Pre-release / continuous build: this is an experimental build, not a stable release.
llama-quant : fix 4D tensor quantization using ggml_nrows() and name-based expert detection (#30820) * llama-quant : fix quantizing 4D tensors nrows_total was ne[1]*ne[2], so for a 4D tensor only the first ne[2] slices were quantized and the rest of the rows were dropped. Use ggml_nrows(tensor) to count all rows, including ne[3]. Fixes #29916 * llama-quant : detect expert tensors by name and fix imatrix size for 4D tensors The MXFP4_MOE path used ne[2] > 1 to detect MoE expert tensors. This is wrong for non-expert tensors with 3 or more dims, e.g. MLA projections (BailingMoE3) and 4D conv kernels. Use the tensor name instead: all tensors used by GGML_OP_MUL_MAT_ID are named *_exps or *_chexps. This also removes the BailingMoE3 special case. The imatrix holds one vector of ne[0] values per 2D slice, so the expected size is ne[0]*ne[2]*ne[3], not ne[0]*ne[2]. This matches how the imatrix is indexed per row for 4D tensors in llama_tensor_quantize_impl. Assisted-by: Claude Opus 5.5Website: - https://llama.app
Attestations: - https://github.com/ggml-org/llama.cpp/attestations/59658790
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