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LFM2.5-8B-A1B-KO-CPT-Repair-BarExamV5-SFT
LLM-OS-Models/LFM2.5-8B-A1B-KO-CPT-Repair-BarExamV5-SFT
LFM2.5-8B-A1B-KO-CPT-Repair-BarExamV5-SFT is a text generation model from LLM-OS-Models. Use it when you need the model to write or continue text. The card lists the license as other.
This is a diagnostic legal-context SFT checkpoint trained on top of LFM2.5-8B-A1B-KO-CPT-Repair-SFT. It is published to preserve the experiment and should not be presented as the main public benchmark model.
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From the Hugging Face model README
LFM2.5-8B-A1B-KO-CPT-Repair-BarExamV5-SFT
This is a diagnostic legal-context SFT checkpoint trained on top of
LFM2.5-8B-A1B-KO-CPT-Repair-SFT.
It is published to preserve the experiment and should not be presented as the
main public benchmark model.
The current recommendation is still the KO-CPT checkpoint:
LLM-OS-Models/LFM2.5-8B-A1B-KO-CPT-FULL.
- SFT GitHub: https://github.com/gyunggyung/LFM25-KO-SFT
- CPT GitHub: https://github.com/gyunggyung/LFM25-KO-CPT
- Repair SFT: https://huggingface.co/LLM-OS-Models/LFM2.5-8B-A1B-KO-CPT-Repair-SFT
Training Data
This stage was designed to teach a narrow legal-context behavior: given a bar-exam style question plus a curated evidence packet, read the legal grounds, compare choices, and output a normalized answer.
| item | value |
|---|---|
| prepared dataset | /home/work/.data/lfm2_ko_sft/prepared/bar_exam_v5/20260630_bar_exam_v5_context_solver_8192/lfm_chat_8192 |
| samples | 6,374 |
| LFM tokens | 5,863,863 |
| max sequence length | 8,192 |
| epochs | 1 |
| learning rate | 5e-7 |
| planned optimizer steps | 57 |
The data includes 1-14th bar-exam safe numeric/symbol answer variants, current law-bar synthetic tasks, 15th v5 evidence-reading procedure samples, legal search-first-action samples, full v5 grounded solutions, and compact v5 grounded answers.
Gate Evaluation
Evaluation root:
/home/work/.data/lfm2_ko_sft/eval/bar_exam_v5_sft_gate_20260630T1306KST
Base/CPT reference values are copied from the KO-CPT model card where available. Some metrics differ from this gate run, so rows marked with a metric mismatch should be read directionally rather than as strict apples-to-apples comparisons.
| task | Base | KO-CPT | Repair-SFT | BarExamV5-SFT | v5 metric | verdict |
|---|---|---|---|---|---|---|
| BoolQ | 0.6544 | 0.7902 | 0.663303 | 0.658716 | acc,none | below CPT, slightly below Repair |
| ARC-Challenge | 0.3771 | 0.4241 | 0.211604 | 0.209898 | acc,none; CPT uses acc_norm | below Base/CPT |
| IFEval | 0.2921 | 0.3216 | 0.181146 | 0.188540 | strict prompt acc; CPT uses loose prompt acc | small gain vs Repair, below CPT |
| Global MMLU KO jurisprudence | 0.2870 | 0.2685 | 0.250000 | 0.296296 | acc,none | narrow gain vs Repair/CPT |
| KMMLU direct hard | 0.2015 | 0.1720 | 0.102339 | 0.101608 | exact_match,none; CPT card uses acc,none | below Base/CPT |
| KMMLU direct hard law | n/a | n/a | 0.170000 | 0.190000 | exact_match,none | small legal-category gain |
| MMLU-ProX Lite KO | 0.2585 | 0.1667 | 0.091837 | 0.068027 | exact_match,custom-extract | worsened vs Repair |
| MMLU-ProX Lite KO law | n/a | n/a | 0.104167 | 0.020833 | exact_match,custom-extract | severe extraction regression |
Interpretation
This stage produced a narrow jurisprudence gain, but it harmed broader multiple-choice/exact-extraction behavior. That is consistent with the training objective: long legal solution traces can push the model toward verbose legal reasoning and away from concise answer extraction.
Final lesson: Korean bar-exam style solving should be treated as an evidence-grounded workflow problem, not as standalone SFT memorization. The model needs curated legal context, explicit O/X judgment, symbol-to-number choice mapping, and strict extraction gates. This diagnostic SFT showed that full-solution traces alone are not enough for reliable open-model bar-exam performance.
For the next attempt, this should be split into either a separate legal-context adapter/model with its own held-out v5 evaluator, or a much smaller late-stage injection after a proven repair checkpoint. It should not be merged into the main public benchmark line without an early gate that preserves KMMLU, MMLU-ProX, GSM8K, ARC, BoolQ, and IFEval.
- Task
- Text Generation
- Type
- lfm2_moe
- License
- other
- Languages
- ko, en
- Created
- Jun 30, 2026
- Updated
- Jun 30, 2026