Model overview
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.*
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
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| Source / channel | Input | Output | Billing unit | Collected at |
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Estimate monthly base token costs
Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.
Public benchmarks & run configuration
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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Do open weights permit free commercial use?
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Sources & traceability
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。