diff options
| -rw-r--r-- | examples/common.h | 26 | ||||
| -rw-r--r-- | examples/main/main.cpp | 24 |
2 files changed, 25 insertions, 25 deletions
diff --git a/examples/common.h b/examples/common.h index 14e6b1b..fce1d42 100644 --- a/examples/common.h +++ b/examples/common.h @@ -17,7 +17,7 @@ struct gpt_params { int32_t seed = -1; // RNG seed int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency()); - int32_t n_predict = 128; // new tokens to predict + int32_t n_predict = -1; // new tokens to predict int32_t n_parts = -1; // amount of model parts (-1 = determine from model dimensions) int32_t n_ctx = 512; // context size int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) @@ -25,18 +25,18 @@ struct gpt_params { // sampling parameters std::unordered_map<llama_token, float> logit_bias; // logit bias for specific tokens - int32_t top_k = 0; // <= 0 to use vocab size - float top_p = 1.0f; // 1.0 = disabled - float tfs_z = 1.0f; // 1.0 = disabled - float typical_p = 1.0f; // 1.0 = disabled - float temp = 1.0f; // 1.0 = disabled - float repeat_penalty = 1.0f; // 1.0 = disabled - int32_t repeat_last_n = -1; // last n tokens to penalize (0 = disable penalty, -1 = context size) - float frequency_penalty = 0.0f; // 0.0 = disabled - float presence_penalty = 0.0f; // 0.0 = disabled - int mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 - float mirostat_tau = 5.0f; // target entropy - float mirostat_eta = 0.1f; // learning rate + int32_t top_k = 40; // <= 0 to use vocab size + float top_p = 0.95f; // 1.0 = disabled + float tfs_z = 1.00f; // 1.0 = disabled + float typical_p = 1.00f; // 1.0 = disabled + float temp = 0.80f; // 1.0 = disabled + float repeat_penalty = 1.10f; // 1.0 = disabled + int32_t repeat_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size) + float frequency_penalty = 0.00f; // 0.0 = disabled + float presence_penalty = 0.00f; // 0.0 = disabled + int mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 + float mirostat_tau = 5.00f; // target entropy + float mirostat_eta = 0.10f; // learning rate std::string model = "models/lamma-7B/ggml-model.bin"; // model path std::string prompt = ""; diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 674920b..990d0fa 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -387,19 +387,19 @@ int main(int argc, char ** argv) { if ((int) embd_inp.size() <= n_consumed && !is_interacting) { // out of user input, sample next token - const float temp = params.temp; - const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k; - const float top_p = params.top_p; - const float tfs_z = params.tfs_z; - const float typical_p = params.typical_p; - const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n; - const float repeat_penalty = params.repeat_penalty; - const float alpha_presence = params.presence_penalty; + const float temp = params.temp; + const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k; + const float top_p = params.top_p; + const float tfs_z = params.tfs_z; + const float typical_p = params.typical_p; + const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n; + const float repeat_penalty = params.repeat_penalty; + const float alpha_presence = params.presence_penalty; const float alpha_frequency = params.frequency_penalty; - const int mirostat = params.mirostat; - const float mirostat_tau = params.mirostat_tau; - const float mirostat_eta = params.mirostat_eta; - const bool penalize_nl = params.penalize_nl; + const int mirostat = params.mirostat; + const float mirostat_tau = params.mirostat_tau; + const float mirostat_eta = params.mirostat_eta; + const bool penalize_nl = params.penalize_nl; // optionally save the session on first sample (for faster prompt loading next time) if (!path_session.empty() && need_to_save_session) { |