Institute of Foundation Models

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/IF…

Jais Chat mobile app is now live!🎙

The most capable Arabic AI model is in your pocket.
Skip the typing, talk to your phone in dialects and get the up-to-date information from web searches.

“Jais Chat” free on iOS and Android:
apps.apple.com/ae/app/jais-ch…
play.google.com/store/apps/det…

In September, we said that K2 Horizon was the largest LLM open drop in AI history yet. We’ve now delivered on the commitment.

When people say open models, they usually mean open weights. K2 Horizon 360 is way more: 8 artifact categories and 30+ repositories across every stage of LLM training.

- Checkpoints: 380 across the six models, pre-training through RL, so you can trace when a capability first appeared
- Data: 13 trillion pre-training tokens across 5 datasets and 24 subsets
- Data tools: 11 repositories, 7 task generation and 3 advanced generation pipelines
- New architecture: MoVA in our 36B-A4B model, MoE sparsity applied to the value vectors in multi-head attention, compatible with efficient attention algorithms at no additional KV-cache cost
- Pre-training infra: xLLM, faster pre-training and fine-tuning, with rework and reconfiguration at lower cost
- Post-training infra: RL360 for long-horizon agentic RL, 8192 concurrent sandboxes, 150 steps in 7 days, ~1.3M rollouts. It separates system crashes from the model’s own mistakes
- Training logs: 58 runs, with loss curves, gradient norms and the configs that actually ran, so you can check your own against a real one at this scale
- Diffusion LLM: Uno, autoregressive quality at diffusion speed

Technical details in previous threads, full artifact directory →
ifm.ai/k2/artifacts/

K2 Horizon now runs on llama.cpp and Ollama, from 0.9B to MoVA-36B.

It runs entirely on local hardware, with no cloud API needed.

GGUF weights: huggingface.co/collections/IF…

Ollama: ollama.com/ifm/k2-horizon

Today we dropped a technical report on Jais 3, a six-fleet Arabic-centric agentic models from 0.9B to 375B.

Quick highlights:

- Adapted from K2 Horizon: +9 to +35 on the Arabic average over the base model
- Agentic at small sizes: 3.7B and 7B beat every open model of their size on all 10 agentic benchmarks
- FTPO: a short preference stage after SFT, repetition loops from 13.8% to 1.2%
- Audited Arabic benchmarks: 15 widely used Arabic benchmarks turned out to contain wrong answers, and we audited to remove the 5.3% of items our three judges flagged

Weights, checkpoints from every stage, data samples, logs and eval code, all open 👉
ifm.ai/jais/research