Today IFM is releasing K2 Horizon, a connected fleet of six models: 375B-A23B, 36B-A4B, 32B, 7B, 3.7B, and 0.9B. Across reasoning, mathematics, coding, agentic tasks, and general capabilities, K2 Horizon delivers top-tier performance in every size class—with the 0.9B, 3.7B, and 7B models setting new state of the art at their respective scales.
We are releasing intermediate checkpoints, training data or detailed data-construction recipes, open architecture, mixture compositions, training code, configurations, fine-grained logs, evaluation results, and final weights.
The models and code are released under the Apache 2.0 license. Datasets are released under their applicable licenses, such as ODC-BY; We disclose how the data was constructed and mixed when redistribution is not possible.
As far as I can tell, open-webui doesn’t have a way to manage gguf models via llama.cpp / llama-swap. It can leverage the models just not a great experience managing (downloading, deleting, etc). I can definitely adjust my workflow to use another tool, it just means an additional hurdle compared to my current flow but it sounds like it’s worth it from a performance standpoint
Eh, are you really using that many new models where sshing into your server / cding into your model folder, wgetting a url and configuring your interfaces every now and then is that much of an issue? I can’t imagine using more than like one or two new models a month unless there’s some insane string of releases or something.
Also, I mean, everyone’s setup is different, but there’s a significant amount of performance you’re potentially leaving on the table by not using llama.cpp, potentially in the double-digit percentages. (Plus, if you have a fairly recent Nvidia setup and are willing to wait a bit for the latest models, ik_llama.cpp is a fantastic fork that I’ve found can get way better performance on most models than even llama.cpp.)