#ifndef LLAMA_H #define LLAMA_H #include "ggml.h" #include "ggml-backend.h" #include #include #include #include #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_FILE_MAGIC_GGLA 0x67676c61u // 'ggla' #define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn' #define LLAMA_FILE_MAGIC_GGSQ 0x67677371u // 'ggsq' #define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN #define LLAMA_SESSION_VERSION 8 #define LLAMA_STATE_SEQ_MAGIC LLAMA_FILE_MAGIC_GGSQ #define LLAMA_STATE_SEQ_VERSION 2 #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_NONE = 0, // For models without vocab LLAMA_VOCAB_TYPE_SPM = 1, // LLaMA tokenizer based on byte-level BPE with byte fallback LLAMA_VOCAB_TYPE_BPE = 2, // GPT-2 tokenizer based on byte-level BPE LLAMA_VOCAB_TYPE_WPM = 3, // BERT tokenizer based on WordPiece LLAMA_VOCAB_TYPE_UGM = 4, // T5 tokenizer based on Unigram LLAMA_VOCAB_TYPE_RWKV = 5, // RWKV tokenizer based on greedy tokenization }; // pre-tokenization types enum llama_vocab_pre_type { LLAMA_VOCAB_PRE_TYPE_DEFAULT = 0, LLAMA_VOCAB_PRE_TYPE_LLAMA3 = 1, LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM = 2, LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER = 3, LLAMA_VOCAB_PRE_TYPE_FALCON = 4, LLAMA_VOCAB_PRE_TYPE_MPT = 5, LLAMA_VOCAB_PRE_TYPE_STARCODER = 6, LLAMA_VOCAB_PRE_TYPE_GPT2 = 7, LLAMA_VOCAB_PRE_TYPE_REFACT = 8, LLAMA_VOCAB_PRE_TYPE_COMMAND_R = 9, LLAMA_VOCAB_PRE_TYPE_STABLELM2 = 10, LLAMA_VOCAB_PRE_TYPE_QWEN2 = 11, LLAMA_VOCAB_PRE_TYPE_OLMO = 12, LLAMA_VOCAB_PRE_TYPE_DBRX = 13, LLAMA_VOCAB_PRE_TYPE_SMAUG = 14, LLAMA_VOCAB_PRE_TYPE_PORO = 15, LLAMA_VOCAB_PRE_TYPE_CHATGLM3 = 16, LLAMA_VOCAB_PRE_TYPE_CHATGLM4 = 17, LLAMA_VOCAB_PRE_TYPE_VIKING = 18, LLAMA_VOCAB_PRE_TYPE_JAIS = 19, LLAMA_VOCAB_PRE_TYPE_TEKKEN = 20, LLAMA_VOCAB_PRE_TYPE_SMOLLM = 21, LLAMA_VOCAB_PRE_TYPE_CODESHELL = 22, LLAMA_VOCAB_PRE_TYPE_BLOOM = 23, LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH = 24, LLAMA_VOCAB_PRE_TYPE_EXAONE = 25, }; enum llama_rope_type { LLAMA_ROPE_TYPE_NONE = -1, LLAMA_ROPE_TYPE_NORM = 0, LLAMA_ROPE_TYPE_NEOX = GGML_ROPE_TYPE_NEOX, }; enum llama_token_type { //TODO: remove, required until per token attributes are available from GGUF file LLAMA_TOKEN_TYPE_UNDEFINED = 0, LLAMA_TOKEN_TYPE_NORMAL = 1, LLAMA_TOKEN_TYPE_UNKNOWN = 2, LLAMA_TOKEN_TYPE_CONTROL = 3, LLAMA_TOKEN_TYPE_USER_DEFINED = 4, LLAMA_TOKEN_TYPE_UNUSED = 5, LLAMA_TOKEN_TYPE_BYTE = 6, }; enum llama_token_attr { LLAMA_TOKEN_ATTR_UNDEFINED = 0, LLAMA_TOKEN_ATTR_UNKNOWN = 1 << 0, LLAMA_TOKEN_ATTR_UNUSED = 1 << 1, LLAMA_TOKEN_ATTR_NORMAL = 1 << 2, LLAMA_TOKEN_ATTR_CONTROL = 1 << 3, // SPECIAL? LLAMA_TOKEN_ATTR_USER_DEFINED = 1 << 4, LLAMA_TOKEN_ATTR_BYTE = 1 << 5, LLAMA_TOKEN_ATTR_NORMALIZED = 1 << 6, LLAMA_TOKEN_ATTR_LSTRIP = 1 << 7, LLAMA_TOKEN_ATTR_RSTRIP = 1 << 8, LLAMA_TOKEN_ATTR_SINGLE_WORD = 1 << 9, }; // model file types enum llama_ftype { LLAMA_FTYPE_ALL_F32 = 0, LLAMA_FTYPE_MOSTLY_F16 = 1, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors // LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16 // LLAMA_FTYPE_MOSTLY_Q4_2 = 5, // support has been removed // LLAMA_FTYPE_MOSTLY_Q4_3 = 6, // support has been removed LLAMA_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q2_K = 10, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q3_K_S = 11, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q3_K_M = 12, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q3_K_L = 13, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q4_K_S = 14, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q4_K_M = 15, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q5_K_S = 16, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q5_K_M = 17, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q6_K = 18, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ2_XXS = 19, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ2_XS = 20, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q2_K_S = 21, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ3_XS = 22, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ3_XXS = 23, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ1_S = 24, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ4_NL = 25, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ3_S = 26, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ3_M = 27, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ2_S = 28, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ2_M = 29, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ4_XS = 30, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ1_M = 31, // except 1d tensors LLAMA_FTYPE_MOSTLY_BF16 = 32, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q4_0_4_4 = 33, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q4_0_4_8 = 34, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q4_0_8_8 = 35, // except 1d tensors LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file }; enum llama_rope_scaling_type { LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED = -1, LLAMA_ROPE_SCALING_TYPE_NONE = 0, LLAMA_ROPE_SCALING_TYPE_LINEAR = 1, LLAMA_ROPE_SCALING_TYPE_YARN = 2, LLAMA_ROPE_SCALING_TYPE_MAX_VALUE = LLAMA_ROPE_SCALING_TYPE_YARN, }; enum llama_pooling_type { LLAMA_POOLING_TYPE_UNSPECIFIED = -1, LLAMA_POOLING_TYPE_NONE = 0, LLAMA_POOLING_TYPE_MEAN = 1, LLAMA_POOLING_TYPE_CLS = 2, LLAMA_POOLING_TYPE_LAST = 3, }; enum llama_attention_type { LLAMA_ATTENTION_TYPE_UNSPECIFIED = -1, LLAMA_ATTENTION_TYPE_CAUSAL = 0, LLAMA_ATTENTION_TYPE_NON_CAUSAL = 1, }; enum llama_split_mode { LLAMA_SPLIT_MODE_NONE = 0, // single GPU LLAMA_SPLIT_MODE_LAYER = 1, // split layers and KV across GPUs LLAMA_SPLIT_MODE_ROW = 2, // split rows across GPUs }; typedef struct llama_token_data { llama_token id; // token id float logit; // log-odds of the token float p; // probability of the token } llama_token_data; typedef struct llama_token_data_array { llama_token_data * data; size_t size; bool sorted; } llama_token_data_array; typedef bool (*llama_progress_callback)(float progress, void * user_data); // 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 (and/or the embeddings) for the respective token will not be output // typedef struct llama_batch { int32_t n_tokens; llama_token * token; float * embd; llama_pos * pos; int32_t * n_seq_id; llama_seq_id ** seq_id; int8_t * logits; // TODO: rename this to "output" // NOTE: helpers for smooth API transition - can be deprecated in the future // for future-proof code, use the above fields instead and ignore everything below // // pos[i] = all_pos_0 + i*all_pos_1 // llama_pos all_pos_0; // used if pos == NULL llama_pos all_pos_1; // used if pos == NULL llama_seq_id all_seq_id; // used if seq_id == NULL } llama_batch; enum llama_model_kv_override_type { LLAMA_KV_OVERRIDE_TYPE_INT, LLAMA_KV_OVERRIDE_TYPE_FLOAT, LLAMA_KV_OVERRIDE_TYPE_BOOL, LLAMA_KV_OVERRIDE_TYPE_STR, }; struct llama_model_kv_override { enum llama_model_kv_override_type tag; char key[128]; union { int64_t val_i64; double val_f64; bool val_bool; char val_str[128]; }; }; struct llama_model_params { int32_t n_gpu_layers; // number of layers to store in VRAM enum llama_split_mode split_mode; // how to split the model across multiple GPUs // main_gpu interpretation depends on split_mode: // LLAMA_SPLIT_MODE_NONE: the GPU that is used for the entire model // LLAMA_SPLIT_MODE_ROW: the GPU that is used for small tensors and intermediate results // LLAMA_SPLIT_MODE_LAYER: ignored int32_t main_gpu; // proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices() const float * tensor_split; // comma separated list of RPC servers to use for offloading const char * rpc_servers; // Called with a progress value between 0.0 and 1.0. Pass NULL to disable. // If the provided progress_callback returns true, model loading continues. // If it returns false, model loading is immediately aborted. llama_progress_callback progress_callback; // context pointer passed to the progress callback void * progress_callback_user_data; // override key-value pairs of the model meta data const struct llama_model_kv_override * kv_overrides; // Keep the booleans together to avoid misalignment during copy-by-value. bool vocab_only; // only load the vocabulary, no weights bool use_mmap; // use mmap if possible bool use_mlock; // force system to keep model in RAM bool check_tensors; // validate model tensor data }; // NOTE: changing the default values of parameters marked as [EXPERIMENTAL] may cause crashes or incorrect results in certain configurations // https://github.com/ggerganov/llama.cpp/pull/7544 struct llama_context_params { uint32_t seed; // RNG seed, -1 for random uint32_t n_ctx; // text context, 0 = from model uint32_t