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from __future__ import annotations
import ctypes
import enum
import os
import pathlib
from ._ggml import (
ggml_abort_callback,
ggml_backend_sched_eval_callback,
ggml_log_callback,
ggml_opt_get_optimizer_params,
)
from typing import (
Callable,
Union,
NewType,
Optional,
TYPE_CHECKING,
)
from llama_cpp._ctypes_extensions import (
load_shared_library,
byref,
ctypes_function_for_shared_library,
)
if TYPE_CHECKING:
from llama_cpp._ctypes_extensions import (
CtypesCData,
CtypesArray,
CtypesPointer,
CtypesVoidPointer,
CtypesRef,
CtypesPointerOrRef,
CtypesFuncPointer,
)
# Specify the base name of the shared library to load
_lib_base_name = "llama"
_override_base_path = os.environ.get("LLAMA_CPP_LIB_PATH")
_base_path = pathlib.Path(os.path.abspath(os.path.dirname(__file__))) / "lib" if _override_base_path is None else pathlib.Path(_override_base_path)
# Load the library
_lib = load_shared_library(_lib_base_name, _base_path)
ctypes_function = ctypes_function_for_shared_library(_lib)
# llama.h bindings
_lib.llama_max_devices.argtypes = []
_lib.llama_max_devices.restype = ctypes.c_size_t
LLAMA_MAX_DEVICES = _lib.llama_max_devices()
LLAMA_MAX_SEQ = 256
# define LLAMA_DEFAULT_SEED 0xFFFFFFFF
LLAMA_DEFAULT_SEED = 0xFFFFFFFF
# define LLAMA_TOKEN_NULL -1
LLAMA_TOKEN_NULL = -1
# define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
LLAMA_FILE_MAGIC_GGLA = 0x67676C61
# define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
LLAMA_FILE_MAGIC_GGSN = 0x6767736E
# define LLAMA_FILE_MAGIC_GGSQ 0x67677371u // 'ggsq'
LLAMA_FILE_MAGIC_GGSQ = 0x67677371
# define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
LLAMA_SESSION_MAGIC = LLAMA_FILE_MAGIC_GGSN
# define LLAMA_SESSION_VERSION 9
LLAMA_SESSION_VERSION = 9
# define LLAMA_STATE_SEQ_MAGIC LLAMA_FILE_MAGIC_GGSQ
LLAMA_STATE_SEQ_MAGIC = LLAMA_FILE_MAGIC_GGSQ
# define LLAMA_STATE_SEQ_VERSION 2
LLAMA_STATE_SEQ_VERSION = 2
# struct llama_vocab;
llama_vocab_p = NewType("llama_vocab_p", int)
llama_vocab_p_ctypes = ctypes.c_void_p
# struct llama_model;
llama_model_p = NewType("llama_model_p", int)
llama_model_p_ctypes = ctypes.c_void_p
# struct llama_context;
llama_context_p = NewType("llama_context_p", int)
llama_context_p_ctypes = ctypes.c_void_p
# # struct llama_sampler;
# llama_sampler_p = NewType("llama_sampler_p", int)
# llama_sampler_p_ctypes = ctypes.c_void_p
# struct llama_opt_params;
llama_opt_params_p = NewType("llama_opt_params_p", int)
llama_opt_params_p_ctypes = ctypes.c_void_p
