Expand description
See llama-cpp-4 for a documented and safe API.
Modules§
- Manual wrapper for values in llama.cpp/common/common.h
Structs§
- If Bindgen could only determine the size and alignment of a type, it is represented like this.
Constants§
Statics§
Functions§
- Apply chat template. Inspired by hf apply_chat_template() on python. Both “model” and “custom_template” are optional, but at least one is required. “custom_template” has higher precedence than “model” NOTE: This function does not use a jinja parser. It only support a pre-defined list of template. See more: https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template @param tmpl A Jinja template to use for this chat. If this is nullptr, the model’s default chat template will be used instead. @param chat Pointer to a list of multiple llama_chat_message @param n_msg Number of llama_chat_message in this chat @param add_ass Whether to end the prompt with the token(s) that indicate the start of an assistant message. @param buf A buffer to hold the output formatted prompt. The recommended alloc size is 2 * (total number of characters of all messages) @param length The size of the allocated buffer @return The total number of bytes of the formatted prompt. If is it larger than the size of buffer, you may need to re-alloc it and then re-apply the template.
- @details Convert the provided tokens into text (inverse of llama_tokenize()). @param text The char pointer must be large enough to hold the resulting text. @return Returns the number of chars/bytes on success, no more than text_len_max. @return Returns a negative number on failure - the number of chars/bytes that would have been returned. @param remove_special Allow to remove BOS and EOS tokens if model is configured to do so. @param unparse_special If true, special tokens are rendered in the output.
- @details DRY sampler, designed by p-e-w, as described in: https://github.com/oobabooga/text-generation-webui/pull/5677, porting Koboldcpp implementation authored by pi6am: https://github.com/LostRuins/koboldcpp/pull/982
- @details Minimum P sampling as described in https://github.com/ggerganov/llama.cpp/pull/3841
- @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words. @param candidates A vector of
llama_token_datacontaining the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text. @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text. @param eta The learning rate used to updatemubased on the error between the target and observed surprisal of the sampled word. A larger learning rate will causemuto be updated more quickly, while a smaller learning rate will result in slower updates. @param m The number of tokens considered in the estimation ofs_hat. This is an arbitrary value that is used to calculates_hat, which in turn helps to calculate the value ofk. In the paper, they usem = 100, but you can experiment with different values to see how it affects the performance of the algorithm. @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (2 * tau) and is updated in the algorithm based on the error between the target and observed surprisal. - @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words. @param candidates A vector of
llama_token_datacontaining the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text. @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text. @param eta The learning rate used to updatemubased on the error between the target and observed surprisal of the sampled word. A larger learning rate will causemuto be updated more quickly, while a smaller learning rate will result in slower updates. @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (2 * tau) and is updated in the algorithm based on the error between the target and observed surprisal. - NOTE: Avoid using on the full vocabulary as searching for repeated tokens can become slow. For example, apply top-k or top-p sampling first.
- #details Updates the logits l_i` = l_i/t. When t <= 0.0f, the maximum logit is kept at it’s original value, the rest are set to -inf
- @details Dynamic temperature implementation (a.k.a. entropy) described in the paper https://arxiv.org/abs/2309.02772.
- @details Top-K sampling described in academic paper “The Curious Case of Neural Text Degeneration” https://arxiv.org/abs/1904.09751
- @details Nucleus sampling described in academic paper “The Curious Case of Neural Text Degeneration” https://arxiv.org/abs/1904.09751
- @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
- @details XTC sampler as described in https://github.com/oobabooga/text-generation-webui/pull/6335
- @details Build a split GGUF final path for this chunk. llama_split_path(split_path, sizeof(split_path), “/models/ggml-model-q4_0”, 2, 4) => split_path = “/models/ggml-model-q4_0-00002-of-00004.gguf”
- @details Extract the path prefix from the split_path if and only if the split_no and split_count match. llama_split_prefix(split_prefix, 64, “/models/ggml-model-q4_0-00002-of-00004.gguf”, 2, 4) => split_prefix = “/models/ggml-model-q4_0”
- @details Convert the provided text into tokens. @param tokens The tokens pointer must be large enough to hold the resulting tokens. @return Returns the number of tokens on success, no more than n_tokens_max @return Returns a negative number on failure - the number of tokens that would have been returned @param add_special Allow to add BOS and EOS tokens if model is configured to do so. @param parse_special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated as plaintext. Does not insert a leading space.