n_batch; // logical maximum batch size that can be submitted to llama_decode uint32_t n_ubatch; // physical maximum batch size uint32_t n_seq_max; // max number of sequences (i.e. distinct states for recurrent models) int32_t n_threads; // number of threads to use for generation int32_t n_threads_batch; // number of threads to use for batch processing enum llama_rope_scaling_type rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type` enum llama_pooling_type pooling_type; // whether to pool (sum) embedding results by sequence id enum llama_attention_type attention_type; // attention type to use for embeddings // ref: https://github.com/ggerganov/llama.cpp/pull/2054 float rope_freq_base; // RoPE base frequency, 0 = from model float rope_freq_scale; // RoPE frequency scaling factor, 0 = from model float yarn_ext_factor; // YaRN extrapolation mix factor, negative = from model float yarn_attn_factor; // YaRN magnitude scaling factor float yarn_beta_fast; // YaRN low correction dim float yarn_beta_slow; // YaRN high correction dim uint32_t yarn_orig_ctx; // YaRN original context size float defrag_thold; // defragment the KV cache if holes/size > thold, < 0 disabled (default) ggml_backend_sched_eval_callback cb_eval; void * cb_eval_user_data; enum ggml_type type_k; // data type for K cache [EXPERIMENTAL] enum ggml_type type_v; // data type for V cache [EXPERIMENTAL] // Keep the booleans together to avoid misalignment during copy-by-value. bool logits_all; // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead) bool embeddings; // if true, extract embeddings (together with logits) bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU bool flash_attn; // whether to use flash attention [EXPERIMENTAL] // Abort callback // if it returns true, execution of llama_decode() will be aborted // currently works only with CPU execution ggml_abort_callback abort_callback; void * abort_callback_data; }; // model quantization parameters typedef struct llama_model_quantize_params { int32_t nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency() enum llama_ftype ftype; // quantize to this llama_ftype enum ggml_type output_tensor_type; // output tensor type enum ggml_type token_embedding_type; // token embeddings tensor type bool allow_requantize; // allow quantizing non-f32/f16 tensors bool quantize_output_tensor; // quantize output.weight bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored bool pure; // quantize all tensors to the default type bool keep_split; // quantize to the same number of shards void * imatrix; // pointer to importance matrix data void * kv_overrides; // pointer to vector containing overrides } llama_model_quantize_params; // grammar types struct llama_grammar; // grammar element type enum llama_gretype { // end of rule definition LLAMA_GRETYPE_END = 0, // start of alternate definition for rule LLAMA_GRETYPE_ALT = 1, // non-terminal element: reference to rule LLAMA_GRETYPE_RULE_REF = 2, // terminal element: character (code point) LLAMA_GRETYPE_CHAR = 3, // inverse char(s) ([^a], [^a-b] [^abc]) LLAMA_GRETYPE_CHAR_NOT = 4, // modifies a preceding LLAMA_GRETYPE_CHAR or LLAMA_GRETYPE_CHAR_ALT to // be an inclusive range ([a-z]) LLAMA_GRETYPE_CHAR_RNG_UPPER = 5, // modifies a preceding LLAMA_GRETYPE_CHAR or // LLAMA_GRETYPE_CHAR_RNG_UPPER to add an alternate char to match ([ab], [a-zA]) LLAMA_GRETYPE_CHAR_ALT = 6, // any character (.) LLAMA_GRETYPE_CHAR_ANY = 7, }; 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 { double t_start_ms; double t_end_ms; double t_load_ms; double t_sample_ms; double t_p_eval_ms; double t_eval_ms; int32_t n_sample; int32_t n_p_eval; int32_t n_eval; }; // used in chat template typedef struct llama_chat_message { const char * role; const char * content; } llama_chat_message; // lora adapter struct llama_lora_adapter; // 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(void); //optional: LLAMA_API void llama_numa_init(enum ggml_numa_strategy numa); // Optional: an auto threadpool gets created in ggml if not passed explicitly LLAMA_API void llama_attach_threadpool( struct llama_context * ctx, ggml_threadpool_t threadpool, ggml_threadpool_t threadpool_batch); LLAMA_API void llama_detach_threadpool(struct llama_context * ctx); // 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 size_t llama_max_devices(void); LLAMA_API