Highlights
* `mx.einsum`: [PR](https://github.com/ml-explore/mlx/pull/1269)
* Big speedups in reductions: [benchmarks](https://github.com/ml-explore/mlx/pull/1300#issuecomment-2303267940)
* 2x faster model loading: [PR](https://github.com/ml-explore/mlx/pull/1330)
* `mx.fast.metal_kernel` for custom GPU kernels: [docs](https://ml-explore.github.io/mlx/build/html/dev/custom_metal_kernels.html)
Core
* Faster program exits
* Laplace sampling
* `mx.nan_to_num`
* `nn.tanh` gelu approximation
* Fused GPU quantization ops
* Faster group norm
* bf16 winograd conv
* vmap support for `mx.scatter`
* `mx.pad` "edge" padding
* More numerically stable `mx.var`
* `mx.linalg.cholesky_inv`/`mx.linalg.tri_inv`
* `mx.isfinite`
* Complex `mx.sign` now mirrors NumPy 2.0 behaviour
* More flexible `mx.fast.rope`
* Update to `nanobind` 2.1
Bug Fixes
* gguf zero initialization
* expm1f overflow handling
* bfloat16 hadamard
* large arrays for various ops
* rope fix
* bf16 array creation
* preserve dtype in `nn.Dropout`
* `nn.TransformerEncoder` with `norm_first=False`
* excess copies from contiguity bug