speculative : initial example
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6 changed files with 425 additions and 115 deletions
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@ -63,6 +63,7 @@ struct gpt_params {
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float cfg_scale = 1.f; // How strong is guidance
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std::string model = "models/7B/ggml-model-f16.gguf"; // model path
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std::string model_draft = ""; // draft model for speculative sampling
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std::string model_alias = "unknown"; // model alias
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std::string prompt = "";
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std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state
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@ -156,6 +157,40 @@ std::string llama_detokenize_bpe(
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llama_context * ctx,
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const std::vector<llama_token> & tokens);
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//
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// Sampling utils
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//
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// this is a common sampling function used across the examples for convenience
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// it can serve as a starting point for implementing your own sampling function
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//
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// required:
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// - ctx: context to use for sampling
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// - params: sampling parameters
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//
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// optional:
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// - ctx_guidance: context to use for classifier-free guidance, ignore if NULL
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// - grammar: grammar to use for sampling, ignore if NULL
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// - last_tokens: needed for repetition penalty, ignore if empty
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// - idx: sample from llama_get_logits(ctx) + idx * n_vocab
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//
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// returns:
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// - token: sampled token
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// - candidates: vector of candidate tokens
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//
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llama_token llama_sample_token(
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struct llama_context * ctx,
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struct llama_context * ctx_guidance,
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struct llama_grammar * grammar,
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const struct gpt_params & params,
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const std::vector<llama_token> & last_tokens,
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std::vector<llama_token_data> & candidates,
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int idx = 0);
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//
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// YAML utils
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//
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bool create_directory_with_parents(const std::string & path);
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void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector<float> & data);
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void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector<int> & data);
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