#ifndef LLAMA_H

#define LLAMA_H

#include "ggml.h"

#ifdef GGML_USE_CUBLAS

#include "ggml-cuda.h"

#define LLAMA_MAX_DEVICES GGML_CUDA_MAX_DEVICES

#else

#define LLAMA_MAX_DEVICES 1

#endif // GGML_USE_CUBLAS

#include <stddef.h>

#include <stdint.h>

#include <stdio.h>

#include <stdbool.h>

#ifdef LLAMA_SHARED

# if defined(_WIN32) && !defined(__MINGW32__)

# ifdef LLAMA_BUILD

# define LLAMA_API __declspec(dllexport)

# else

# define LLAMA_API __declspec(dllimport)

# endif

# else

# define LLAMA_API __attribute__ ((visibility ("default")))

# endif

#else

# define LLAMA_API

#endif

#ifdef __GNUC__

# define DEPRECATED(func, hint) func __attribute__((deprecated(hint)))

#elif defined(_MSC_VER)

# define DEPRECATED(func, hint) __declspec(deprecated(hint)) func

#else

# define DEPRECATED(func, hint) func

#endif

#define LLAMA_DEFAULT_SEED 0xFFFFFFFF

#define LLAMA_MAX_RNG_STATE (64*1024)

#define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'

#define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'

#define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN

#define LLAMA_SESSION_VERSION 3

#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL)

// Defined when llama.cpp is compiled with support for offloading model layers to GPU.

#define LLAMA_SUPPORTS_GPU_OFFLOAD

#endif

#ifdef __cplusplus

extern "C" {

#endif

//

// C interface

//

// TODO: show sample usage

//

struct llama_model;

struct llama_context;

typedef int32_t llama_pos;

typedef int32_t llama_token;

typedef int32_t llama_seq_id;

enum llama_vocab_type {

LLAMA_VOCAB_TYPE_SPM = 0, // SentencePiece

LLAMA_VOCAB_TYPE_BPE = 1, // Byte Pair Encoding

};

enum llama_token_type {

};

// model file types

enum llama_ftype {

};

enum llama_rope_scaling_type {

};

typedef struct llama_token_data {

} llama_token_data;

typedef struct llama_token_data_array {

} llama_token_data_array;

typedef bool (*llama_progress_callback)(float progress, void *ctx);

// Input data for llama_decode

// A llama_batch object can contain input about one or many sequences

// The provided arrays (i.e. token, embd, pos, etc.) must have size of n_tokens

//

// - token : the token ids of the input (used when embd is NULL)

// - embd : token embeddings (i.e. float vector of size n_embd) (used when token is NULL)

// - pos : the positions of the respective token in the sequence

// - seq_id : the sequence to which the respective token belongs

// - logits : if zero, the logits for the respective token will not be output

//

typedef struct llama_batch {

} llama_batch;

enum llama_model_kv_override_type {

};

struct llama_model_kv_override {

};

struct llama_model_params {

};

struct llama_context_params {

};

// model quantization parameters

typedef struct llama_model_quantize_params {

} llama_model_quantize_params;

// grammar types

struct llama_grammar;

// grammar element type

enum llama_gretype {

};

typedef struct llama_grammar_element {

enum llama_gretype type;

uint32_t value; // Unicode code point or rule ID

} llama_grammar_element;

// performance timing information

struct llama_timings {

};

// Helpers for getting default parameters

LLAMA_API struct llama_model_params llama_model_default_params(void);

LLAMA_API struct llama_context_params llama_context_default_params(void);

LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params(void);

// Initialize the llama + ggml backend

// If numa is true, use NUMA optimizations

// Call once at the start of the program

LLAMA_API void llama_backend_init(bool numa);

// Call once at the end of the program - currently only used for MPI

LLAMA_API void llama_backend_free(void);

LLAMA_API struct llama_model * llama_load_model_from_file(

const char * path_model,

struct llama_model_params params);

LLAMA_API void llama_free_model(struct llama_model * model);

LLAMA_API struct llama_context * llama_new_context_with_model(

struct llama_model * model,

struct llama_context_params params);

