TwIL-LM3

모델 소개

A 3B reasoning model for **formal logic** tasks, built from `HuggingFaceTB/SmolLM3-3B` through LoRA supervised fine-tuning, checkpoint fusion, WiSE-FT weight interpolation, and entropy-weighted GRPO reinforcement learning. It improves in-domain formal-logic performance by **+26% relative** over its base model (macro gate 0.336 → 0.422) **and improves held-out benchmark performance at the same time** (+0.022 core average). It is the only arm in this project that gains on both tracks, which is why it is the recommended release of the pair. * **Gains on both tracks at once** — the only arm in this project that does. In-domain macro gate 0.336 → 0.422, and the held-out 10-dataset macro 0.7193 → 0.7339 rather than the usual collapse that follows task-specific fine-tuning. * **Beats every arm up to and including LFM2.5-8B-A1B** — roughly three times its parameter count — on all six Track A objective lanes and all four summary rows, not on average alone. * **Competitive with 8B on strict scoring.** On strict-7, which gives no loose-match credit anywhere, it sits 0.012 behind Qwen3-8B (0.1971 against 0.2093) at 2.6x fewer parameters, and ahead of it on Lean formalisation (token-F1 0.5869 against 0.4022) and semantic parsing (0.4416 against 0.4257). * **Structured formal output.** Tuned for the objects rather than the prose: FOL translation, entailment labels, semantic parses, Lean statements and Lean proof critique. * **The most efficient arm measured, at any scale.** 482-token Track B generations and 32.9 completed answers per second — about eight times gpt-oss-120b's rate — because it answers short rather than because it decodes unusually fast. * **Lowest maths-corpus perplexity of any released arm in the table** (3.8229), including Qwen3-8B at 4.0083. * **Runs anywhere.** 3.08B parameters in bf16, with Q4\_K\_M GGUF at 1.78 GiB for CPU or 4 GB of VRAM.

주요 특징

特点摘要待依据来源补齐，不凭模型名称推断能力。

한계 및 유의 사항

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출처별 요금 및 비용 분석

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공개 평가 및 실행 설정

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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출처 및 추적 기록

许可证：other。模型介绍与第三方评测均以原来源的具体版本和方法为准。