# typedef struct llama_memory_i * llama_memory_t;
llama_memory_i_p = NewType("llama_memory_i_p", int)
llama_memory_i_p_ctypes = ctypes.c_void_p
# typedef int32_t llama_pos;
llama_pos = ctypes.c_int32
# typedef int32_t llama_token;
llama_token = ctypes.c_int32
llama_token_p = ctypes.POINTER(llama_token)
# typedef int32_t llama_seq_id;
llama_seq_id = ctypes.c_int32
# 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
# LLAMA_VOCAB_TYPE_PLAMO2 = 6, // PLaMo-2 tokenizer based on Aho-Corasick with dynamic programming
# };
class llama_vocab_type(enum.IntEnum):
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"""
LLAMA_VOCAB_TYPE_PLAMO2 = 6
"""PLaMo-2 tokenizer based on Aho-Corasick with dynamic programming"""
# NOTE: Deprecated and will be removed in the future. (already gone in llama.cpp)
# https://github.com/ggml-org/llama.cpp/blob/master/src/llama-vocab.h#L10
# // 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,
# LLAMA_VOCAB_PRE_TYPE_CHAMELEON = 26,
# LLAMA_VOCAB_PRE_TYPE_MINERVA = 27,
# LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM = 28,
# LLAMA_VOCAB_PRE_TYPE_GPT4O = 29,
# LLAMA_VOCAB_PRE_TYPE_SUPERBPE = 30,
# LLAMA_VOCAB_PRE_TYPE_TRILLION = 31,
# LLAMA_VOCAB_PRE_TYPE_BAILINGMOE = 32,
# LLAMA_VOCAB_PRE_TYPE_LLAMA4 = 33,
# LLAMA_VOCAB_PRE_TYPE_PIXTRAL = 34,
# LLAMA_VOCAB_PRE_TYPE_SEED_CODER = 35,
# LLAMA_VOCAB_PRE_TYPE_HUNYUAN = 36,
# LLAMA_VOCAB_PRE_TYPE_KIMI_K2 = 37,
# LLAMA_VOCAB_PRE_TYPE_HUNYUAN_DENSE = 38,
# LLAMA_VOCAB_PRE_TYPE_GROK_2 = 39,
# LLAMA_VOCAB_PRE_TYPE_GRANITE_DOCLING = 40,
# LLAMA_VOCAB_PRE_TYPE_MINIMAX_M2 = 41,
# LLAMA_VOCAB_PRE_TYPE_AFMOE = 42,
# LLAMA_VOCAB_PRE_TYPE_SOLAR_OPEN = 43,
# LLAMA_VOCAB_PRE_TYPE_YOUTU = 44,
# LLAMA_VOCAB_PRE_TYPE_EXAONE_MOE = 45,
# LLAMA_VOCAB_PRE_TYPE_QWEN35 = 46,
# LLAMA_VOCAB_PRE_TYPE_TINY_AYA = 47,
# LLAMA_VOCAB_PRE_TYPE_JOYAI_LLM = 48,
# LLAMA_VOCAB_PRE_TYPE_JAIS2 = 49,
# LLAMA_VOCAB_PRE_TYPE_GEMMA4 = 50,
# LLAMA_VOCAB_PRE_TYPE_SARVAM_MOE = 51,
# LLAMA_VOCAB_PRE_TYPE_MINICPM5 = 52,
# LLAMA_VOCAB_PRE_TYPE_WHITESPACE = 53,
# };
class llama_vocab_pre_type(enum.IntEnum):
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
LLAMA_VOCAB_PRE_TYPE_CHAMELEON = 26
LLAMA_VOCAB_PRE_TYPE_MINERVA = 27
LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM = 28
LLAMA_VOCAB_PRE_TYPE_GPT4O = 29
LLAMA_VOCAB_PRE_TYPE_SUPERBPE = 30
LLAMA_VOCAB_PRE_TYPE_TRILLION = 31
LLAMA_VOCAB_PRE_TYPE_BAILINGMOE = 32
LLAMA_VOCAB_PRE_TYPE_LLAMA4 = 33
LLAMA_VOCAB_PRE_TYPE_PIXTRAL = 34
LLAMA_VOCAB_PRE_TYPE_SEED_CODER = 35
LLAMA_VOCAB_PRE_TYPE_HUNYUAN = 36
LLAMA_VOCAB_PRE_TYPE_KIMI_K2 = 37