// Frees all allocated memory

LLAMA_API void llama_free(struct llama_context * ctx);

LLAMA_API int64_t llama_time_us(void);

LLAMA_API int32_t llama_max_devices(void);

LLAMA_API bool llama_mmap_supported (void);

LLAMA_API bool llama_mlock_supported(void);

LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx);

LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx);

LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx);

LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model);

LLAMA_API int32_t llama_n_vocab (const struct llama_model * model);

LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);

LLAMA_API int32_t llama_n_embd (const struct llama_model * model);

// Get the model's RoPE frequency scaling factor

LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model);

// Functions to access the model's GGUF metadata scalar values

// - The functions return the length of the string on success, or -1 on failure

// - The output string is always null-terminated and cleared on failure

// - GGUF array values are not supported by these functions

// Get metadata value as a string by key name

LLAMA_API int32_t llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size);

// Get the number of metadata key/value pairs

LLAMA_API int32_t llama_model_meta_count(const struct llama_model * model);

// Get metadata key name by index

LLAMA_API int32_t llama_model_meta_key_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size);

// Get metadata value as a string by index

LLAMA_API int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size);

// Get a string describing the model type

LLAMA_API int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size);

// Returns the total size of all the tensors in the model in bytes

LLAMA_API uint64_t llama_model_size(const struct llama_model * model);

// Returns the total number of parameters in the model

LLAMA_API uint64_t llama_model_n_params(const struct llama_model * model);

// Get a llama model tensor

LLAMA_API struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name);

// Returns 0 on success

LLAMA_API uint32_t llama_model_quantize(

const char * fname_inp,

const char * fname_out,

const llama_model_quantize_params * params);

// Apply a LoRA adapter to a loaded model

// path_base_model is the path to a higher quality model to use as a base for

// the layers modified by the adapter. Can be NULL to use the current loaded model.

// The model needs to be reloaded before applying a new adapter, otherwise the adapter

// will be applied on top of the previous one

// Returns 0 on success

LLAMA_API DEPRECATED(int32_t llama_apply_lora_from_file(

struct llama_context * ctx,

const char * path_lora,

float scale,

const char * path_base_model,

int32_t n_threads),

"use llama_model_apply_lora_from_file instead");

LLAMA_API int32_t llama_model_apply_lora_from_file(

const struct llama_model * model,

const char * path_lora,

float scale,

const char * path_base_model,

int32_t n_threads);

//

// KV cache

//

// Information associated with an individual cell in the KV cache view.

struct llama_kv_cache_view_cell {

};

// An updateable view of the KV cache.

struct llama_kv_cache_view {

};

// Create an empty KV cache view. (use only for debugging purposes)

LLAMA_API struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_max_seq);

// Free a KV cache view. (use only for debugging purposes)

LLAMA_API void llama_kv_cache_view_free(struct llama_kv_cache_view * view);

// Update the KV cache view structure with the current state of the KV cache. (use only for debugging purposes)

LLAMA_API void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_kv_cache_view * view);

// Returns the number of tokens in the KV cache (slow, use only for debug)

// If a KV cell has multiple sequences assigned to it, it will be counted multiple times

LLAMA_API int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx);

// Returns the number of used KV cells (i.e. have at least one sequence assigned to them)

LLAMA_API int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx);

// Clear the KV cache

LLAMA_API void llama_kv_cache_clear(

struct llama_context * ctx);

// Removes all tokens that belong to the specified sequence and have positions in [p0, p1)

// seq_id < 0 : match any sequence

// p0 < 0 : [0, p1]

// p1 < 0 : [p0, inf)

LLAMA_API void llama_kv_cache_seq_rm(

struct llama_context * ctx,

llama_seq_id seq_id,

llama_pos p0,

llama_pos p1);

// Copy all tokens that belong to the specified sequence to another sequence

// Note that this does not allocate extra KV cache memory - it simply assigns the tokens to the new sequence

// p0 < 0 : [0, p1]

// p1 < 0 : [p0, inf)

LLAMA_API void llama_kv_cache_seq_cp(

struct llama_context * ctx,

llama_seq_id seq_id_src,

llama_seq_id seq_id_dst,

llama_pos p0,

llama_pos p1);

