Releases·Model Releasefeatured
IFM researchers host AMA on K2 Horizon, a fleet of six fully open models from 0.9B to 375B
Researchers at the Institute of Foundation Models (IFM) opened an AMA on r/LocalLLaMA about K2 Horizon, a connected fleet of six fully open foundation models ranging from 0.9B to 375B parameters. Alongside weights, IFM open-sourced training data and recipes, training code, intermediate checkpoints, fine-grained training logs, and evals. The AMA session runs Monday, Oct 5, 8–10 PM PT, with questions already open, giving practitioners direct access to the team on pre-training, post-training, MoVA, sparse attention, and deployment.
132 engagement1 sourceThu, Oct 1, 2026, 07:34 PM
AI Overview
- IFM released K2 Horizon, six open models (0.9B–375B) with open training data, code, checkpoints, logs, and evals
- IFM researchers answer questions live Mon, Oct 5, 8–10 PM PT; questions open now on r/LocalLLaMA
- AMA topics include pre-training data mixes, post-training, on-device small models, MoVA, sparse attention, and deployment
Related
Releases·Model Release
IFM releases GGUF quants for K2-Horizon models, sizes 0.9B to 36B-A4B MoVA, on Hugging Face
Community-shared GGUF quantized weights for the K2-Horizon model family are now downloadable from Hugging Face under the IFM organization, spanning sizes from 0.9B to a 36B-A4B MoVA variant. Local inference users need a llama.cpp fork for now, since official llama.cpp support remains an in-progress pull request.
Community-shared GGUF quantized weights for the K2-Horizon model family are now downloadable from Hugging Face under the IFM organization, spanning sizes from 0.9B to a 36B-A4B MoVA variant. Local inference users need a llama.cpp fork for now, since official llama.cpp support remains an in-progress pull request.
50 engagement1 sourceredditTue, Sep 22, 2026, 08:26 PM
Releases·Model Release
Ifm releases K2 Horizon model, claims frontier performance with open availability
Introducing K2 Horizon: Frontier Performance, Radically Open
Introducing K2 Horizon: Frontier Performance, Radically Open
733 engagement1 sourceredditThu, Sep 3, 2026, 02:19 PM
Social·Community
K2 Horizon lineup lands on Artificial Analysis; KV cache design inflates RAM needs despite SOTA Intelligence Index scores
A Reddit analysis of the newly listed K2 Horizon model lineup on Artificial Analysis argues the AA intelligence-vs-parameters plots mislead, because the models' KV cache design inflates RAM needs at long context. The poster maps each tier (3.7B, 7B, 36B-A4B) to the specific hardware where it makes sense, often concluding alternatives like Qwen3.8-27B or MiniCPM5-2B are better buys.
A Reddit analysis of the newly listed K2 Horizon model lineup on Artificial Analysis argues the AA intelligence-vs-parameters plots mislead, because the models' KV cache design inflates RAM needs at long context. The poster maps each tier (3.7B, 7B, 36B-A4B) to the specific hardware where it makes sense, often concluding alternatives like Qwen3.8-27B or MiniCPM5-2B are better buys.
732 engagement2 sourcesredditMon, Sep 14, 2026, 02:01 PM
Developments·Paper
Researchers propose fine-tuning method for sparse attention long-context LLMs
A new paper introduces a fine-tuning method that lets transformer language models co-adapt with any KV cache policy, enabling long-context inference on modest hardware. The approach works on a single Nvidia A100 GPU with 40 GB RAM and often outperforms models trained with exact attention.
A new paper introduces a fine-tuning method that lets transformer language models co-adapt with any KV cache policy, enabling long-context inference on modest hardware. The approach works on a single Nvidia A100 GPU with 40 GB RAM and often outperforms models trained with exact attention.
0 engagement1 sourcearxivThu, Aug 20, 2026, 11:37 AM