bool llama_supports_mmap (void); LLAMA_API bool llama_supports_mlock (void); LLAMA_API bool llama_supports_gpu_offload(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 uint32_t llama_n_ubatch (const struct llama_context * ctx); LLAMA_API uint32_t llama_n_seq_max (const struct llama_context * ctx); LLAMA_API enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx); LLAMA_API enum llama_vocab_type llama_vocab_type (const struct llama_model * model); LLAMA_API enum llama_rope_type llama_rope_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); LLAMA_API int32_t llama_n_layer (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 true if the model contains an encoder that requires llama_encode() call LLAMA_API bool llama_model_has_encoder(const struct llama_model * model); // Returns true if the model contains a decoder that requires llama_decode() call LLAMA_API bool llama_model_has_decoder(const struct llama_model * model); // For encoder-decoder models, this function returns id of the token that must be provided // to the decoder to start generating output sequence. For other models, it returns -1. LLAMA_API llama_token llama_model_decoder_start_token(const struct llama_model * model); // Returns true if the model is recurrent (like Mamba, RWKV, etc.) LLAMA_API bool llama_model_is_recurrent(const struct llama_model * model); // 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); // Load a LoRA adapter from file // The loaded adapter will be associated to the given model, and will be free when the model is deleted LLAMA_API struct llama_lora_adapter * llama_lora_adapter_init( struct llama_model * model, const char * path_lora); // Add a loaded LoRA adapter to given context // This will not modify model's weight LLAMA_API int32_t llama_lora_adapter_set( struct llama_context * ctx, struct llama_lora_adapter * adapter, float scale); // Remove a specific LoRA adapter from given context // Return -1 if the adapter is not present in the context LLAMA_API int32_t llama_lora_adapter_remove( struct llama_context * ctx, struct llama_lora_adapter * adapter); // Remove all LoRA adapters from given context LLAMA_API void llama_lora_adapter_clear( struct llama_context * ctx); // Manually free a LoRA adapter // Note: loaded adapters will be free when the associated model is deleted LLAMA_API void llama_lora_adapter_free(struct llama_lora_adapter * adapter); // Apply a loaded control vector to a llama_context, or if data is NULL, clear // the currently loaded vector. // n_embd should be the size of a single layer's control, and data should point // to an n_embd x n_layers buffer starting from layer 1. // il_start and il_end are the layer range the vector should apply to (both inclusive) // See llama_control_vector_load in common to load a control vector. LLAMA_API int32_t llama_control_vector_apply( struct llama_context * lctx, const float * data, size_t len, int32_t n_embd, int32_t il_start, int32_t il_end); // // KV cache // // Information associated with an individual cell in the KV cache view. struct llama_kv_cache_view_cell { // The position for this cell. Takes KV cache shifts into account. // May be negative if the cell is not populated. llama_pos pos; }; // An updateable view of the KV cache. struct llama_kv_cache_view { // Number of KV cache cells. This will be the same as the context size. int32_t n_cells; // Maximum number of sequences that can exist in a cell. It's not an error // if there are more sequences in a cell than this value, however they will // not be visible in the view cells_sequences. int32_t n_seq_max; // Number of tokens in the cache. For example, if there are two populated // cells, the first with 1 sequence id in it and the second with 2 sequence // ids then you'll have 3 tokens. int32_t token_count; // Number of populated cache cells. int32_t used_cells; // Maximum contiguous empty slots in the cache. int32_t max_contiguous; // Index to the start of the max_contiguous slot range. Can be negative // when cache is full. int32_t max_contiguous_idx; // Information for an individual cell. struct llama_kv_cache_view_cell * cells; // The sequences for each cell. There will be n_seq_max items per cell. llama_seq_id * cells_sequences; }; // 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_seq_max); // 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 - both cell info is erased and KV data is zeroed 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) // Returns false if a partial sequence cannot be removed. Removing a whole sequence never fails // seq_id < 0 : match any sequence // p0 < 0 : [0, p1] // p1 < 0 : [p0, inf) LLAMA_API bool 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: // - lazily on next llama_decode() // - explicitly with llama_kv_cache_update() // p0 < 0 : [0, p1] // p1 < 0 : [p0, inf) LLAMA_API void llama_kv_cache_seq_add( 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: // - lazily on next llama_decode() // - explicitly with llama_kv_cache_update() // 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); // Returns the largest position present in the KV cache for the specified sequence LLAMA_API llama_pos llama_kv_cache_seq_pos_max( struct llama_context * ctx, llama_seq_id seq_id); // Defragment the KV cache // This will be applied: // - lazily on next llama_decode() // - explicitly with llama_kv_cache_update() LLAMA_API void llama_kv_cache_defrag(struct llama_context * ctx); // Apply the KV cache updates (such as K-shifts, defragmentation, etc.) LLAMA_API void llama_kv_cache_update(struct llama_context * ctx); // // State / sessions // // Returns the *actual* size in bytes of the state // (rng, logits, embedding and kv_cache) // Only use when saving the state, not when restoring it, otherwise the size may be too small. LLAMA_API size_t llama_state_get_size(struct llama_context * ctx); LLAMA_API DEPRECATED(size_t llama_get_state_size(struct llama_context * ctx), "use llama_state_get_size instead"); // 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_state_get_data( struct llama_context * ctx, uint8_t * dst, size_t size); LLAMA_API DEPRECATED(size_t llama_copy_state_data( struct llama_context * ctx, uint8_t * dst), "use llama_state_get_data instead"); // Set the state reading from the specified address // Returns the number of bytes read LLAMA_API size_t llama_state_set_data( struct llama_context * ctx, const uint8_t * src, size_t size); LLAMA_API DEPRECATED(size_t llama_set_state_data( struct llama_context * ctx, const uint8_t * src), "use llama_state_set_data instead"); // Save/load session file LLAMA_API bool llama_state_load_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 DEPRECATED(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), "use llama_state_load_file instead"); LLAMA_API bool llama_state_save_file( struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count); LLAMA_API DEPRECATED(bool llama_save_session_file( struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count), "use llama_state_save_file instead"); // Get the exact size needed to copy the KV cache of a single sequence LLAMA_API size_t llama_state_seq_get_size( struct llama_context * ctx, llama_seq_id seq_id); // Copy the KV cache of a single sequence into the specified buffer LLAMA_API size_t llama_state_seq_get_data( struct llama_context * ctx, uint8_t * dst, size_t size, llama_seq_id seq_id); // Copy the sequence data (originally copied with `llama_state_seq_get_data`) into the specified sequence // Returns: // - Positive: Ok // - Zero: Failed to load LLAMA_API size_t llama_state_seq_set_data( struct llama_context * ctx, const uint8_t * src, size_t size, llama_seq_id dest_seq_id); LLAMA_API size_t llama_state_seq_save_file( struct llama_context * ctx, const char * filepath, llama_seq_id seq_id, const llama_token * tokens, size_t n_token_count); LLAMA_API size_t llama_state_seq_load_file( struct llama_context * ctx, const char * filepath, llama_seq_id dest_seq_id, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out); // // Decoding // // 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); // Processes a batch of tokens with the ecoder part of the encoder-decoder model. // Stores the encoder output internally for later use by the decoder cross-attention layers. // 0 - success // < 0 - error LLAMA_API int32_t llama_encode( struct llama_context * ctx, 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, int32_t n_threads, int32_t n_threads_batch); // Get the number of threads used for generation of a single token. LLAMA_API int32_t llama_n_threads(struct llama_context * ctx); // Get the number of threads used for prompt and batch processing (multiple