common : refactor cli arg parsing (#7675)
* common : gpt_params_parse do not print usage * common : rework usage print (wip) * common : valign * common : rework print_usage * infill : remove cfg support * common : reorder args * server : deduplicate parameters ggml-ci * common : add missing header ggml-ci * common : remote --random-prompt usages ggml-ci * examples : migrate to gpt_params ggml-ci * batched-bench : migrate to gpt_params * retrieval : migrate to gpt_params * common : change defaults for escape and n_ctx * common : remove chatml and instruct params ggml-ci * common : passkey use gpt_params
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34 changed files with 899 additions and 1455 deletions
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@ -112,9 +112,12 @@ struct llava_context {
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struct llama_model * model = NULL;
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};
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static void show_additional_info(int /*argc*/, char ** argv) {
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LOG_TEE("\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
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LOG_TEE(" note: a lower temperature value like 0.1 is recommended for better quality.\n");
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static void print_usage(int argc, char ** argv, const gpt_params & params) {
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gpt_params_print_usage(argc, argv, params);
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LOG_TEE("\n example usage:\n");
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LOG_TEE("\n %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
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LOG_TEE("\n note: a lower temperature value like 0.1 is recommended for better quality.\n");
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}
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static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params, const std::string & fname) {
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@ -278,7 +281,7 @@ int main(int argc, char ** argv) {
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gpt_params params;
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if (!gpt_params_parse(argc, argv, params)) {
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show_additional_info(argc, argv);
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print_usage(argc, argv, params);
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return 1;
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}
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@ -290,8 +293,7 @@ int main(int argc, char ** argv) {
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#endif // LOG_DISABLE_LOGS
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if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
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gpt_params_print_usage(argc, argv, params);
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show_additional_info(argc, argv);
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print_usage(argc, argv, {});
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return 1;
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}
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auto model = llava_init(¶ms);
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