LLAMA_VOCAB_PRE_TYPE_HUNYUAN_DENSE = 38
LLAMA_VOCAB_PRE_TYPE_GROK_2 = 39
LLAMA_VOCAB_PRE_TYPE_GRANITE_DOCLING = 40
LLAMA_VOCAB_PRE_TYPE_MINIMAX_M2 = 41
LLAMA_VOCAB_PRE_TYPE_AFMOE = 42
LLAMA_VOCAB_PRE_TYPE_SOLAR_OPEN = 43
LLAMA_VOCAB_PRE_TYPE_YOUTU = 44
LLAMA_VOCAB_PRE_TYPE_EXAONE_MOE = 45
LLAMA_VOCAB_PRE_TYPE_QWEN35 = 46
LLAMA_VOCAB_PRE_TYPE_TINY_AYA = 47
LLAMA_VOCAB_PRE_TYPE_JOYAI_LLM = 48
LLAMA_VOCAB_PRE_TYPE_JAIS2 = 49
LLAMA_VOCAB_PRE_TYPE_GEMMA4 = 50
LLAMA_VOCAB_PRE_TYPE_SARVAM_MOE = 51
LLAMA_VOCAB_PRE_TYPE_MINICPM5 = 52
LLAMA_VOCAB_PRE_TYPE_WHITESPACE = 53
# // note: these values should be synchronized with ggml_rope
# // TODO: maybe move this enum to ggml.h (ggml_rope_type)
# enum llama_rope_type {
# LLAMA_ROPE_TYPE_NONE = -1,
# LLAMA_ROPE_TYPE_NORM = 0,
# LLAMA_ROPE_TYPE_NEOX = GGML_ROPE_TYPE_NEOX,
# LLAMA_ROPE_TYPE_MROPE = GGML_ROPE_TYPE_MROPE,
# LLAMA_ROPE_TYPE_IMROPE = GGML_ROPE_TYPE_IMROPE,
# LLAMA_ROPE_TYPE_VISION = GGML_ROPE_TYPE_VISION,
# };
class llama_rope_type(enum.IntEnum):
LLAMA_ROPE_TYPE_NONE = -1
LLAMA_ROPE_TYPE_NORM = 0
LLAMA_ROPE_TYPE_NEOX = GGML_ROPE_TYPE_NEOX = 2
LLAMA_ROPE_TYPE_MROPE = GGML_ROPE_TYPE_MROPE = 8
LLAMA_ROPE_TYPE_VISION = GGML_ROPE_TYPE_VISION = 24
LLAMA_ROPE_TYPE_IMROPE = GGML_ROPE_TYPE_IMROPE = 40
# 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,
# };
class llama_token_type(enum.IntEnum):
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,
# };
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
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, // removed from gguf files, use Q4_0 and runtime repack
# //LLAMA_FTYPE_MOSTLY_Q4_0_4_8 = 34, // removed from gguf files, use Q4_0 and runtime repack
# //LLAMA_FTYPE_MOSTLY_Q4_0_8_8 = 35, // removed from gguf files, use Q4_0 and runtime repack
# LLAMA_FTYPE_MOSTLY_TQ1_0 = 36, // except 1d tensors
# LLAMA_FTYPE_MOSTLY_TQ2_0 = 37, // except 1d tensors
# LLAMA_FTYPE_MOSTLY_MXFP4_MOE = 38, // except 1d tensors
# LLAMA_FTYPE_MOSTLY_NVFP4 = 39, // except 1d tensors
# LLAMA_FTYPE_MOSTLY_Q1_0 = 40, // except 1d tensors
#
# LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
# };
class llama_ftype(enum.IntEnum):
LLAMA_FTYPE_ALL_F32 = 0
LLAMA_FTYPE_MOSTLY_F16 = 1
LLAMA_FTYPE_MOSTLY_Q4_0 = 2
LLAMA_FTYPE_MOSTLY_Q4_1 = 3
LLAMA_FTYPE_MOSTLY_Q8_0 = 7
LLAMA_FTYPE_MOSTLY_Q5_0 = 8
LLAMA_FTYPE_MOSTLY_Q5_1 = 9
LLAMA_FTYPE_MOSTLY_Q2_K = 10
LLAMA_FTYPE_MOSTLY_Q3_K_S = 11
LLAMA_FTYPE_MOSTLY_Q3_K_M = 12
LLAMA_FTYPE_MOSTLY_Q3_K_L = 13
LLAMA_FTYPE_MOSTLY_Q4_K_S = 14
LLAMA_FTYPE_MOSTLY_Q4_K_M = 15
LLAMA_FTYPE_MOSTLY_Q5_K_S = 16