// Removes all tokens that do not belong to the specified sequence

LLAMA_API void llama_kv_cache_seq_keep(

struct llama_context * ctx,

llama_seq_id seq_id);

// Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1)

// If the KV cache is RoPEd, the KV data is updated accordingly

// p0 < 0 : [0, p1]

// p1 < 0 : [p0, inf)

LLAMA_API void llama_kv_cache_seq_shift(

struct llama_context * ctx,

llama_seq_id seq_id,

llama_pos p0,

llama_pos p1,

llama_pos delta);

// Integer division of the positions by factor of `d > 1`

// If the KV cache is RoPEd, the KV data is updated accordingly

// p0 < 0 : [0, p1]

// p1 < 0 : [p0, inf)

LLAMA_API void llama_kv_cache_seq_div(

struct llama_context * ctx,

llama_seq_id seq_id,

llama_pos p0,

llama_pos p1,

int d);

//

// State / sessions

//

// Returns the maximum size in bytes of the state (rng, logits, embedding

// and kv_cache) - will often be smaller after compacting tokens

LLAMA_API size_t llama_get_state_size(const struct llama_context * ctx);

// Copies the state to the specified destination address.

// Destination needs to have allocated enough memory.

// Returns the number of bytes copied

LLAMA_API size_t llama_copy_state_data(

struct llama_context * ctx,

uint8_t * dst);

// Set the state reading from the specified address

// Returns the number of bytes read

LLAMA_API size_t llama_set_state_data(

struct llama_context * ctx,

uint8_t * src);

// Save/load session file

LLAMA_API bool llama_load_session_file(

struct llama_context * ctx,

const char * path_session,

llama_token * tokens_out,

size_t n_token_capacity,

size_t * n_token_count_out);

LLAMA_API bool llama_save_session_file(

struct llama_context * ctx,

const char * path_session,

const llama_token * tokens,

size_t n_token_count);

//

// Decoding

//

// Run the llama inference to obtain the logits and probabilities for the next token(s).

// tokens + n_tokens is the provided batch of new tokens to process

// n_past is the number of tokens to use from previous eval calls

// Returns 0 on success

// DEPRECATED: use llama_decode() instead

LLAMA_API DEPRECATED(int llama_eval(

struct llama_context * ctx,

llama_token * tokens,

int32_t n_tokens,

int32_t n_past),

"use llama_decode() instead");

// Same as llama_eval, but use float matrix input directly.

// DEPRECATED: use llama_decode() instead

LLAMA_API DEPRECATED(int llama_eval_embd(

struct llama_context * ctx,

float * embd,

int32_t n_tokens,

int32_t n_past),

"use llama_decode() instead");

// Return batch for single sequence of tokens starting at pos_0

//

// NOTE: this is a helper function to facilitate transition to the new batch API - avoid using it

//

LLAMA_API struct llama_batch llama_batch_get_one(

llama_token * tokens,

int32_t n_tokens,

llama_pos pos_0,

llama_seq_id seq_id);

// Allocates a batch of tokens on the heap that can hold a maximum of n_tokens

// Each token can be assigned up to n_seq_max sequence ids

// The batch has to be freed with llama_batch_free()

// If embd != 0, llama_batch.embd will be allocated with size of n_tokens * embd * sizeof(float)

// Otherwise, llama_batch.token will be allocated to store n_tokens llama_token

// The rest of the llama_batch members are allocated with size n_tokens

// All members are left uninitialized

LLAMA_API struct llama_batch llama_batch_init(

int32_t n_tokens,

int32_t embd,

int32_t n_seq_max);

// Frees a batch of tokens allocated with llama_batch_init()

LLAMA_API void llama_batch_free(struct llama_batch batch);

// Positive return values does not mean a fatal error, but rather a warning.

