a lightweight hybrid reasoning MoE model with 7.9B total parameters and only 1.3B activated parameters per token. It is designed to deliver strong reasoning and agentic capabilities under a small inference compute footprint, making advanced model capabilities more accessible for local and resource-constrained deployment.
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anyone try this? this might be good for my crappy laptop lol
is it good enough to use with Zoo Code? is it better than Qwen 3.5 4b?
EDIT: woa
https://artificialanalysis.ai/models/ling-3-0-tiny
But not yet supported in llama.cpp https://github.com/ggml-org/llama.cpp/pull/26608
I haven’t tried it yet, but I tested one of their previous ones with CPU interference on an Intel n97, and it was one of the best in terms of t/s performance and also prompt response quality on the metrics I tested against. When this run on llama.cpp I will try and give it a test.
personally,
i did one test of ling3-flash and gemma-4-31B side-by-side .
ling3 understood me and had a fantastic answer. gemma must have misunderstood what i was saying… it wrote a long story-like paragraph that didnt answer my question. but it kinda had 1 bit of insight.
so: (maybe…) don’t sleep on ling models !