LLAMA_FTYPE_MOSTLY_Q5_K_M = 17
LLAMA_FTYPE_MOSTLY_Q6_K = 18
LLAMA_FTYPE_MOSTLY_IQ2_XXS = 19
LLAMA_FTYPE_MOSTLY_IQ2_XS = 20
LLAMA_FTYPE_MOSTLY_Q2_K_S = 21
LLAMA_FTYPE_MOSTLY_IQ3_XS = 22
LLAMA_FTYPE_MOSTLY_IQ3_XXS = 23
LLAMA_FTYPE_MOSTLY_IQ1_S = 24
LLAMA_FTYPE_MOSTLY_IQ4_NL = 25
LLAMA_FTYPE_MOSTLY_IQ3_S = 26
LLAMA_FTYPE_MOSTLY_IQ3_M = 27
LLAMA_FTYPE_MOSTLY_IQ2_S = 28
LLAMA_FTYPE_MOSTLY_IQ2_M = 29
LLAMA_FTYPE_MOSTLY_IQ4_XS = 30
LLAMA_FTYPE_MOSTLY_IQ1_M = 31
LLAMA_FTYPE_MOSTLY_BF16 = 32
# LLAMA_FTYPE_MOSTLY_Q4_0_4_4 = 33
# LLAMA_FTYPE_MOSTLY_Q4_0_4_8 = 34
# LLAMA_FTYPE_MOSTLY_Q4_0_8_8 = 35
LLAMA_FTYPE_MOSTLY_TQ1_0 = 36
LLAMA_FTYPE_MOSTLY_TQ2_0 = 37
LLAMA_FTYPE_MOSTLY_MXFP4_MOE = 38
LLAMA_FTYPE_MOSTLY_NVFP4 = 39
LLAMA_FTYPE_MOSTLY_Q1_0 = 40
LLAMA_FTYPE_GUESSED = 1024
# // Get the model file type (quantization) as a string, e.g. "Q8_0" or "Q4_K - Medium"
# LLAMA_API const char * llama_ftype_name(enum llama_ftype ftype);
@ctypes_function(
"llama_ftype_name",
[ctypes.c_int],
ctypes.c_char_p,
)
def llama_ftype_name(
ftype: llama_ftype, /
) -> bytes:
"""
Get the model file type (quantization) as a string, e.g. "Q8_0" or "Q4_K - Medium"
"""
# 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_LONGROPE = 3,
# LLAMA_ROPE_SCALING_TYPE_MAX_VALUE = LLAMA_ROPE_SCALING_TYPE_YARN,
# };
class llama_rope_scaling_type(enum.IntEnum):
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_LONGROPE = 3
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,
# LLAMA_POOLING_TYPE_RANK = 4, // used by reranking models to attach the classification head to the graph
# };
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
LLAMA_POOLING_TYPE_RANK = 4
# enum llama_attention_type {
# LLAMA_ATTENTION_TYPE_UNSPECIFIED = -1,
# LLAMA_ATTENTION_TYPE_CAUSAL = 0,
# LLAMA_ATTENTION_TYPE_NON_CAUSAL = 1,
# };
class llama_attention_type(enum.IntEnum):
LLAMA_ATTENTION_TYPE_UNSPECIFIED = -1
LLAMA_ATTENTION_TYPE_CAUSAL = 0
LLAMA_ATTENTION_TYPE_NON_CAUSAL = 1
# enum llama_flash_attn_type {
# LLAMA_FLASH_ATTN_TYPE_AUTO = -1,
# LLAMA_FLASH_ATTN_TYPE_DISABLED = 0,
# LLAMA_FLASH_ATTN_TYPE_ENABLED = 1,
# };
class llama_flash_attn_type(enum.IntEnum):
LLAMA_FLASH_ATTN_TYPE_AUTO = -1
LLAMA_FLASH_ATTN_TYPE_DISABLED = 0
LLAMA_FLASH_ATTN_TYPE_ENABLED = 1
# LLAMA_API const char * llama_flash_attn_type_name(enum llama_flash_attn_type flash_attn_type);
@ctypes_function(
"llama_flash_attn_type_name",
[ctypes.c_int],
ctypes.c_char_p,
)
def llama_flash_attn_type_name(
flash_attn_type: llama_flash_attn_type, /
) -> bytes:
"""
Gets the name of a llama_flash_attn_type.
