Latest

A sparse mixture-of-experts design that keeps only 2B parameters active per token lets a 24B model run locally while matching a larger dense-activation rival on tool-use work, changing what is practical on consumer hardware. Developers building local agent workflows can pull it with a single Ollama command or grab the open weights on Hugging Face to fine-tune. Compared with Qwen3.6-35B-A3B, it targets the same agentic niche at lower active-parameter cost.

ollama@ollamaSource

The 2B active-parameter design targets efficiency over raw capability, so the interesting question is how it trades benchmark rank for cheaper inference against larger dense models. Developers running edge or high-throughput deployments can evaluate the weights directly from the release link. The comparison with Qwen gives a concrete starting point for choosing between the two families.

Gorden Sun@Gorden_SunSource