`fuse_projections` promises "one weight copy", and on a freshly loaded model it keeps that promise. It `torch.cat`s each group (q/k/v/gate, w1/w3) into a new tensor, the separate linears are dropped, and their storage is freed.
The promise breaks when a group's linears are already slices of one merged tensor that something else keeps, for example code that merged the same linears for its own matmul. Then the copy can't free anything, and building `SystemOne` holds the language model's projections twice.
This change stacks a group as a view when its weights already sit back to back in one storage, in order, and copies otherwise.
- **Freshly loaded models are unchanged.** Each linear owns its own storage, so every group still takes the copy path.
- **Pre-merged weights get one copy.** The fused weight is that same storage, so the model holds the group once.
**Repro.** Merge each language-model group into one tensor that a separate list keeps, then point the linears at its slices:
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