"""
# 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 layers and KV across GPUs, use tensor parallelism if supported
# LLAMA_SPLIT_MODE_TENSOR = 3,
# };
class llama_split_mode(enum.IntEnum):
LLAMA_SPLIT_MODE_NONE = 0
LLAMA_SPLIT_MODE_LAYER = 1
LLAMA_SPLIT_MODE_ROW = 2
LLAMA_SPLIT_MODE_TENSOR = 3
# enum llama_context_type {
# LLAMA_CONTEXT_TYPE_DEFAULT = 0,
# LLAMA_CONTEXT_TYPE_MTP = 1,
# };
class llama_context_type(enum.IntEnum):
LLAMA_CONTEXT_TYPE_DEFAULT = 0
LLAMA_CONTEXT_TYPE_MTP = 1
# 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;
class llama_token_data(ctypes.Structure):
"""Used to store token data
Attributes:
id (llama_token): token id
logit (float): log-odds of the token
p (float): probability of the token"""
if TYPE_CHECKING:
id: llama_token
logit: float
p: float
_fields_ = [
("id", llama_token),
("logit", ctypes.c_float),
("p", ctypes.c_float),
]
llama_token_data_p = ctypes.POINTER(llama_token_data)
# typedef struct llama_token_data_array {
# // TODO: consider SoA
# // NOTE: this pointer can be modified by the samplers
# llama_token_data * data;
# size_t size;
# int64_t selected; // this is the index in the data array (i.e. not the token id)
# bool sorted; // note: do not assume the data is sorted - always check this flag
# } llama_token_data_array;
class llama_token_data_array(ctypes.Structure):
"""Used to sample tokens given logits
Attributes:
data (ctypes.Array[llama_token_data]): token data
size (int): size of the array
selected (int): index in the data array (i.e. not the token id)
sorted (bool): whether the array is sorted"""
if TYPE_CHECKING:
data: CtypesArray[llama_token_data]
size: int
selected: int
sorted: bool
_fields_ = [
("data", llama_token_data_p),
("size", ctypes.c_size_t),
("selected", ctypes.c_int64),
("sorted", ctypes.c_bool),
]
llama_token_data_array_p = ctypes.POINTER(llama_token_data_array)
# typedef bool (*llama_progress_callback)(float progress, void * user_data);
llama_progress_callback = ctypes.CFUNCTYPE(
ctypes.c_bool, ctypes.c_float, ctypes.c_void_p
)
# // Input data for llama_encode/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
# // (if set to NULL, the token position will be tracked automatically by llama_encode/llama_decode)
# // - seq_id : the sequence to which the respective token belongs
# // (if set to NULL, the sequence ID will be assumed to be 0)
# // - logits : if zero, the logits (and/or the embeddings) for the respective token will not be output
# // - logits : if zero, the logits (and/or the embeddings) for the respective token will not be output
# // (if set to NULL:
# // - if embeddings: all tokens are output
# // - if not: only the last token is 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"
# } llama_batch;
class llama_batch(ctypes.Structure):
"""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
Attributes:
n_tokens (ctypes.c_int32): number of tokens
token (ctypes.Array[llama_token]): the token ids of the input (used when embd is NULL)
embd (ctypes.Array[ctypes.ctypes.c_float]): token embeddings (i.e. float vector of size n_embd) (used when token is NULL)
pos (ctypes.Array[ctypes.Array[llama_pos]]): the positions of the respective token in the sequence
seq_id (ctypes.Array[ctypes.Array[llama_seq_id]]): the sequence to which the respective token belongs
logits (ctypes.Array[ctypes.ctypes.c_int8]): if zero, the logits for the respective token will not be output
"""
if TYPE_CHECKING:
n_tokens: ctypes.c_int32
token: CtypesArray[llama_token]
embd: CtypesArray[ctypes.c_float]
pos: CtypesArray[CtypesArray[llama_pos]]
n_seq_id: CtypesArray[ctypes.c_int]
seq_id: CtypesArray[CtypesArray[llama_seq_id]]
logits: CtypesArray[ctypes.c_int8]
_fields_ = [
("n_tokens", ctypes.c_int32),
("token", ctypes.POINTER(llama_token)),
("embd", ctypes.POINTER(ctypes.c_float)),
("pos", ctypes.POINTER(llama_pos)),
("n_seq_id", ctypes.POINTER(ctypes.c_int32)),
("seq_id", ctypes.POINTER(ctypes.POINTER(llama_seq_id))),
("logits", ctypes.POINTER(ctypes.c_int8)),
]
# 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,
# };
class LlamaModelKVOverrideType(enum.IntEnum):
LLAMA_KV_OVERRIDE_TYPE_INT = 0
LLAMA_KV_OVERRIDE_TYPE_FLOAT = 1
LLAMA_KV_OVERRIDE_TYPE_BOOL = 2
