OrbitQuant 0.2.0

OrbitQuant 0.2.0 extends calibration-free quantization from the paper-specific
Diffusers targets to transformer backbones across modalities.

Highlights

• Universal structural policy for registered linear-compatible modules, with
  machine-readable coverage inspection before quantization.
• Built-in adapters for torch.nn.Linear and Hugging Face Conv1D, plus a
  public adapter API for custom weight layouts.
• Named W2A3, W2A4, W3A3, W4A4, and W4A6 recipes and a direct
  quantize_model() API.
• Transformers quantize-on-load and packed save_pretrained() /
  from_pretrained() support, including base-model prefixes and transposed
  GPT-2 projections.
• Shape-generic packed CUDA, Triton, and Metal inference paths for arbitrary
  leading dimensions, short autoregressive decode rows, and partial matrix
  tiles without full weight materialization.
• Paper-specific FLUX.1, FLUX.2, Z-Image, and Wan policies remain unchanged and
  pass their exact module-inventory methodology gate.

Architecture compatibility does not imply that every low-bit recipe preserves
quality on every model. Inspect coverage and validate the selected bit profile
for the target task before publishing a checkpoint.