jawbreaker
+- Dataset/eval bundle: https://huggingface.co/datasets/build-small-hackathon/jawbreaker-scam-defense-data
+- GitHub: https://github.com/gowtham0992/jawbreaker
+
+## Task
+
+The model converts one suspicious text, email, or DM into strict JSON for a scam-safety card:
+
+- risk level: `safe`, `needs_check`, `suspicious`, or `dangerous`
+- scam type
+- short summary
+- safest next action
+- warning tactics
+- scam DNA fields for what the sender pretends to be, how they apply pressure, what they ask for, and what could happen
+
+Jawbreaker is intentionally narrow. It is not a general chatbot. The goal is to help a non-expert pause before clicking, replying, sharing a code, or sending money.
+
+## Training
+
+The adapter was trained with PEFT/LoRA on Modal A100 using synthetic and sanitized scam-defense examples generated in the public repository.
+
+The v8 pass is failure-driven calibration. It targeted two gaps found during earlier v7/fresh-pattern evals:
+
+- wrong-number crypto / gold / trading grooming that could be under-called
+- ordinary family, school, pharmacy, and logistics messages that should not be over-called as dangerous
+
+Key training/eval files:
+
+- `training/generate_v8_data.py`
+- `training/data/train_v8.jsonl`
+- `training/data/dev_v8.jsonl`
+- `training/data/test_v8.jsonl`
+- `eval/hard_v8_eval.jsonl`
+- `eval/reports/jawbreaker-minicpm5-1b-lora-v8-hard632-safetyguard-v4.json`
+
+## Final Eval
+
+Final guarded Modal A100 eval on `eval/hard_v8_eval.jsonl`:
+
+| Metric | Result |
+| --- | ---: |
+| Cases | 632 |
+| Risk accuracy | 579/632, 91.61% |
+| Scam type accuracy | 561/632, 88.77% |
+| Mean tactic recall | 90.69% |
+| Dangerous as safe | 0 |
+| Dangerous as needs_check | 0 |
+| Safe as dangerous or suspicious | 0 |
+| Unsafe action violations | 0 |
+| Invalid predictions | 0 |
+| Model errors | 0 |
+
+The final report is published in the dataset/eval bundle and in the app repository.
+
+## Runtime Safety
+
+The live app validates model output against a strict schema before rendering. It also applies a deterministic safety guard for obvious high-risk patterns, so a weak small-model response does not render an obvious scam as safe.
+
+If the model fails, returns malformed JSON, or under-calls an obvious danger signal, Jawbreaker falls back to deterministic safety analysis and recommends verification through trusted official channels.
+
+## Limitations
+
+- This is a hackathon prototype, not legal, financial, or cybersecurity advice.
+- Training data is synthetic/sanitized, not a proprietary corpus of private user messages.
+- The model is optimized for short scam-like messages and may not generalize to long documents.
+- The safest action should still be verified by the user through official channels or a trusted person.