LLAMA_KV_OVERRIDE_TYPE_STR = 3
# enum llama_model_meta_key {
# LLAMA_MODEL_META_KEY_SAMPLING_SEQUENCE,
# LLAMA_MODEL_META_KEY_SAMPLING_TOP_K,
# LLAMA_MODEL_META_KEY_SAMPLING_TOP_P,
# LLAMA_MODEL_META_KEY_SAMPLING_MIN_P,
# LLAMA_MODEL_META_KEY_SAMPLING_XTC_PROBABILITY,
# LLAMA_MODEL_META_KEY_SAMPLING_XTC_THRESHOLD,
# LLAMA_MODEL_META_KEY_SAMPLING_TEMP,
# LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_LAST_N,
# LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_REPEAT,
# LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT,
# LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_TAU,
# LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_ETA,
# };
class LlamaModelMetaKey(enum.IntEnum):
LLAMA_MODEL_META_KEY_SAMPLING_SEQUENCE = 0
LLAMA_MODEL_META_KEY_SAMPLING_TOP_K = 1
LLAMA_MODEL_META_KEY_SAMPLING_TOP_P = 2
LLAMA_MODEL_META_KEY_SAMPLING_MIN_P = 3
LLAMA_MODEL_META_KEY_SAMPLING_XTC_PROBABILITY = 4
LLAMA_MODEL_META_KEY_SAMPLING_XTC_THRESHOLD = 5
LLAMA_MODEL_META_KEY_SAMPLING_TEMP = 6
LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_LAST_N = 7
LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_REPEAT = 8
LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT = 9
LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_TAU = 10
LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_ETA = 11
# 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];
# };
# };
class llama_model_kv_override_value(ctypes.Union):
_fields_ = [
("val_i64", ctypes.c_int64),
("val_f64", ctypes.c_double),
("val_bool", ctypes.c_bool),
("val_str", ctypes.c_char * 128),
]
if TYPE_CHECKING:
val_i64: int
val_f64: float
val_bool: bool
val_str: bytes
class llama_model_kv_override(ctypes.Structure):
_fields_ = [
("tag", ctypes.c_int),
("key", ctypes.c_char * 128),
("value", llama_model_kv_override_value),
]
if TYPE_CHECKING:
tag: int
key: bytes
value: Union[int, float, bool, bytes]
llama_model_kv_override_p = ctypes.POINTER(llama_model_kv_override)
# struct llama_model_tensor_buft_override {
# const char * pattern;
# ggml_backend_buffer_type_t buft;
# };
class llama_model_tensor_buft_override(ctypes.Structure):
_fields_ = [
("pattern", ctypes.c_char_p),
("buft", ctypes.c_void_p),
]
if TYPE_CHECKING:
pattern: ctypes.c_char_p
buft: ctypes.c_void_p
# struct llama_model_params {
# // NULL-terminated list of devices to use for offloading (if NULL, all available devices are used)
# ggml_backend_dev_t * devices;
#
# // NULL-terminated list of buffer types to use for tensors that match a pattern
# const struct llama_model_tensor_buft_override * tensor_buft_overrides;
#
# int32_t n_gpu_layers; // number of layers to store in VRAM, a negative value means all layers
# 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;
# // 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_direct_io; // use direct io, takes precedence over use_mmap when supported
# bool use_mlock; // force system to keep model in RAM
# bool check_tensors; // validate model tensor data
# bool use_extra_bufts; // use extra buffer types (used for weight repacking)
# bool no_host; // bypass host buffer allowing extra buffers to be used
# bool no_alloc; // only load metadata and simulate memory allocations
# };
class llama_model_params(ctypes.Structure):
"""Parameters for llama_model
Attributes:
devices (ctypes.Array[ggml_backend_dev_t]): NULL-terminated list of devices to use for offloading (if NULL, all available devices are used)
tensor_buft_overrides(llama_model_tensor_buft_override): NULL-terminated list of buffer types to use for tensors that match a pattern
n_gpu_layers (int): number of layers to store in VRAM, a negative value means all layers
split_mode (int): how to split the model across multiple GPUs
main_gpu (int): the GPU that is used for the entire model. main_gpu interpretation depends on split_mode: LLAMA_SPLIT_NONE: the GPU that is used for the entire model LLAMA_SPLIT_ROW: the GPU that is used for small tensors and intermediate results LLAMA_SPLIT_LAYER: ignored
tensor_split (ctypes.Array[ctypes.ctypes.c_float]): proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices()
progress_callback (llama_progress_callback): 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.
