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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.”
This means they have gone through an additional phase where it’s exposed to curated examples of desired behavior.
For instruction tuned models, that means giving it many examples of instructions paired with ideal responses, so it learns to respond more helpfully, politely, or safely to natural language requests.
The gpt-oss models OpenAI put out on August 5 were “reasoning-optimized”: trained and fine-tuned not just to predict the next word, but to follow instructions in a safe, consistent way, often stepping through problems with structured “chain of thought” reasoning before producing a final answer.
This is a trend that goes back to OpenAI’s o1 model released almost a year ago in September 2024, but which numerous leading AI labs have now adopted — forcing the models to think longer over multiple steps and check their own work before outputting a well-reasoned response to the user.
That makes them better suited for tasks like coding, solving math problems, or answering factual questions with explanations — but a …