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AI 模型库/webAI-Official/TwIL-LM3
MODEL PROFILE · 模型档案

TwIL-LM3

webAI-Official · TwIL-LM3

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.

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档案更新时间2026/11/2 19:17:03
OVERVIEW

模型介绍

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.

主要特点

特点摘要待依据来源补齐，不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

PRICING & COST

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价，不将渠道价格视为厂商官方统一价格。

来源 / 渠道输入输出计费单位采集时间
暂未取得可核验报价，请查阅来源渠道。

按月估算基础 Token 费用

—

预估不包含缓存优惠、阶梯定价、多模态附加费用、网络检索、税费及推理额外输出。

INDEPENDENT BENCHMARKS

公开评测与运行配置

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。

查看完整已收录榜单 →

常见问题

开放权重是否代表可以免费商用？

不一定。应核对具体版本许可证、商用门槛和使用政策。自部署还会产生算力和维护成本。

这些价格是实时结算价格吗？

不是实时结算价格，而是带采集时间的来源报价快照。请到对应渠道核对当前版本、区域、折扣和计费方式。

资料来源与可追溯记录

huggingface · webAI-Official/TwIL-LM3 ↗2026/10/29 06:08:09
huggingface · enrich:57079714-5a9e-4ea5-a26a-d902e0c4cc97 ↗2026/10/29 00:50:56

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。