IFM releases GGUF quants for K2-Horizon models, sizes 0.9B to 36B-A4B MoVA, on Hugging Face

Releases·Model Release

IFM releases K2-Horizon-7B, diffusion-augmented LLM claiming up to 5200 tps lossless

IFM released K2-Horizon-7B on Hugging Face, a diffusion-augmented LLM that adds a plug-and-play diffusion adapter to a causal autoregressive architecture. IFM claims speeds up to 5200 tokens per second with no quality loss, with a paper on arXiv (2609.04010).

IFM released K2-Horizon-7B on Hugging Face, a diffusion-augmented LLM that adds a plug-and-play diffusion adapter to a causal autoregressive architecture. IFM claims speeds up to 5200 tokens per second with no quality loss, with a paper on arXiv (2609.04010).

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.

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.

Social·Community

User audit finds 64 of 443 GGUF quants mislabeled as llama-quantize swaps in higher-bitwidth fallbacks

A community audit of 443 GGUF quants across 25 repos found 64 files whose contents don't match their filename's claimed quant type. The cause: k-quants and i-quants need tensor rows divisible by 256, and when they aren't, llama-quantize silently substitutes a ~4.5 bpw fallback while keeping the low-bit name. Follow-up posts show AtomicChat quants affected and a patched-llama.cpp workaround producing a true 3.07 bpw, 11.77 GiB Nemotron-3.5-Lightning file that runs 262K context on 16 GB.

A community audit of 443 GGUF quants across 25 repos found 64 files whose contents don't match their filename's claimed quant type. The cause: k-quants and i-quants need tensor rows divisible by 256, and when they aren't, llama-quantize silently substitutes a ~4.5 bpw fallback while keeping the low-bit name. Follow-up posts show AtomicChat quants affected and a patched-llama.cpp workaround producing a true 3.07 bpw, 11.77 GiB Nemotron-3.5-Lightning file that runs 262K context on 16 GB.

Social·Community

Community discovers Spark-X2.5-4B and 1.7B small LLMs with native 1M context on Hugging Face

A Reddit user browsing Hugging Face found Spark-X2.5-4B and Spark-X2.5-1.7B from XHToken, small models with their own architecture rather than fine-tunes. The 4B reportedly benchmarks neck and neck with Qwen 3.5 9B, and both claim native 1M context. They do not yet run out of the box on llama.cpp.

A Reddit user browsing Hugging Face found Spark-X2.5-4B and Spark-X2.5-1.7B from XHToken, small models with their own architecture rather than fine-tunes. The 4B reportedly benchmarks neck and neck with Qwen 3.5 9B, and both claim native 1M context. They do not yet run out of the box on llama.cpp.

News·Incident

llama.cpp breaking change forces regeneration of all GGUF files

A new release of llama.cpp includes a breaking change that invalidates all previously generated GGUF files. The project's release notes explicitly state that all GGUFs generated before this change must be regenerated, indicating a significant format or compatibility update. Users and developers relying on GGUF models will need to regenerate their files to work with the latest version.

A new release of llama.cpp includes a breaking change that invalidates all previously generated GGUF files. The project's release notes explicitly state that all GGUFs generated before this change must be regenerated, indicating a significant format or compatibility update. Users and developers relying on GGUF models will need to regenerate their files to work with the latest version.