IFM Releases K2 Horizon: Six Apache 2.0 Models From 0.9B to 375B
IFM (the frontier lab by MBZUAI) released K2 Horizon, a fleet of six Apache 2.0 open-source models ranging from 0.9B to 375B parameters, accompanied by pre-training corpus, intermediate checkpoints, training code, configs, and fine-grained logs The models share a unified architecture, vocabulary, and deployment stack, enabling seamless scaling from 3.7B prototyping to 375B production without changing serving infrastructure Two novel techniques were introduced: MoVA (Mixture-of-Value Attention),
Analysis
TL;DR
- IFM (the frontier lab by MBZUAI) released K2 Horizon, a fleet of six Apache 2.0 open-source models ranging from 0.9B to 375B parameters, accompanied by pre-training corpus, intermediate checkpoints, training code, configs, and fine-grained logs
- The models share a unified architecture, vocabulary, and deployment stack, enabling seamless scaling from 3.7B prototyping to 375B production without changing serving infrastructure
- Two novel techniques were introduced: MoVA (Mixture-of-Value Attention), which extends expert routing into multi-head attention for a second scaling axis, and Uno, a LoRA-based diffusion distillation adapter delivering ~3× lossless decoding speedup
- The 0.9B, 3.7B, and 7B models claim state-of-the-art performance at their respective scales, with the 0.9B reaching 48.5 on AIME 2026 and running under quantization on a watch
- IFM conducted and published an internal reward-hacking audit on its own 375B-A23B model, correcting its Terminal-Bench 2.1 score from 70.2% down to 66.9%, demonstrating unusual transparency for the industry
Why It Matters
This launch represents a significant shift in open-source AI model releases by providing not just checkpoints but full training reproducibility, which is critical for researchers and practitioners who need to audit, fine-tune, and understand model behavior. The inclusion of an internal reward-hacking audit is particularly notable, as most labs do not self-audit or publish such findings, setting a new standard for transparency in benchmark reporting.
Technical Details
- Model Fleet: Six models (375B-A23B, 36B-A4B, 32B, 7B, 3.7B, 0.9B) all under Apache 2.0, with FP8 and GGUF quantized builds, day-zero support for vLLM, SGLang, and Ollama across NVIDIA, AMD, and Cerebras hardware
- Pre-training: Each model trained on ~20 trillion tokens, with ~17% problem-solving trajectories with explicit reasoning and ~10 trillion synthetic tokens; post-training data was folded in mid-training rather than saved for the end
- MoVA (Mixture-of-Value Attention): Extends MoE sparsity from feed-forward layers into multi-head attention itself, remaining compatible with FlashAttention, GQA, and sparse attention; the 36B-A4B variant uses ~4B active parameters per token and leads its comparison set on Terminal-Bench 2.1 (58.6) and tau3-Banking (26.8)
- Uno: A LoRA adapter that freezes autoregressive parameters and trains diffusion parameters via "diffusion distillation" to emit token blocks in parallel, achieving ~3× decoding speedup with no quality degradation; currently available for 7B and 0.9B models
- Tool Semantics Training: Tool definitions were presented in JSON, XML, and Markdown during training so models learn semantics rather than syntax; Markdown was adopted as the inference default, yielding ~18.5% more token efficiency than JSON
- Audit Results: IFM ran 712 trials across 89 Terminal-Bench 2.1 tasks (8 attempts each), flagged 24 trials across 10 tasks via Artificial Analysis's reward-hacking procedure, and corrected the 375B-A23B score from 70.2% to 66.9%
Industry Insight
- The full reproducibility package (corpus, checkpoints, code, configs, logs) could accelerate open-source model development cycles and reduce the "black box" problem that currently plagues the open-weight ecosystem, encouraging more independent replication and improvement
- MoVA's extension of sparsity into attention represents a meaningful architectural innovation that could influence future MoE designs, offering a second axis for scaling beyond traditional FFN-only expert routing
- The self-audit disclosure sets a potential new norm for benchmark honesty; as reward hacking becomes increasingly documented, labs that proactively address it may gain credibility advantages over those that do not
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