progress_callback_user_data (ctypes.ctypes.c_void_p): context pointer passed to the progress callback
kv_overrides (ctypes.Array[llama_model_kv_override]): override key-value pairs of the model meta data
vocab_only (bool): only load the vocabulary, no weights
use_mmap (bool): use mmap if possible
use_direct_io(bool): use direct io, takes precedence over use_mmap when supported
use_mlock (bool): force system to keep model in RAM
check_tensors (bool): validate model tensor data
use_extra_bufts (bool): use extra buffer types (used for weight repacking)
no_host (bool): bypass host buffer allowing extra buffers to be used
no_alloc (bool): only load metadata and simulate memory allocations"""
if TYPE_CHECKING:
devices: CtypesArray[ctypes.c_void_p] # NOTE: unused
tensor_buft_overrides: CtypesPointer[llama_model_tensor_buft_override]
n_gpu_layers: int
split_mode: int
main_gpu: int
tensor_split: CtypesArray[ctypes.c_float]
progress_callback: Callable[[float, ctypes.c_void_p], bool]
progress_callback_user_data: ctypes.c_void_p
kv_overrides: CtypesArray[llama_model_kv_override]
vocab_only: bool
use_mmap: bool
use_direct_io: bool
use_mlock: bool
check_tensors: bool
use_extra_bufts: bool
no_host: bool
no_alloc: bool
_fields_ = [
("devices", ctypes.c_void_p), # NOTE: unnused
("tensor_buft_overrides", ctypes.POINTER(llama_model_tensor_buft_override)),
("n_gpu_layers", ctypes.c_int32),
("split_mode", ctypes.c_int),
("main_gpu", ctypes.c_int32),
("tensor_split", ctypes.POINTER(ctypes.c_float)),
("progress_callback", llama_progress_callback),
("progress_callback_user_data", ctypes.c_void_p),
("kv_overrides", ctypes.POINTER(llama_model_kv_override)),
("vocab_only", ctypes.c_bool),
("use_mmap", ctypes.c_bool),
("use_direct_io", ctypes.c_bool),
("use_mlock", ctypes.c_bool),
("check_tensors", ctypes.c_bool),
("use_extra_bufts", ctypes.c_bool),
("no_host", ctypes.c_bool),
("no_alloc", ctypes.c_bool),
]
llama_model_params_p = ctypes.POINTER(llama_model_params)
# struct llama_sampler_seq_config {
# llama_seq_id seq_id;
# struct llama_sampler * sampler;
# };
class llama_sampler_seq_config(ctypes.Structure):
if TYPE_CHECKING:
seq_id: llama_seq_id
sampler: ctypes.c_void_p
_fields_ = [
("seq_id", llama_seq_id),
("sampler", ctypes.c_void_p),
]
llama_sampler_seq_config_p = ctypes.POINTER(llama_sampler_seq_config)
# // NOTE: changing the default values of parameters marked as [EXPERIMENTAL] may cause crashes or incorrect results in certain configurations
# // https://github.com/ggml-org/llama.cpp/pull/7544
# struct llama_context_params {
# 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)
# uint32_t n_rs_seq; // number of recurrent-state snapshots per seq for rollback (0 = no rollback) [EXPERIMENTAL]
# uint32_t n_outputs_max; // max outputs in a ubatch (0 = n_batch)
# 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_context_type ctx_type; // set the context type (e.g. MTP)
# 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
# enum llama_flash_attn_type flash_attn_type; // when to enable Flash Attention
# // ref: https://github.com/ggml-org/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; // [DEPRECATED] 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]