token). LLAMA_API int32_t llama_n_threads_batch(struct llama_context * ctx); // Set whether the model is in embeddings mode or not // If true, embeddings will be returned but logits will not LLAMA_API void llama_set_embeddings(struct llama_context * ctx, bool embeddings); // Set whether to use causal attention or not // If set to true, the model will only attend to the past tokens LLAMA_API void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn); // Set abort callback LLAMA_API void llama_set_abort_callback(struct llama_context * ctx, ggml_abort_callback abort_callback, void * abort_callback_data); // Wait until all computations are finished // This is automatically done when using one of the functions below to obtain the computation results // and is not necessary to call it explicitly in most cases LLAMA_API void llama_synchronize(struct llama_context * ctx); // Token logits obtained from the last call to llama_decode() // The logits for which llama_batch.logits[i] != 0 are stored contiguously // in the order they have appeared in the batch. // Rows: number of tokens for which llama_batch.logits[i] != 0 // Cols: n_vocab LLAMA_API float * llama_get_logits(struct llama_context * ctx); // Logits for the ith token. For positive indices, Equivalent to: // llama_get_logits(ctx) + ctx->output_ids[i]*n_vocab // Negative indicies can be used to access logits in reverse order, -1 is the last logit. // returns NULL for invalid ids. LLAMA_API float * llama_get_logits_ith(struct llama_context * ctx, int32_t i); // Get all output token embeddings. // when pooling_type == LLAMA_POOLING_TYPE_NONE or when using a generative model, // the embeddings for which llama_batch.logits[i] != 0 are stored contiguously // in the order they have appeared in the batch. // shape: [n_outputs*n_embd] // Otherwise, returns NULL. LLAMA_API float * llama_get_embeddings(struct llama_context * ctx); // Get the embeddings for the ith token. For positive indices, Equivalent to: // llama_get_embeddings(ctx) + ctx->output_ids[i]*n_embd // Negative indicies can be used to access embeddings in reverse order, -1 is the last embedding. // shape: [n_embd] (1-dimensional) // returns NULL for invalid ids. LLAMA_API float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i); // Get the embeddings for a sequence id // Returns NULL if pooling_type is LLAMA_POOLING_TYPE_NONE // shape: [n_embd] (1-dimensional) LLAMA_API float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id); // // 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_attr llama_token_get_attr(const struct llama_model * model, llama_token token); // Check if the token is supposed to end generation (end-of-generation, eg. EOS, EOT, etc.) LLAMA_API bool llama_token_is_eog(const struct llama_model * model, llama_token token); // Identify if Token Id is a control token or a render-able token LLAMA_API bool llama_token_is_control(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_cls(const struct llama_model * model); // classification LLAMA_API llama_token llama_token_sep(const struct llama_model * model); // sentence separator LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line LLAMA_API llama_token llama_token_pad(const struct llama_model * model); // padding LLAMA_API bool llama_add_bos_token(const struct llama_model * model); LLAMA_API bool 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_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. LLAMA_API int32_t llama_tokenize( const struct llama_model * model, const char * text, int32_t text_len, llama_token * tokens, int32_t n_tokens_max, bool add_special, bool parse_special); // Token Id -> Piece. // Uses the vocabulary in the provided context. // Does not write null terminator to the buffer. // User can skip up to 'lstrip' leading spaces before copying (useful when encoding/decoding multiple tokens with 'add_space_prefix') // @param special If true, special tokens are rendered in the output. LLAMA_API int32_t llama_token_to_piece( const struct llama_model * model, llama_token token, char * buf, int32_t length, int32_t lstrip, bool special); /// @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. LLAMA_API int32_t llama_detokenize( const struct llama_model * model, const llama_token * tokens, int32_t n_tokens, char * text, int32_t text_len_max, bool remove_special, bool unparse_special); // // Chat templates // /// 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. LLAMA_API int32_t llama_chat_apply_template( const struct llama_model * model, const char * tmpl, const struct llama_chat_message * chat, size_t n_msg, bool add_ass, char * buf, int32_t length); // // Grammar // /// Initialize a llama_grammar. /// /// @param rules The rule elements of the grammar to initialize. /// @param n_rules The number of rules. /// @param start_rule_index The index of the root rule (the starting point of the grammar). /// @return The initialized llama_grammar or nullptr if initialization failed. 