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OpenAI’s new, powerful open weights AI large language model (LLM) family gpt-oss was released less than two weeks ago under a permissive Apache 2.0 license — the company’s first open weights model launch since GPT-2 in 2019 — but developers outside the company are already reshaping it.
One of the most striking examples comes from Jack Morris, a Cornell Tech PhD student, former Google Brain Resident, and current researcher at Meta, who this week unveiled gpt-oss-20b-base, his own reworked version of OpenAI’s smaller gpt-oss-20B model, which removes the “reasoning” behavior of the model and returns it to a pre-trained “base” version that offers faster, freer, more uncensored and unconstrained responses.
The model is available now on Hugging Face under a permissive MIT License, allowing it to be used for both additional research and commercial applications.
How gpt-oss-20B-base is different than OpenAI’s gpt-oss models
To understand what Morris did, it helps to know the difference between OpenAI’s release and what AI researchers call a “base model.”
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Most LLMs offered by leading AI labs such as OpenAI, Anthropic, Google and even open source players like Meta, DeepSeek, and Alibaba’s Qwen team are “post-trained.”
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