simplify for style
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@ -480,14 +480,12 @@ static size_t llama_tensor_quantize_impl(enum ggml_type new_type, const float *
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}
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static bool tensor_type_requires_imatrix(const ggml_type dst_type) {
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if (dst_type == GGML_TYPE_IQ2_XXS || dst_type == GGML_TYPE_IQ2_XS ||
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return (
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dst_type == GGML_TYPE_IQ2_XXS || dst_type == GGML_TYPE_IQ2_XS ||
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dst_type == GGML_TYPE_IQ3_XXS || dst_type == GGML_TYPE_IQ1_S ||
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dst_type == GGML_TYPE_IQ2_S || dst_type == GGML_TYPE_IQ1_M ||
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dst_type == GGML_TYPE_TQ1_0 || dst_type == GGML_TYPE_TQ2_0) {
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return true;
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} else {
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return false;
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}
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dst_type == GGML_TYPE_TQ1_0 || dst_type == GGML_TYPE_TQ2_0
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);
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}
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static void llama_model_quantize_impl(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
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@ -1066,6 +1064,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
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LLAMA_LOG_INFO("%s: model size = %8.2f MiB (%.2f BPW)\n", __func__, total_size_org/1024.0/1024.0, total_size_org*8.0/ml.n_elements);
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LLAMA_LOG_INFO("%s: quant size = %8.2f MiB (%.2f BPW)\n", __func__, total_size_new/1024.0/1024.0, total_size_new*8.0/ml.n_elements);
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if (!params->imatrix && params->dry_run && will_require_imatrix) {
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LLAMA_LOG_WARN("%s: WARNING: dry run completed successfully, but actually completing this quantization will require an imatrix!\n",
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__func__
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