Model Overview

Ling-3.0-tiny inherits the hybrid linear attention architecture of Ling-3.0 series, while being specifically optimized for lightweight and accessible deployment. The model has 7.9B total parameters, with only 1.3B parameters activated per token.

The architecture of Ling-3.0-tiny is designed to make computational efficiency serve real-world agentic performance.

• A 3:1 KDA–MLA architecture (3 KDA layers and 1 MLA layer per 4-layer block) provides more efficient long-context processing;
• A sparse MoE FFN with 128 experts activates 8 routed experts and 1 shared expert per token, enabling broad model capabilities with only 1.3B activated parameters per token.
• Native hybrid reasoning enables fast responses for routine tasks and multi-step reasoning for complex tasks within a single model.

Overall, these designs deliver the inference efficiency needed to deploy lightweight models in real-world agentic workflows.

Model developed by inclusionAI. For the full model card, see inclusionAI/Ling-3.0-tiny.

To pull the original weights directly, run:

ollama pull hf.co/inclusionAI/Ling-3.0-tiny-GGUF:Q8_0