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common : change default parameters to pre-#1126 (#1223)
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@ -17,7 +17,7 @@
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struct gpt_params {
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struct gpt_params {
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int32_t seed = -1; // RNG seed
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int32_t seed = -1; // RNG seed
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int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
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int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
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int32_t n_predict = 128; // new tokens to predict
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int32_t n_predict = -1; // new tokens to predict
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int32_t n_parts = -1; // amount of model parts (-1 = determine from model dimensions)
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int32_t n_parts = -1; // amount of model parts (-1 = determine from model dimensions)
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int32_t n_ctx = 512; // context size
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int32_t n_ctx = 512; // context size
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int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
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int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
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@ -25,18 +25,18 @@ struct gpt_params {
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// sampling parameters
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// sampling parameters
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std::unordered_map<llama_token, float> logit_bias; // logit bias for specific tokens
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std::unordered_map<llama_token, float> logit_bias; // logit bias for specific tokens
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int32_t top_k = 0; // <= 0 to use vocab size
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int32_t top_k = 40; // <= 0 to use vocab size
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float top_p = 1.0f; // 1.0 = disabled
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float top_p = 0.95f; // 1.0 = disabled
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float tfs_z = 1.0f; // 1.0 = disabled
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float tfs_z = 1.00f; // 1.0 = disabled
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float typical_p = 1.0f; // 1.0 = disabled
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float typical_p = 1.00f; // 1.0 = disabled
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float temp = 1.0f; // 1.0 = disabled
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float temp = 0.80f; // 1.0 = disabled
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float repeat_penalty = 1.0f; // 1.0 = disabled
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float repeat_penalty = 1.10f; // 1.0 = disabled
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int32_t repeat_last_n = -1; // last n tokens to penalize (0 = disable penalty, -1 = context size)
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int32_t repeat_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
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float frequency_penalty = 0.0f; // 0.0 = disabled
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float frequency_penalty = 0.00f; // 0.0 = disabled
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float presence_penalty = 0.0f; // 0.0 = disabled
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float presence_penalty = 0.00f; // 0.0 = disabled
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int mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
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int mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
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float mirostat_tau = 5.0f; // target entropy
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float mirostat_tau = 5.00f; // target entropy
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float mirostat_eta = 0.1f; // learning rate
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float mirostat_eta = 0.10f; // learning rate
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std::string model = "models/lamma-7B/ggml-model.bin"; // model path
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std::string model = "models/lamma-7B/ggml-model.bin"; // model path
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std::string prompt = "";
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std::string prompt = "";
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@ -387,19 +387,19 @@ int main(int argc, char ** argv) {
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if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
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if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
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// out of user input, sample next token
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// out of user input, sample next token
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const float temp = params.temp;
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const float temp = params.temp;
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const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
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const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
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const float top_p = params.top_p;
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const float top_p = params.top_p;
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const float tfs_z = params.tfs_z;
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const float tfs_z = params.tfs_z;
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const float typical_p = params.typical_p;
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const float typical_p = params.typical_p;
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const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
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const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
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const float repeat_penalty = params.repeat_penalty;
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const float repeat_penalty = params.repeat_penalty;
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const float alpha_presence = params.presence_penalty;
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const float alpha_presence = params.presence_penalty;
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const float alpha_frequency = params.frequency_penalty;
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const float alpha_frequency = params.frequency_penalty;
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const int mirostat = params.mirostat;
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const int mirostat = params.mirostat;
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const float mirostat_tau = params.mirostat_tau;
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const float mirostat_tau = params.mirostat_tau;
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const float mirostat_eta = params.mirostat_eta;
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const float mirostat_eta = params.mirostat_eta;
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const bool penalize_nl = params.penalize_nl;
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const bool penalize_nl = params.penalize_nl;
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// optionally save the session on first sample (for faster prompt loading next time)
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// optionally save the session on first sample (for faster prompt loading next time)
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if (!path_session.empty() && need_to_save_session) {
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if (!path_session.empty() && need_to_save_session) {
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