Abu Dhabi-based research institute IFM has released six artificial intelligence models together with their training data, code, methodologies and intermediate checkpoints, positioning the K2 Horizon initiative as a push toward more transparent and reproducible AI development.
The release includes model weights and the underlying materials used to build the systems, allowing researchers to examine how the models were trained and attempt to reproduce the results.
IFM founder Eric Xing said the objective is to establish a benchmark for what a genuinely open AI model release could look like and demonstrate that transparency does not necessarily come at the expense of competitive performance.
K2 Horizon goes beyond open-weight releases
The K2 Horizon release is broader than the open-weight model approach used by some AI developers.
Open-weight models typically allow users to download and run trained models, but may provide limited visibility into the datasets, training procedures and development decisions behind them.
IFM is instead releasing a wider set of development assets, including training data, code, methodologies and intermediate checkpoints.
That structure gives researchers more insight into how individual models evolved during training and could make independent evaluation and reproduction easier.
Six models span edge devices to enterprise-scale AI
The K2 Horizon family includes six models covering a wide range of computing environments.
At the smaller end, IFM has developed a model intended for constrained devices such as smartwatches.
At the upper end, the family includes a 375-billion-parameter system designed for enterprise deployments.
The broad range suggests IFM is targeting use cases extending from edge computing and embedded AI to large-scale organisational applications.
The source does not provide performance benchmarks or detailed architectural specifications for each model.
IFM challenges increasingly closed AI development
The release comes as parts of the AI industry move toward more restrictive development models.
Companies such as OpenAI and Anthropic do not generally make their frontier models available for download and do not publicly disclose the complete training data and methodologies used to build them.
Some other developers have adopted more open distribution models, particularly by releasing model weights, but still keep substantial parts of the development process private.
IFM is attempting to differentiate K2 Horizon by making the full development path more visible.
Openness positioned as policy issue
Xing said the initiative is intended not only for researchers but also for policymakers, regulators and public-interest organisations.
The institute wants to demonstrate that openness and competitive AI performance do not have to be treated as mutually exclusive objectives.
That argument could become increasingly relevant as governments consider how much transparency should be expected from advanced AI systems, particularly around training data, reproducibility, safety testing and model governance.
UAE expands AI research ambitions
The launch also fits within the UAE’s broader ambition to strengthen its position in advanced artificial intelligence research and development.
The country has been investing across AI infrastructure, foundation models, research institutions and commercial applications as it seeks to build a larger domestic technology ecosystem.
By releasing the models and associated development materials openly, IFM is taking a different strategic approach from organisations focused primarily on proprietary model access.
Why this matters
The most important aspect of K2 Horizon is not simply that six new AI models have been released.
The more significant move is IFM’s decision to expose much more of the development process, including training data, code and intermediate checkpoints.
That level of disclosure could improve reproducibility, enable deeper independent scrutiny and give researchers more information for understanding how model behaviour changes during training.
It also adds another dimension to the global debate over what “open AI” should actually mean, particularly as open-weight releases become increasingly common but often stop short of full transparency.
Editor’s note
K2 Horizon could become a useful test of whether genuinely open development can remain competitive as AI models grow larger and more expensive to train.
The key questions will be whether independent researchers can successfully reproduce IFM’s results, how the models perform against established alternatives and whether the released training data is sufficiently documented for meaningful scrutiny.
If those conditions are met, IFM could help push the industry conversation beyond open weights toward a more demanding standard built around reproducibility, inspectability and transparency across the full model development lifecycle.
