Function train_print_usage

examples/finetune/finetune.cpp:1282–1310

}

static void train_print_usage(int argc, char ** argv, const struct train_params * params) {
    fprintf(stderr, "usage: %s [options]\n", argv[0]);
    fprintf(stderr, "\n");
    fprintf(stderr, "options:\n");
    fprintf(stderr, "  -h, --help                 show this help message and exit\n");

    fprintf(stderr, "  --model-base FNAME         model path from which to load base model (default '%s')\n", params->fn_model_base);
    fprintf(stderr, "  --lora-out FNAME           path to save llama lora (default '%s')\n", params->fn_lora_out);
    fprintf(stderr, "  --only-write-lora          only save llama lora, don't do any training.  use this if you only want to convert a checkpoint to a lora adapter.\n");
    fprintf(stderr, "  --norm-rms-eps F           RMS-Norm epsilon value (default %f)\n", params->f_norm_rms_eps);
    fprintf(stderr, "  --rope-freq-base F         Frequency base for ROPE (default %f)\n", params->rope_freq_base);
    fprintf(stderr, "  --rope-freq-scale F        Frequency scale for ROPE (default %f)\n", params->rope_freq_scale);
    fprintf(stderr, "  --lora-alpha N             LORA alpha : resulting LORA scaling is alpha/r. (default %d)\n", params->lora_alpha);
    fprintf(stderr, "  --lora-r N                 LORA r: default rank. Also specifies resulting scaling together with lora-alpha. (default %d)\n", params->lora_r);
    fprintf(stderr, "  --rank-att-norm N          LORA rank for attention norm tensor, overrides default rank. Norm tensors should generally have rank 1.\n");
    fprintf(stderr, "  --rank-ffn-norm N          LORA rank for feed-forward norm tensor, overrides default rank. Norm tensors should generally have rank 1.\n");
    fprintf(stderr, "  --rank-out-norm N          LORA rank for output norm tensor, overrides default rank. Norm tensors should generally have rank 1.\n");
    fprintf(stderr, "  --rank-tok-embd N          LORA rank for token embeddings tensor, overrides default rank.\n");
    fprintf(stderr, "  --rank-out N               LORA rank for output tensor, overrides default rank.\n");
    fprintf(stderr, "  --rank-wq N                LORA rank for wq tensor, overrides default rank.\n");
    fprintf(stderr, "  --rank-wk N                LORA rank for wk tensor, overrides default rank.\n");
    fprintf(stderr, "  --rank-wv N                LORA rank for wv tensor, overrides default rank.\n");
    fprintf(stderr, "  --rank-wo N                LORA rank for wo tensor, overrides default rank.\n");
    fprintf(stderr, "  --rank-w1 N                LORA rank for w1 tensor, overrides default rank.\n");
    fprintf(stderr, "  --rank-w2 N                LORA rank for w2 tensor, overrides default rank.\n");
    fprintf(stderr, "  --rank-w3 N                LORA rank for w3 tensor, overrides default rank.\n");

    print_common_train_usage(argc, argv, &params->common);
}

static bool train_params_parse(int argc, char ** argv, struct train_params * params) {
    bool invalid_param = false;