diff options
Diffstat (limited to 'common/common.h')
| -rw-r--r-- | common/common.h | 56 |
1 files changed, 4 insertions, 52 deletions
diff --git a/common/common.h b/common/common.h index c8021527..fa115536 100644 --- a/common/common.h +++ b/common/common.h @@ -4,6 +4,8 @@ #include "llama.h" +#include "sampling.h" + #define LOG_NO_FILE_LINE_FUNCTION #include "log.h" @@ -49,31 +51,12 @@ struct gpt_params { int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default) int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs - int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens. int32_t n_beams = 0; // if non-zero then use beam search of given width. float rope_freq_base = 0.0f; // RoPE base frequency float rope_freq_scale = 0.0f; // RoPE frequency scaling factor - // sampling parameters - 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 - int32_t 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::unordered_map<llama_token, float> logit_bias; // logit bias for specific tokens - - // Classifier-Free Guidance - // https://arxiv.org/abs/2306.17806 - std::string cfg_negative_prompt; // string to help guidance - float cfg_scale = 1.f; // How strong is guidance + // // sampling parameters + struct llama_sampling_params sampling_params; std::string model = "models/7B/ggml-model-f16.gguf"; // model path std::string model_draft = ""; // draft model for speculative decoding @@ -115,7 +98,6 @@ struct gpt_params { bool input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix bool ignore_eos = false; // ignore generated EOS tokens bool instruct = false; // instruction mode (used for Alpaca models) - bool penalize_nl = true; // consider newlines as a repeatable token bool logits_all = false; // return logits for all tokens in the batch bool use_mmap = true; // use mmap for faster loads bool use_mlock = false; // use mlock to keep model in memory @@ -181,36 +163,6 @@ std::string llama_detokenize_bpe( const std::vector<llama_token> & tokens); // -// Sampling utils -// - -// this is a common sampling function used across the examples for convenience -// it can serve as a starting point for implementing your own sampling function -// -// required: -// - ctx: context to use for sampling -// - params: sampling parameters -// -// optional: -// - ctx_guidance: context to use for classifier-free guidance, ignore if NULL -// - grammar: grammar to use for sampling, ignore if NULL -// - last_tokens: needed for repetition penalty, ignore if empty -// - idx: sample from llama_get_logits_ith(ctx, idx) -// -// returns: -// - token: sampled token -// - candidates: vector of candidate tokens -// -llama_token llama_sample_token( - struct llama_context * ctx, - struct llama_context * ctx_guidance, - struct llama_grammar * grammar, - const struct gpt_params & params, - const std::vector<llama_token> & last_tokens, - std::vector<llama_token_data> & candidates, - int idx = 0); - -// // YAML utils // |