IFM releases K2 Horizon, a six-model open-source AI fleet with full training lifecycle
IFM has launched K2 Horizon, a family of six models ranging from 0.9B to 375B-A23B parameters, released under Apache 2.0 with datasets under licenses like ODC-BY. Unlike typical open-weight releases, IFM is publishing the entire training lifecycle—intermediate checkpoints, data recipes, architecture details, training code, logs, and evaluations—alongside final weights. The smallest models (0.9B, 3.7B, 7B) reportedly set new state-of-the-art benchmarks for their size class.
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Most 'open' AI releases only share final weights, leaving researchers unable to understand how a model's capabilities emerged. By exposing the full pretraining-through-agentic-post-training pipeline, IFM aims to make advanced AI reproducible and extensible rather than an opaque artifact, potentially accelerating independent research and reducing dependence on closed labs.
- K2 Horizon includes six models from 0.9B to 375B-A23B parameters, all under open licenses.
- IFM discloses the complete training lifecycle—checkpoints, data recipes, code, and logs—not just final weights.
- The smallest models in the fleet reportedly achieve new state-of-the-art results at their scale.
NVIDIA Jetson Orin Nano Super Developer Kit — K2 Horizon's tiny 0.9B, 3.7B and 7B checkpoints are made for running models yourself, and the Jetson Orin Nano Super Developer Kit is a compact way to do exactly that on your desk. It's a purpose-built edge AI board with NVIDIA's software stack, so you can load open weights and experiment with local inference and agentic tinkering.
See NVIDIA Jetson Orin Nano Super Developer Kit on Amazon → Affiliate link — we may earn a commission on purchases, at no extra cost to you. Product picked by AI based on this article; it is not a tested recommendation.Source: ifm.ai — Institute Of Foundation Models, 2026-09-03
Published there as: “K2 Horizon: Frontier Performance, Radically Open”
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