fix freq_base/scale default to model value
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39f4afac20
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f28e4953a8
2 changed files with 3 additions and 3 deletions
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@ -621,7 +621,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
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printf(" -f FNAME, --file FNAME\n");
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printf(" prompt file to start generation.\n");
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printf(" -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict);
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printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
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printf(" -c N, --ctx-size N size of the prompt context (default: %d, 0 = loaded from model)\n", params.n_ctx);
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printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
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printf(" --top-k N top-k sampling (default: %d, 0 = disabled)\n", params.top_k);
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printf(" --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)params.top_p);
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@ -48,8 +48,8 @@ struct gpt_params {
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float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs
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int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.
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int32_t n_beams = 0; // if non-zero then use beam search of given width.
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float rope_freq_base = 10000.0f; // RoPE base frequency
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float rope_freq_scale = 1.0f; // RoPE frequency scaling factor
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float rope_freq_base = 0.0f; // RoPE base frequency
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float rope_freq_scale = 0.0f; // RoPE frequency scaling factor
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// sampling parameters
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int32_t top_k = 40; // <= 0 to use vocab size
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