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); /// @details Apply constraints from grammar LLAMA_API void llama_grammar_sample( const struct llama_grammar * grammar, const struct llama_context * ctx, llama_token_data_array * candidates); LLAMA_API DEPRECATED(void llama_sample_grammar( struct llama_context * ctx, llama_token_data_array * candidates, const struct llama_grammar * grammar), "use llama_grammar_sample instead"); /// @details Accepts the sampled token into the grammar LLAMA_API void llama_grammar_accept_token( struct llama_grammar * grammar, struct llama_context * ctx, llama_token token); // // 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 logits Logits extracted from the original generation context. /// @param logits_guidance Logits extracted from 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. /// @param scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance. LLAMA_API void llama_sample_apply_guidance( struct llama_context * ctx, float * logits, float * logits_guidance, 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); /// @details Dynamic temperature implementation described in the paper https://arxiv.org/abs/2309.02772. LLAMA_API void llama_sample_entropy( struct llama_context * ctx, llama_token_data_array * candidates_p, float min_temp, float max_temp, float exponent_val); LLAMA_API void llama_sample_temp( struct llama_context * ctx, llama_token_data_array * candidates, float temp); /// @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 using the RNG of ctx. LLAMA_API llama_token llama_sample_token( struct llama_context * ctx, llama_token_data_array * candidates); // // Model split // /// @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" // Returns the split_path length. LLAMA_API int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count); /// @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" // Returns the split_prefix length. LLAMA_API int llama_split_prefix(char * split_prefix, size_t maxlen, const char * split_path, int split_no, int split_count); // 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 #include #include struct ggml_tensor; const std::vector> & llama_internal_get_tensor_map( struct llama_context * ctx ); struct llama_partial_utf8 { uint32_t value; // bit value so far (unshifted) int n_remain; // num bytes remaining; -1 indicates invalid sequence }; struct llama_grammar_candidate { size_t index; const uint32_t * code_points; llama_partial_utf8 partial_utf8; }; using llama_grammar_rule = std::vector< llama_grammar_element>; using llama_grammar_stack = std::vector; using llama_grammar_rules = std::vector; using llama_grammar_stacks = std::vector; using llama_grammar_candidates = std::vector; const llama_grammar_rules & llama_grammar_get_rules (const struct llama_grammar * grammar); llama_grammar_stacks & llama_grammar_get_stacks( struct llama_grammar * grammar); void llama_grammar_accept( const llama_grammar_rules & rules, const llama_grammar_stacks & stacks, const uint32_t chr, llama_grammar_stacks & new_stacks); std::vector llama_grammar_reject_candidates_for_stack( const llama_grammar_rules & rules, const llama_grammar_stack & stack, const llama_grammar_candidates & candidates); std::pair, llama_partial_utf8> decode_utf8( const std::string & src, llama_partial_utf8 partial_start); // Randomly selects a token from the candidates based on their probabilities using given std::mt19937. // This is a temporary workaround in order to fix race conditions when sampling with multiple sequences. llama_token llama_sample_token_with_rng(struct llama_context * ctx, llama_token_data_array * candidates, std::mt19937 & rng); #endif // LLAMA_API_INTERNAL #endif // LLAMA_H