MODEL PROFILE · 模型档案

TwIL-LM3-Pro

webAI-Official · TwIL-LM3-Pro

A 3.66B reasoning model for **formal logic** tasks, built from `ibm-granite/granite-4.2-3b` 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 **+28% relative** over its base model (macro gate 0.431 → 0.554) **while holding held-out benchmark performance** — the 10-dataset macro is level with the base (0.7942 → 0.7901) and the 14-dataset macro improves slightly (0.7332 → 0.7425). On the same harness and sampled rows it reaches the highest Track A macro gate, strict-7 and six-lane average of any arm in the tables below for which each can be computed, including Qwen3-8B, Qwen3.5-4B, VibeThinker-3B and gpt-oss-120b (the 120B has no gate or strict-7 value). *VibeThinker-webAI-trained is the public VibeThinker-3B after the same post-training pipeline (SLERP, d = 0.5, t = 0.5); see Against the tuned VibeThinker-3B. It has no strict-7 or six-lane-average value, so those bars show n/a.*

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文本生成reasoninglicense:other
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输入 → 输出文本 → 文本
模型权重待核验
档案更新时间2026/10/30 09:17:29
OVERVIEW

模型介绍

A 3.66B reasoning model for **formal logic** tasks, built from `ibm-granite/granite-4.2-3b` 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 **+28% relative** over its base model (macro gate 0.431 → 0.554) **while holding held-out benchmark performance** — the 10-dataset macro is level with the base (0.7942 → 0.7901) and the 14-dataset macro improves slightly (0.7332 → 0.7425). On the same harness and sampled rows it reaches the highest Track A macro gate, strict-7 and six-lane average of any arm in the tables below for which each can be computed, including Qwen3-8B, Qwen3.5-4B, VibeThinker-3B and gpt-oss-120b (the 120B has no gate or strict-7 value). *VibeThinker-webAI-trained is the public VibeThinker-3B after the same post-training pipeline (SLERP, d = 0.5, t = 0.5); see Against the tuned VibeThinker-3B. It has no strict-7 or six-lane-average value, so those bars show n/a.*

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

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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

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。