Two fine-tuned versions of Qwen 3.6 35B—Apex 35B and Axion 35B—are now available on Hugging Face, but discussion remains largely confined to a single Reddit thread, with almost no hands-on feedback from the community. Our judgment is that open-source competition among large models is shifting from “releasing bigger models” to “making more devices able to run them affordably and effectively.” Even so, neither version has yet demonstrated broad adoption value.
What this is
Fine-tuning—continuing to train an existing model so it specializes in particular tasks or styles—is not training a new model from scratch. According to a Reddit user, both versions are based on Qwen 3.6 35B: Apex was released by Apex Intelligence AI, while Axion is associated with ByteDance, although public information about it remains scarce.
These models are generally intended for local deployment—downloading the model to run it on a personal computer or server without relying on a cloud API. For users who prioritize privacy, offline access, or call costs, whether a model runs smoothly on existing hardware often matters more than its ranking on leaderboards.
Industry view
Notably, the open-source community is beginning to evaluate new models by asking “has anyone actually used it?” rather than judging value solely from the download page or parameter count. Limited discussion may mean the models lack clear differentiation, but it could also reflect a recent release whose visibility has not yet spread.
The risks are equally clear: there are currently almost no third-party benchmarks, quantized-version documentation, or stable toolchain support. The real-world performance, license restrictions, VRAM requirements, and maintenance quality of Apex and Axion cannot be inferred from their model names alone. We do not equate “built on a popular base model” with “more advanced.” Until reproducible testing is available, they are better viewed as experimental options.
Impact on regular people
For enterprise IT: Local models may reduce the risks of sending sensitive data outside the organization and lower ongoing API costs, but teams still have to bear the hardware, deployment, and maintenance burden. At this stage, they should not directly replace production systems.
For individual professionals: Efficiency in drafting materials, organizing information, and running small tools is likely to continue improving. However, “35B” does not mean lightweight, and the ability to run reliably on an everyday computer remains the barrier to adoption.
For the consumer market: More vendors may integrate offline or private AI into note-taking software, productivity assistants, and professional tools. Pricing will increasingly focus not simply on selling model access, but on compute efficiency and the concrete user experience.