gemma-explain-lora-v0

Kámárí Gemma Explanation LoRA (v0)

A QLoRA adapter for google/gemma-4-E4B-it that turns Kámárí's CNN + policy signals into a safe, multilingual, strict-JSON explanation. Gemma explains a decision; it never estimates age and never invents a reason code.

• LoRA r=32, alpha 64, target_modules="all-linear", 4-bit nf4 QLoRA, 3 epochs on H200.
• Output schema: decision, reason_code, user_message, admin_summary, next_step, language, safety_note. Reason codes come from a fixed list.

Intended use

Input: {cnn_result, policy_context:{decision, reason_code, legal_threshold, challenge_threshold}, language}. Output: one strict-JSON object with the message in the requested language. Languages in the SFT set: en, sw, yo, ha, am, fr, ar (more in the app picker). Non-English strings should get native-speaker review before release.

Training data

Synthesized from the Kámárí policy engine, not from child faces: sampled signals run through the same decide() rules, and approved per-reason, per-language templates render the message. The set is reason-code balanced (so it is not dominated by ALLOW): 8,000 rows, 7,200 train / 800 eval.

Evaluation

Training loss converged from 3.00 to a best eval loss of 0.087, at 96.3% eval token accuracy (3 epochs / 675 steps, about 35 minutes on an H200; tracked in Weights & Biases, project kamari). Evaluated through the served endpoint (the manual KV-cached greedy decode used in production, not the buggy generate() path), over n=70 cases across 5 reason codes and 7 languages (en, sw, yo, ha, am, fr, ar):

The endpoint validates output and falls back to an approved template on any model failure, so the system always returns valid, schema-correct, policy-consistent JSON; language correctness reflects in-language generation by the model. (An earlier v0 eval showed 0.0 across the board because it ran through the buggy generate() path; those numbers are superseded.) Non-English strings still benefit from a native review.

Training curves

Pulled from the Weights & Biases run (project kamari, run gemma4b-lora-r32). Cross-entropy loss converges from 3.00 to a best eval loss of 0.087, and mean token accuracy rises to 96.3%, with train and eval tracking closely across 675 steps (3 epochs).

Serving

Load base Gemma 4 + this adapter, merge_and_unload(), and decode greedily token by token (avoid generate()). On any validation failure, return a deterministic safe fallback so the caller always gets valid JSON. Served on a Modal L4 GPU, always-on.

Methodology: https://github.com/Mystique1337/kamari/blob/main/docs/methodology.md

License

Apache-2.0. Every output carries: this is an estimate, not a legal age determination.

Links

• Code (Apache-2.0): https://github.com/Mystique1337/kamari
• Live demo: https://kamari.shinzii.tech
• Methodology: https://github.com/Mystique1337/kamari/blob/main/docs/methodology.md