// 0 - success

// 1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)

// < 0 - error

LLAMA_API int32_t llama_decode(

struct llama_context * ctx,

struct llama_batch batch);

// Set the number of threads used for decoding

// n_threads is the number of threads used for generation (single token)

// n_threads_batch is the number of threads used for prompt and batch processing (multiple tokens)

LLAMA_API void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch);

// Token logits obtained from the last call to llama_eval()

// The logits for the last token are stored in the last row

// Logits for which llama_batch.logits[i] == 0 are undefined

// Rows: n_tokens provided with llama_batch

// Cols: n_vocab

LLAMA_API float * llama_get_logits(struct llama_context * ctx);

// Logits for the ith token. Equivalent to:

// llama_get_logits(ctx) + i*n_vocab

LLAMA_API float * llama_get_logits_ith(struct llama_context * ctx, int32_t i);

// Get the embeddings for the input

// shape: [n_embd] (1-dimensional)

LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);

//

// Vocab

//

LLAMA_API const char * llama_token_get_text(const struct llama_model * model, llama_token token);

LLAMA_API float llama_token_get_score(const struct llama_model * model, llama_token token);

LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token);

// Special tokens

LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence

LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence

LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line

// Returns -1 if unknown, 1 for true or 0 for false.

LLAMA_API int32_t llama_add_bos_token(const struct llama_model * model);

// Returns -1 if unknown, 1 for true or 0 for false.

LLAMA_API int32_t llama_add_eos_token(const struct llama_model * model);

// codellama infill tokens

LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix

LLAMA_API llama_token llama_token_middle(const struct llama_model * model); // Beginning of infill middle

LLAMA_API llama_token llama_token_suffix(const struct llama_model * model); // Beginning of infill suffix

LLAMA_API llama_token llama_token_eot (const struct llama_model * model); // End of infill middle

//

// Tokenization

//

/// @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_max_tokens

/// @return Returns a negative number on failure - the number of tokens that would have been returned

/// @param special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated as plaintext.

/// Does not insert a leading space.

LLAMA_API int32_t llama_tokenize(

const struct llama_model * model,

const char * text,

int32_t text_len,

llama_token * tokens,

int32_t n_max_tokens,

bool add_bos,

bool special);

// Token Id -> Piece.

// Uses the vocabulary in the provided context.

// Does not write null terminator to the buffer.

// User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.

LLAMA_API int32_t llama_token_to_piece(

const struct llama_model * model,

llama_token token,

char * buf,

int32_t length);

//

// Grammar

//

LLAMA_API struct llama_grammar * llama_grammar_init(

const llama_grammar_element ** rules,

size_t n_rules,

size_t start_rule_index);

LLAMA_API void llama_grammar_free(struct llama_grammar * grammar);

LLAMA_API struct llama_grammar * llama_grammar_copy(const struct llama_grammar * grammar);

//

// Sampling functions

//

// Sets the current rng seed.

LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed);

/// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.

/// @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.

LLAMA_API void llama_sample_repetition_penalties(

struct llama_context * ctx,

llama_token_data_array * candidates,

const llama_token * last_tokens,

size_t penalty_last_n,

float penalty_repeat,

float penalty_freq,

float penalty_present);

/// @details Apply classifier-free guidance to the logits as described in academic paper "Stay on topic with Classifier-Free Guidance" https://arxiv.org/abs/2306.17806

/// @param candidates A vector of `llama_token_data` containing the candidate tokens, the logits must be directly extracted from the original generation context without being sorted.

/// @params guidance_ctx A separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context.

/// @params scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance.

LLAMA_API void llama_sample_classifier_free_guidance(

struct llama_context * ctx,

llama_token_data_array * candidates,

struct llama_context * guidance_ctx,

float scale);

/// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.

LLAMA_API void llama_sample_softmax(

struct llama_context * ctx,

llama_token_data_array * candidates);

/// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751

LLAMA_API void llama_sample_top_k(

struct llama_context * ctx,

llama_token_data_array * candidates,

int32_t k,

size_t min_keep);

/// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751

LLAMA_API void llama_sample_top_p(

struct llama_context * ctx,

llama_token_data_array * candidates,

float p,

size_t min_keep);

/// @details Minimum P sampling as described in https://github.com/ggerganov/llama.cpp/pull/3841

LLAMA_API void llama_sample_min_p(

struct llama_context * ctx,

llama_token_data_array * candidates,

float p,

size_t min_keep);

/// @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.

