webAI Releases TwIL-LM: A 1.7B and 3B Formal-Logic Model Family for Autoformalization on Local Hardware
webAI has released TwIL-LM, a two-model family of formal-logic reasoners at 1.7B and 3B parameters. The 3B member, TwIL-LM3, is a merged fine-tune of SmolLM3-3B; the 1.7B member is a PEFT LoRA adapter for SmolLM2-1.7B-Instruct. Both target autoformalization: translating English into first-order logic and checking whether a conclusion follows from its premises. Both run locally, with a 1.06 GB quantized build for the 1.7B and a 1.78 GiB Q4_K_M GGUF for the 3B. webAI’s announcement frames t
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webAI has released a family of small AI models designed to translate English into formal logic and verify whether conclusions follow from stated premises. The models are sized at 1.7 billion and 3 billion parameters and can run on local hardware without sending data to external servers, making them relevant for industries like finance, healthcare, and legal services where data privacy is critical. The larger 3-billion model performs formal reasoning tasks more efficiently than much larger competing models, generating shorter responses and answering more questions per second, though it trails a 120-billion parameter model on overall accuracy. The models are currently available only for non-commercial use, with commercial deployment requiring a separate agreement.
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