arg: fix order to use short form before long form

Author: ServeurpersoCom

common/arg.cpp

@@ -1139,15 +1139,15 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_env("LLAMA_ARG_CTX_CHECKPOINTS").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
-        {"--cache-ram", "-cram"}, "N",
+        {"-cram", "--cache-ram"}, "N",
         string_format("set the maximum cache size in MiB (default: %d, -1 - no limit, 0 - disable)"
             "[(more info)](https://github.com/ggml-org/llama.cpp/pull/16391)", params.cache_ram_mib),
         [](common_params & params, int value) {
             params.cache_ram_mib = value;
         }
     ).set_env("LLAMA_ARG_CACHE_RAM").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
-        {"--kv-unified", "-kvu"},
+        {"-kvu", "--kv-unified"},
         "use single unified KV buffer shared across all sequences (default: enabled if number of slots is auto)",
         [](common_params & params) {
             params.kv_unified = true;
@@ -2073,26 +2073,26 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ));
     add_opt(common_arg(
-        {"--override-tensor", "-ot"}, "<tensor name pattern>=<buffer type>,...",
+        {"-ot", "--override-tensor"}, "<tensor name pattern>=<buffer type>,...",
         "override tensor buffer type", [](common_params & params, const std::string & value) {
             parse_tensor_buffer_overrides(value, params.tensor_buft_overrides);
         }
     ));
     add_opt(common_arg(
-        {"--override-tensor-draft", "-otd"}, "<tensor name pattern>=<buffer type>,...",
+        {"-otd", "--override-tensor-draft"}, "<tensor name pattern>=<buffer type>,...",
         "override tensor buffer type for draft model", [](common_params & params, const std::string & value) {
             parse_tensor_buffer_overrides(value, params.speculative.tensor_buft_overrides);
         }
     ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
-        {"--cpu-moe", "-cmoe"},
+        {"-cmoe", "--cpu-moe"},
         "keep all Mixture of Experts (MoE) weights in the CPU",
         [](common_params & params) {
             params.tensor_buft_overrides.push_back(llm_ffn_exps_cpu_override());
         }
     ).set_env("LLAMA_ARG_CPU_MOE"));
     add_opt(common_arg(
-        {"--n-cpu-moe", "-ncmoe"}, "N",
+        {"-ncmoe", "--n-cpu-moe"}, "N",
         "keep the Mixture of Experts (MoE) weights of the first N layers in the CPU",
         [](common_params & params, int value) {
             if (value < 0) {
@@ -2107,14 +2107,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_env("LLAMA_ARG_N_CPU_MOE"));
     add_opt(common_arg(
-        {"--cpu-moe-draft", "-cmoed"},
+        {"-cmoed", "--cpu-moe-draft"},
         "keep all Mixture of Experts (MoE) weights in the CPU for the draft model",
         [](common_params & params) {
             params.speculative.tensor_buft_overrides.push_back(llm_ffn_exps_cpu_override());
         }
     ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_CPU_MOE_DRAFT"));
     add_opt(common_arg(
-        {"--n-cpu-moe-draft", "-ncmoed"}, "N",
+        {"-ncmoed", "--n-cpu-moe-draft"}, "N",
         "keep the Mixture of Experts (MoE) weights of the first N layers in the CPU for the draft model",
         [](common_params & params, int value) {
             if (value < 0) {