LLAMA_API void llama_sample_tail_free(

struct llama_context * ctx,

llama_token_data_array * candidates,

float z,

size_t min_keep);

/// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.

LLAMA_API void llama_sample_typical(

struct llama_context * ctx,

llama_token_data_array * candidates,

float p,

size_t min_keep);

LLAMA_API void llama_sample_temp(

struct llama_context * ctx,

llama_token_data_array * candidates,

float temp);

LLAMA_API DEPRECATED(void llama_sample_temperature(

struct llama_context * ctx,

llama_token_data_array * candidates,

float temp),

"use llama_sample_temp instead");

/// @details Apply constraints from grammar

LLAMA_API void llama_sample_grammar(

struct llama_context * ctx,

llama_token_data_array * candidates,

const struct llama_grammar * grammar);

/// @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_data` containing 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 update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.

/// @param m The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 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.

LLAMA_API llama_token llama_sample_token_mirostat(

struct llama_context * ctx,

llama_token_data_array * candidates,

float tau,

float eta,

int32_t m,

float * mu);

/// @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_data` containing 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 update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to 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.

LLAMA_API llama_token llama_sample_token_mirostat_v2(

struct llama_context * ctx,

llama_token_data_array * candidates,

float tau,

float eta,

float * mu);

/// @details Selects the token with the highest probability.

/// Does not compute the token probabilities. Use llama_sample_softmax() instead.

LLAMA_API llama_token llama_sample_token_greedy(

struct llama_context * ctx,

llama_token_data_array * candidates);

/// @details Randomly selects a token from the candidates based on their probabilities.

LLAMA_API llama_token llama_sample_token(

struct llama_context * ctx,

llama_token_data_array * candidates);

/// @details Accepts the sampled token into the grammar

LLAMA_API void llama_grammar_accept_token(

struct llama_context * ctx,

struct llama_grammar * grammar,

llama_token token);

//

// Beam search

//

struct llama_beam_view {

};

// Passed to beam_search_callback function.

// Whenever 0 < common_prefix_length, this number of tokens should be copied from any of the beams

// (e.g. beams[0]) as they will be removed (shifted) from all beams in all subsequent callbacks.

// These pointers are valid only during the synchronous callback, so should not be saved.

struct llama_beams_state {

};

// Type of pointer to the beam_search_callback function.

// void* callback_data is any custom data passed to llama_beam_search, that is subsequently

// passed back to beam_search_callback. This avoids having to use global variables in the callback.

typedef void (*llama_beam_search_callback_fn_t)(void * callback_data, struct llama_beams_state);

/// @details Deterministically returns entire sentence constructed by a beam search.

/// @param ctx Pointer to the llama_context.

/// @param callback Invoked for each iteration of the beam_search loop, passing in beams_state.

/// @param callback_data A pointer that is simply passed back to callback.

/// @param n_beams Number of beams to use.

/// @param n_past Number of tokens already evaluated.

/// @param n_predict Maximum number of tokens to predict. EOS may occur earlier.

LLAMA_API void llama_beam_search(

struct llama_context * ctx,

llama_beam_search_callback_fn_t callback,

void * callback_data,

size_t n_beams,

int32_t n_past,

int32_t n_predict);

// Performance information

LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);

LLAMA_API void llama_print_timings(struct llama_context * ctx);

LLAMA_API void llama_reset_timings(struct llama_context * ctx);

// Print system information

LLAMA_API const char * llama_print_system_info(void);

// Set callback for all future logging events.

// If this is not called, or NULL is supplied, everything is output on stderr.

LLAMA_API void llama_log_set(ggml_log_callback log_callback, void * user_data);

LLAMA_API void llama_dump_timing_info_yaml(FILE * stream, const struct llama_context * ctx);

#ifdef __cplusplus

}

#endif

// Internal API to be implemented by llama.cpp and used by tests/benchmarks only

#ifdef LLAMA_API_INTERNAL

#include <vector>

#include <string>

struct ggml_tensor;

const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(

struct llama_context * ctx

);

#endif // LLAMA_API_INTERNAL

#endif // LLAMA_H