ThakiCloud

Qwen3.8-27B-Human-KO-Finance

README

License: apache-2.0

Why the gate is mandatory

Table with columns: Weights alone, With gate
	Weights alone	With gate
Disclosure coverage	0.905	1.000
Factual errors (out of 200)	0-1	0

Deploying without the gate misses disclosures. Because probabilistic generation cannot guarantee zero, the design has a deterministic verifier block it. Use gates/ together with the model.

Measurements (200-item eval set, median of 3 repetitions)

Table with columns: Metric, FIN2 (volume increase only), FIN5
Metric	FIN2 (volume increase only)	FIN5
Suitability behavior pass	0.08	0.818
Factual errors (raw)	3	0
Disclosure coverage	0.885	0.905
Suitability-refusal rate (over-refusal)	0.008	0.044
Bullets/bold/headers	0	0

Superiority over the baseline was confirmed with the same-item paired McNemar test, all 3 runs p<0.005.

⛔ Limitations (please read these as they are)

Only n=33 items are judgeable for suitability, so the 3-run spread is 0.212. Do not cite the absolute value; judge only by the paired comparison. ±0.05 precision requires n≈380.
The situation classifier's accuracy is 0.885. A misclassification drops a required disclosure from the list, so this repository uses the union of input classification and output-based judging.
Factual errors run 0-1 per 200 cases, so the "0" verdict itself has a statistical-power limit.
⛔ This model does not by itself prevent missing disclosures. The gate only catches shortfalls and backfills them. Legal review is required before deployment into an actual regulatory environment.

Structure

markdown
gates/      Disclosure, suitability, output-based, and factual judging and enforcement (7 types)
eval/       Measurement harness + paired superiority test
promptset/  200-item eval set (synthetic)

Source

The evaluation criteria are based on the Korea Financial Investment Association's Standard Investment Recommendation Guidelines and Article 19 of the Financial Consumer Protection Act. ⛔ The Association's original text is copyrighted (ALL RIGHTS RESERVED) and is not included in this repository. Check it directly at https://law.kofia.or.kr.

Lineage

Qwen/Qwen3.8-27B (Apache-2.0) → ThakiCloud/Qwen3.8-27B-Human-KO (Apache-2.0) → FIN5. The training corpus is synthetic data generated by our own serving model.

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Model Details

Model Provider

ThakiCloud

Model Tree

Base

ThakiCloud/Qwen3.8-27B-Human-KO

Fine-tuned

this model

Input Modalities

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Output Modalities

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Supported Functionality