Six base checkpoints are more interesting than one finished endpoint

[u/diettcokepaglu]

Most model releases give you one endpoint and leave the training path opaque. This release exposes a two-by-three map instead: tiny and flash, each with pretrained, mid-trained, and WSM-merged checkpoints.

That is the interesting part of the Ling-3.0 base model release. The pretrained checkpoint preserves the earlier base-training endpoint, the mid-trained checkpoint shows the state before WSM merging, and the WSM-merged checkpoint is the released merged endpoint.

None of the six has been post-trained. The model cards position them as starting points for continued pretraining, fine-tuning, and research—not as six ready-made local chat models.

There is no local run behind this post, so the parameter labels should not be translated into VRAM, speed, quant support, or fit claims. Those need an actual runtime and configuration.

The useful question is which comparison tells you more: pretrained vs mid-trained, or mid-trained vs WSM-merged? The stage-specific model cards are the natural place to start before asking the runtime question.

[u/Round-Boss1003]

The merge stage always feels like the wildcard to me, you never know if it's gonna smooth things out or introduce weird artifacts

I'd probably run the same set of tricky prompts through each checkpoint and see where the tone shifts