Introducing K2 Horizon: a connected fleet of six foundation models ranging from 0.9 billion to 375 billion parameters. - Frontier performance: Across coding and agentic tasks, 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. - Radical openness: K2 Horizon represents the largest fully open-source model launch in AI history. The fully open code, training data and recipes are a significant step forward in transparency. Launch page: ifm.ai/k2/ Tech blog: ifm.ai/blog/k2 Hugging Face: huggingface.co/collections/I…

Day-0 support for Jais 3 on @sgl_project, so an Arabic-first agentic model is now straightforward to serve at scale. Thank you to the SGLang team! Weights: huggingface.co/collections/I… API: platform.ifm.ai

Huge thanks to the vLLM team! With Day-0 support on @vLLM, Jais 3 now runs on both of the serving stacks most teams already have. Weights: huggingface.co/collections/I… API: platform.ifm.ai/

Jais 3, the first adaptation of K2 Horizon. Coming soon. #Jais3

Hector Liu, IFM's Head of Technology and US Lab Director, walked MBZUAI through how K2 Horizon was built. A rare, candid close-up of LLM training. From the room: - Two new attention designs: softplus gated attention is a more expressive form of gated attention; Mixture-of-Value Attention adds capacity at no extra KV cache cost. - Nearly half the pre-training data was synthetic. An interesting generation method: Socratic dialogues, a teacher model questioning a student model toward the answer. - A scaling law guided learning-rate choices across model sizes. Four models trained on the same data learned at the same pace, with normalized loss curves lining up almost perfectly. More in the upcoming paper.

K2-Type, our open Jev-style decision model, now runs free in your browser on ZeroGPU. Huge thanks to @multimodalart from @huggingface for spinning up the Space! Ask Yes/No, Choice, or Score questions and get structured answers in one pass, no GPU or API key needed. Try it 👇 huggingface.co/spaces/IFM/k2…

Scaling up a model has meant paying twice, in compute and in memory. Our researchers show that looped models can pay in compute alone, using the loop's fixed point as a shortcut. A 1.6B looped model runs twelve blocks deep on four blocks of memory. On the same tokens, it beats a four-block Transformer by 17.5% and comes within 2.4% of a twelve-block one needing 3x memory. It can also train, pre-fill, and post-train without paying for every loop. Paper: arxiv.org/abs/2610.06833 Fully open-sourced at: github.com/ifm-ai/xllm-loop