Code style fixes according to review
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ff8adc1196
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2 changed files with 26 additions and 26 deletions
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@ -156,15 +156,15 @@ llama_token llama_sampling_sample(
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const int n_vocab = llama_n_vocab(llama_get_model(ctx_main));
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const float temp = params.temp;
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const int32_t penalty_last_n = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n;
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const float penalty_repeat = params.penalty_repeat;
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const float penalty_freq = params.penalty_freq;
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const float penalty_present = params.penalty_present;
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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_eta = params.mirostat_eta;
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const bool penalize_nl = params.penalize_nl;
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const float temp = params.temp;
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const int32_t penalty_last_n = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n;
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const float penalty_repeat = params.penalty_repeat;
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const float penalty_freq = params.penalty_freq;
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const float penalty_present = params.penalty_present;
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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_eta = params.mirostat_eta;
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const bool penalize_nl = params.penalize_nl;
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auto & prev = ctx_sampling->prev;
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auto & cur = ctx_sampling->cur;
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@ -10,23 +10,23 @@
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// sampling parameters
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typedef struct llama_sampling_params {
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int32_t n_prev = 64; // number of previous tokens to remember
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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 top_k = 40; // <= 0 to use vocab size
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float top_p = 0.95f; // 1.0 = disabled
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float min_p = 0.05f; // 0.0 = disabled
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float tfs_z = 1.00f; // 1.0 = disabled
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float typical_p = 1.00f; // 1.0 = disabled
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float temp = 0.80f; // 1.0 = disabled
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int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
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float penalty_repeat = 1.10f; // 1.0 = disabled
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float penalty_freq = 0.00f; // 0.0 = disabled
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float penalty_present = 0.00f; // 0.0 = disabled
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int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
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float mirostat_tau = 5.00f; // target entropy
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float mirostat_eta = 0.10f; // learning rate
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bool penalize_nl = true; // consider newlines as a repeatable token
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std::string samplers_sequence = "kfypmt"; // top_k, tail_free, typical_p, top_p, min_p, temp
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int32_t n_prev = 64; // number of previous tokens to remember
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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 top_k = 40; // <= 0 to use vocab size
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float top_p = 0.95f; // 1.0 = disabled
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float min_p = 0.05f; // 0.0 = disabled
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float tfs_z = 1.00f; // 1.0 = disabled
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float typical_p = 1.00f; // 1.0 = disabled
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float temp = 0.80f; // 1.0 = disabled
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int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
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float penalty_repeat = 1.10f; // 1.0 = disabled
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float penalty_freq = 0.00f; // 0.0 = disabled
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float penalty_present = 0.00f; // 0.0 = disabled
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int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
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float mirostat_tau = 5.00f; // target entropy
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float mirostat_eta = 0.10f; // learning rate
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bool penalize_nl = true; // consider newlines as a repeatable token
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std::string samplers_sequence = "kfypmt"; // top_k, tail_free, typical_p, top_p, min_p, temp
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std::string grammar; // optional BNF-like grammar to constrain sampling
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