# // 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;
# // Keep the booleans together and at the end of the struct to avoid misalignment during copy-by-value.
# bool embeddings; // if true, extract embeddings (together with logits)
# bool offload_kqv; // offload the KQV ops (including the KV cache) to GPU
# bool no_perf; // measure performance timings
# bool op_offload; // offload host tensor operations to device
# bool swa_full; // use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
# // NOTE: setting to false when n_seq_max > 1 can cause bad performance in some cases
# // ref: https://github.com/ggml-org/llama.cpp/pull/13845#issuecomment-2924800573
# bool kv_unified; // use a unified buffer across the input sequences when computing the attention
# // try to disable when n_seq_max > 1 for improved performance when the sequences do not share a large prefix
# // ref: https://github.com/ggml-org/llama.cpp/pull/14363
# // [EXPERIMENTAL]
# // backend sampler chain configuration (make sure the caller keeps the sampler chains alive)
# // note: the samplers must be sampler chains (i.e. use llama_sampler_chain_init)
# struct llama_sampler_seq_config * samplers;
# size_t n_samplers;
# // a source/target/parent context
# // can be utilized in various ways, for example by sharing results or llama_memory between 2 contexts
# struct llama_context * ctx_other;
# };
class llama_context_params(ctypes.Structure):
"""Parameters for llama_context
Attributes:
n_ctx (int): text context, 0 = from model
n_batch (int): logical maximum batch size that can be submitted to llama_decode
n_ubatch (int): physical maximum batch size
n_seq_max (int): max number of sequences (i.e. distinct states for recurrent models)
n_rs_seq (int): number of recurrent-state snapshots per seq for rollback (0 = no rollback) [EXPERIMENTAL]
n_outputs_max (int): max outputs in a ubatch (0 = n_batch)
n_threads (int): number of threads to use for generation
n_threads_batch (int): number of threads to use for batch processing
ctx_type (int): set the context type (e.g. MTP)
rope_scaling_type (int): RoPE scaling type, from `enum llama_rope_scaling_type`
pooling_type (int): whether to pool (sum) embedding results by sequence id (ignored if no pooling layer)
attention_type (int): attention type to use for embeddings
flash_attn_type (int): when to enable Flash Attention
rope_freq_base (float): RoPE base frequency, 0 = from model
rope_freq_scale (float): RoPE frequency scaling factor, 0 = from model
yarn_ext_factor (float): YaRN extrapolation mix factor, negative = from model
yarn_attn_factor (float): YaRN magnitude scaling factor
yarn_beta_fast (float): YaRN low correction dim
yarn_beta_slow (float): YaRN high correction dim
yarn_orig_ctx (int): YaRN original context size
defrag_thold (float): [DEPRECATED] defragment the KV cache if holes/size > thold, <= 0 disabled (default)
cb_eval (ggml_backend_sched_eval_callback): callback for scheduling eval
cb_eval_user_data (ctypes.ctypes.c_void_p): user data for cb_eval
type_k (int): data type for K cache
type_v (int): data type for V cache
abort_callback (ggml_abort_callback): abort callback if it returns true, execution of llama_decode() will be aborted
abort_callback_data (ctypes.ctypes.c_void_p): data for abort_callback
embeddings (bool): if true, extract embeddings (together with logits)
offload_kqv (bool): whether to offload the KQV ops (including the KV cache) to GPU
no_perf (bool): whether to measure performance timings
op_offload(bool): whether to offload host tensor operations to device
swa_full(bool): whether to use full-size SWA cache
kv_unified(bool): use a unified buffer across the input sequences when computing the attention
samplers(llama_sampler_seq_config *): the samplers must be sampler chains (i.e. use llama_sampler_chain_init)
n_samplers(size_t): numbers of sampler chains
ctx_other(llama_context *): a source/target/parent context can be utilized in various ways, for example by sharing results or llama_memory between 2 contexts
"""
if TYPE_CHECKING:
n_ctx: int
n_batch: int
n_ubatch: int
n_seq_max: int
n_rs_seq: int
n_outputs_max: int
n_threads: int
n_threads_batch: int
ctx_type: int
rope_scaling_type: int
pooling_type: int
attention_type: int
flash_attn_type: int
rope_freq_base: float
rope_freq_scale: float
yarn_ext_factor: float
yarn_attn_factor: float
yarn_beta_fast: float
yarn_beta_slow: float
yarn_orig_ctx: int
defrag_thold: float
cb_eval: Callable[[ctypes.c_void_p, bool], bool]
cb_eval_user_data: ctypes.c_void_p
type_k: int
type_v: int
abort_callback: Callable[[ctypes.c_void_p], bool]
abort_callback_data: ctypes.c_void_p
embeddings: bool
offload_kqv: bool
no_perf: bool
op_offload:bool