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* Refactored Compressed Tensors NVFP4 support for new base.py * Support compressed-tensors NVFP4 conversion * Moved Qwen MTP remap into filter_tensors * simplify * pathlib no longer used --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* CUDA: add fast walsh-hadamard transform * review: add unrolls + change size_t -> int * warp size 64 --------- Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
ffn_latent_down/up are declared GGML_OP_MUL in LLM_TENSOR_INFOS but nemotron-h feeds them through ggml_mul_mat. The loader buft probe asks the backend about the declared op, so it tested an elementwise MUL on a q8_0 weight. That used to return true unconditionally and the weight stayed on GPU by luck. Once supports_op told the truth, the probe got a no and the loader pushed the weight and its matmul to CPU, splitting the graph. Tagging it MUL_MAT asks the real question, the math is unchanged. Verified on Nemotron 3 Super 120B Q5_K_M: from 64.9 back to 103.22 t/s.
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