from __future__ import annotations from enum import Enum, IntEnum, auto from typing import Any # # constants # GGUF_MAGIC = 0x46554747 # "GGUF" GGUF_VERSION = 3 GGUF_DEFAULT_ALIGNMENT = 32 GGML_QUANT_VERSION = 2 # GGML_QNT_VERSION from ggml.h # # metadata keys # class Keys: class General: TYPE = "general.type" ARCHITECTURE = "general.architecture" QUANTIZATION_VERSION = "general.quantization_version" ALIGNMENT = "general.alignment" FILE_TYPE = "general.file_type" # Authorship Metadata NAME = "general.name" AUTHOR = "general.author" VERSION = "general.version" ORGANIZATION = "general.organization" FINETUNE = "general.finetune" BASENAME = "general.basename" DESCRIPTION = "general.description" QUANTIZED_BY = "general.quantized_by" SIZE_LABEL = "general.size_label" # Licensing details LICENSE = "general.license" LICENSE_NAME = "general.license.name" LICENSE_LINK = "general.license.link" # Typically represents the converted GGUF repo (Unless native) URL = "general.url" # Model Website/Paper DOI = "general.doi" UUID = "general.uuid" REPO_URL = "general.repo_url" # Model Source Repository (git/svn/etc...) # Model Source during conversion SOURCE_URL = "general.source.url" # Model Website/Paper SOURCE_DOI = "general.source.doi" SOURCE_UUID = "general.source.uuid" SOURCE_REPO_URL = "general.source.repo_url" # Model Source Repository (git/svn/etc...) # Base Model Source. There can be more than one source if it's a merged # model like with 'Mistral-7B-Merge-14-v0.1'. This will assist in # tracing linage of models as it is finetuned or merged over time. BASE_MODEL_COUNT = "general.base_model.count" BASE_MODEL_NAME = "general.base_model.{id}.name" BASE_MODEL_AUTHOR = "general.base_model.{id}.author" BASE_MODEL_VERSION = "general.base_model.{id}.version" BASE_MODEL_ORGANIZATION = "general.base_model.{id}.organization" BASE_MODEL_URL = "general.base_model.{id}.url" # Model Website/Paper BASE_MODEL_DOI = "general.base_model.{id}.doi" BASE_MODEL_UUID = "general.base_model.{id}.uuid" BASE_MODEL_REPO_URL = "general.base_model.{id}.repo_url" # Model Source Repository (git/svn/etc...) # Array based KV stores TAGS = "general.tags" LANGUAGES = "general.languages" DATASETS = "general.datasets" class LLM: VOCAB_SIZE = "{arch}.vocab_size" CONTEXT_LENGTH = "{arch}.context_length" EMBEDDING_LENGTH = "{arch}.embedding_length" BLOCK_COUNT = "{arch}.block_count" LEADING_DENSE_BLOCK_COUNT = "{arch}.leading_dense_block_count" FEED_FORWARD_LENGTH = "{arch}.feed_forward_length" EXPERT_FEED_FORWARD_LENGTH = "{arch}.expert_feed_forward_length" EXPERT_SHARED_FEED_FORWARD_LENGTH = "{arch}.expert_shared_feed_forward_length" USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual" TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout" EXPERT_COUNT = "{arch}.expert_count" EXPERT_USED_COUNT = "{arch}.expert_used_count" EXPERT_SHARED_COUNT = "{arch}.expert_shared_count" EXPERT_WEIGHTS_SCALE = "{arch}.expert_weights_scale" POOLING_TYPE = "{arch}.pooling_type" LOGIT_SCALE = "{arch}.logit_scale" DECODER_START_TOKEN_ID = "{arch}.decoder_start_token_id" ATTN_LOGIT_SOFTCAPPING = "{arch}.attn_logit_softcapping" FINAL_LOGIT_SOFTCAPPING = "{arch}.final_logit_softcapping" SWIN_NORM = "{arch}.swin_norm" RESCALE_EVERY_N_LAYERS = "{arch}.rescale_every_n_layers" TIME_MIX_EXTRA_DIM = "{arch}.time_mix_extra_dim" TIME_DECAY_EXTRA_DIM = "{arch}.time_decay_extra_dim" RESIDUAL_SCALE = "{arch}.residual_scale" EMBEDDING_SCALE = "{arch}.embedding_scale" class Attention: HEAD_COUNT = "{arch}.attention.head_count" HEAD_COUNT_KV = "{arch}.attention.head_count_kv" MAX_ALIBI_BIAS = "{arch}.attention.max_alibi_bias" CLAMP_KQV = "{arch}.attention.clamp_kqv" KEY_LENGTH = "{arch}.attention.key_length" VALUE_LENGTH = "{arch}.attention.value_length" LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon" LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon" CAUSAL = "{arch}.attention.causal" Q_LORA_RANK = "{arch}.attention.q_lora_rank" KV_LORA_RANK = "{arch}.attention.kv_lora_rank" REL_BUCKETS_COUNT = "{arch}.attention.relative_buckets_count" SLIDING_WINDOW = "{arch}.attention.sliding_window" SCALE = "{arch}.attention.scale" class Rope: DIMENSION_COUNT = "{arch}.rope.dimension_count" FREQ_BASE = "{arch}.rope.freq_base" SCALING_TYPE = "{arch}.rope.scaling.type" SCALING_FACTOR = "{arch}.rope.scaling.factor" SCALING_ATTN_FACTOR = "{arch}.rope.scaling.attn_factor" SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length" SCALING_FINETUNED = "{arch}.rope.scaling.finetuned" SCALING_YARN_LOG_MUL = "{arch}.rope.scaling.yarn_log_multiplier" class Split: LLM_KV_SPLIT_NO = "split.no" LLM_KV_SPLIT_COUNT = "split.count" LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count" class SSM: CONV_KERNEL = "{arch}.ssm.conv_kernel" INNER_SIZE = "{arch}.ssm.inner_size" STATE_SIZE = "{arch}.ssm.state_size" TIME_STEP_RANK = "{arch}.ssm.time_step_rank" DT_B_C_RMS = "{arch}.ssm.dt_b_c_rms" class WKV: HEAD_SIZE = "{arch}.wkv.head_size" class Tokenizer: MODEL = "tokenizer.ggml.model" PRE = "tokenizer.ggml.pre" LIST = "tokenizer.ggml.tokens" TOKEN_TYPE = "tokenizer.ggml.token_type" TOKEN_TYPE_COUNT = "tokenizer.ggml.token_type_count" # for BERT-style token types SCORES = "tokenizer.ggml.scores" MERGES = "tokenizer.ggml.merges" BOS_ID = "tokenizer.ggml.bos_token_id" EOS_ID = "tokenizer.ggml.eos_token_id" UNK_ID = "tokenizer.ggml.unknown_token_id" SEP_ID = "tokenizer.ggml.seperator_token_id" PAD_ID = "tokenizer.ggml.padding_token_id" CLS_ID = "tokenizer.ggml.cls_token_id" MASK_ID = "tokenizer.ggml.mask_token_id" ADD_BOS = "tokenizer.ggml.add_bos_token" ADD_EOS = "tokenizer.ggml.add_eos_token" ADD_PREFIX = "tokenizer.ggml.add_space_prefix" REMOVE_EXTRA_WS = "tokenizer.ggml.remove_extra_whitespaces" PRECOMPILED_CHARSMAP = "tokenizer.ggml.precompiled_charsmap" HF_JSON = "tokenizer.huggingface.json" RWKV = "tokenizer.rwkv.world" CHAT_TEMPLATE = "tokenizer.chat_template" CHAT_TEMPLATE_N = "tokenizer.chat_template.{name}" CHAT_TEMPLATES = "tokenizer.chat_templates" # FIM/Infill special tokens constants PREFIX_ID = "tokenizer.ggml.prefix_token_id" SUFFIX_ID = "tokenizer.ggml.suffix_token_id" MIDDLE_ID = "tokenizer.ggml.middle_token_id" EOT_ID = "tokenizer.ggml.eot_token_id" EOM_ID = "tokenizer.ggml.eom_token_id" class Adapter: TYPE = "adapter.type" LORA_ALPHA = "adapter.lora.alpha" # # recommended mapping of model tensor names for storage in gguf # class GGUFType: MODEL = "model" ADAPTER = "adapter" class MODEL_ARCH(IntEnum): LLAMA = auto() FALCON = auto() BAICHUAN = auto() GROK = auto() GPT2 = auto() GPTJ = auto() GPTNEOX = auto() MPT = auto() STARCODER = auto() REFACT = auto() BERT = auto() NOMIC_BERT = auto() JINA_BERT_V2 = auto() BLOOM = auto() STABLELM = auto() QWEN = auto() QWEN2 = auto() QWEN2MOE = auto() PHI2 = auto() PHI3 = auto() PLAMO = auto() CODESHELL = auto() ORION = auto() INTERNLM2 = auto() MINICPM = auto() MINICPM3 = auto() GEMMA = auto() GEMMA2 = auto() STARCODER2 = auto() RWKV6 = auto() MAMBA = auto() XVERSE = auto() COMMAND_R = auto() DBRX = auto() OLMO = auto() OLMOE = auto() OPENELM = auto() ARCTIC = auto() DEEPSEEK2 = auto() CHATGLM = auto() BITNET = auto() T5 = auto() T5ENCODER = auto() JAIS = auto() NEMOTRON = auto() EXAONE = auto() GRANITE = auto() GRANITE_MOE = auto() CHAMELEON = auto() class MODEL_TENSOR(IntEnum): TOKEN_EMBD = auto() TOKEN_EMBD_NORM = auto() TOKEN_TYPES = auto() POS_EMBD = auto() OUTPUT = auto() OUTPUT_NORM = auto() ROPE_FREQS = auto() ROPE_FACTORS_LONG = auto() ROPE_FACTORS_SHORT = auto() ATTN_Q = auto() ATTN_K = auto() ATTN_V = auto() ATTN_QKV = auto() ATTN_OUT = auto() ATTN_NORM = auto() ATTN_NORM_2 = auto() ATTN_OUT_NORM = auto() ATTN_POST_NORM = auto() ATTN_ROT_EMBD = auto() FFN_GATE_INP = auto() FFN_GATE_INP_SHEXP = auto() FFN_NORM = auto() FFN_PRE_NORM = auto() FFN_POST_NORM = auto() FFN_GATE = auto() FFN_DOWN = auto() FFN_UP = auto() FFN_ACT = auto() FFN_NORM_EXP = auto() FFN_GATE_EXP = auto() FFN_DOWN_EXP = auto() FFN_UP_EXP = auto() FFN_GATE_SHEXP = auto() FFN_DOWN_SHEXP = auto() FFN_UP_SHEXP = auto() ATTN_Q_NORM = auto() ATTN_K_NORM = auto() LAYER_OUT_NORM = auto() SSM_IN = auto() SSM_CONV1D = auto() SSM_X = auto() SSM_DT = auto() SSM_A = auto() SSM_D = auto() SSM_OUT = auto() TIME_MIX_W1 = auto() TIME_MIX_W2 = auto() TIME_MIX_LERP_X = auto() TIME_MIX_LERP_K = auto() TIME_MIX_LERP_V = auto() TIME_MIX_LERP_R = auto() TIME_MIX_LERP_G = auto() TIME_MIX_LERP_W = auto() TIME_MIX_FIRST = auto() TIME_MIX_DECAY = auto() TIME_MIX_DECAY_W1 = auto() TIME_MIX_DECAY_W2 = auto() TIME_MIX_KEY = auto() TIME_MIX_VALUE = auto() TIME_MIX_RECEPTANCE = auto() TIME_MIX_GATE = auto() TIME_MIX_LN = auto() TIME_MIX_OUTPUT = auto() CHANNEL_MIX_LERP_K = auto() CHANNEL_MIX_LERP_R = auto() CHANNEL_MIX_KEY = auto() CHANNEL_MIX_RECEPTANCE = auto() CHANNEL_MIX_VALUE = auto() ATTN_Q_A = auto() ATTN_Q_B = auto() ATTN_KV_A_MQA = auto() ATTN_KV_B = auto() ATTN_Q_A_NORM = auto() ATTN_KV_A_NORM = auto() FFN_SUB_NORM = auto() ATTN_SUB_NORM = auto() DEC_ATTN_NORM = auto() DEC_ATTN_Q = auto() DEC_ATTN_K = auto() DEC_ATTN_V = auto() DEC_ATTN_OUT = auto() DEC_ATTN_REL_B = auto() DEC_CROSS_ATTN_NORM = auto() DEC_CROSS_ATTN_Q = auto() DEC_CROSS_ATTN_K = auto() DEC_CROSS_ATTN_V = auto() DEC_CROSS_ATTN_OUT = auto() DEC_CROSS_ATTN_REL_B = auto() DEC_FFN_NORM = auto() DEC_FFN_GATE = auto() DEC_FFN_DOWN = auto() DEC_FFN_UP = auto() DEC_OUTPUT_NORM = auto() ENC_ATTN_NORM = auto() ENC_ATTN_Q = auto() ENC_ATTN_K = auto() ENC_ATTN_V = auto() ENC_ATTN_OUT = auto() ENC_ATTN_REL_B = auto() ENC_FFN_NORM = auto() ENC_FFN_GATE = auto() ENC_FFN_DOWN = auto() ENC_FFN_UP = auto() ENC_OUTPUT_NORM = auto() CLS = auto() # classifier CLS_OUT = auto() # classifier output projection MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.LLAMA: "llama", MODEL_ARCH.FALCON: "falcon", MODEL_ARCH.BAICHUAN: "baichuan", MODEL_ARCH.GROK: "grok", MODEL_ARCH.GPT2: "gpt2", MODEL_ARCH.GPTJ: "gptj", MODEL_ARCH.GPTNEOX: "gptneox", MODEL_ARCH.MPT: "mpt", MODEL_ARCH.STARCODER: "starcoder", MODEL_ARCH.REFACT: "refact", MODEL_ARCH.BERT: "bert", MODEL_ARCH.NOMIC_BERT: "nomic-bert", MODEL_ARCH.JINA_BERT_V2: "jina-bert-v2", MODEL_ARCH.BLOOM: "bloom", MODEL_ARCH.STABLELM: "stablelm", MODEL_ARCH.QWEN: "qwen", MODEL_ARCH.QWEN2: "qwen2", MODEL_ARCH.QWEN2MOE: "qwen2moe", MODEL_ARCH.PHI2: "phi2", MODEL_ARCH.PHI3: "phi3", MODEL_ARCH.PLAMO: "plamo", MODEL_ARCH.CODESHELL: "codeshell", MODEL_ARCH.ORION: "orion", MODEL_ARCH.INTERNLM2: "internlm2", MODEL_ARCH.MINICPM: "minicpm", MODEL_ARCH.MINICPM3: "minicpm3", MODEL_ARCH.GEMMA: "gemma", MODEL_ARCH.GEMMA2: "gemma2", MODEL_ARCH.STARCODER2: "starcoder2", MODEL_ARCH.RWKV6: "rwkv6", MODEL_ARCH.MAMBA: "mamba", MODEL_ARCH.XVERSE: "xverse", MODEL_ARCH.COMMAND_R: "command-r", MODEL_ARCH.DBRX: "dbrx", MODEL_ARCH.OLMO: "olmo", MODEL_ARCH.OLMOE: "olmoe", MODEL_ARCH.OPENELM: "openelm", MODEL_ARCH.ARCTIC: "arctic", MODEL_ARCH.DEEPSEEK2: "deepseek2", MODEL_ARCH.CHATGLM: "chatglm", MODEL_ARCH.BITNET: "bitnet", MODEL_ARCH.T5: "t5", MODEL_ARCH.T5ENCODER: "t5encoder", MODEL_ARCH.JAIS: "jais", MODEL_ARCH.NEMOTRON: "nemotron", MODEL_ARCH.EXAONE: "exaone", MODEL_ARCH.GRANITE: "granite", MODEL_ARCH.GRANITE_MOE: "granitemoe", MODEL_ARCH.CHAMELEON: "chameleon", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { MODEL_TENSOR.TOKEN_EMBD: "token_embd", MODEL_TENSOR.TOKEN_EMBD_NORM: "token_embd_norm", MODEL_TENSOR.TOKEN_TYPES: "token_types", MODEL_TENSOR.POS_EMBD: "position_embd", MODEL_TENSOR.OUTPUT_NORM: "output_norm", MODEL_TENSOR.OUTPUT: "output", MODEL_TENSOR.ROPE_FREQS: "rope_freqs", MODEL_TENSOR.ROPE_FACTORS_LONG: "rope_factors_long", MODEL_TENSOR.ROPE_FACTORS_SHORT: "rope_factors_short", MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm", MODEL_TENSOR.ATTN_NORM_2: "blk.{bid}.attn_norm_2", MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv", MODEL_TENSOR.ATTN_Q: "blk.{bid}.attn_q", MODEL_TENSOR.ATTN_K: "blk.{bid}.attn_k", MODEL_TENSOR.ATTN_V: "blk.{bid}.attn_v", MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output", MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd", MODEL_TENSOR.ATTN_Q_NORM: "blk.{bid}.attn_q_norm", MODEL_TENSOR.ATTN_K_NORM: "blk.{bid}.attn_k_norm", MODEL_TENSOR.ATTN_OUT_NORM: "blk.{bid}.attn_output_norm", MODEL_TENSOR.ATTN_POST_NORM: "blk.{bid}.post_attention_norm", MODEL_TENSOR.FFN_GATE_INP: "blk.{bid}.ffn_gate_inp", MODEL_TENSOR.FFN_GATE_INP_SHEXP: "blk.{bid}.ffn_gate_inp_shexp", MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm", MODEL_TENSOR.FFN_PRE_NORM: "blk.{bid}.ffn_norm", MODEL_TENSOR.FFN_POST_NORM: "blk.{bid}.post_ffw_norm", MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate", MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down", MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up", MODEL_TENSOR.FFN_GATE_SHEXP: "blk.{bid}.ffn_gate_shexp", MODEL_TENSOR.FFN_DOWN_SHEXP: "blk.{bid}.ffn_down_shexp", MODEL_TENSOR.FFN_UP_SHEXP: "blk.{bid}.ffn_up_shexp", MODEL_TENSOR.FFN_ACT: "blk.{bid}.ffn", MODEL_TENSOR.FFN_NORM_EXP: "blk.{bid}.ffn_norm_exps", MODEL_TENSOR.FFN_GATE_EXP: "blk.{bid}.ffn_gate_exps", MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down_exps", MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up_exps", MODEL_TENSOR.LAYER_OUT_NORM: "blk.{bid}.layer_output_norm", MODEL_TENSOR.SSM_IN: "blk.{bid}.ssm_in", MODEL_TENSOR.SSM_CONV1D: "blk.{bid}.ssm_conv1d", MODEL_TENSOR.SSM_X: "blk.{bid}.ssm_x", MODEL_TENSOR.SSM_DT: "blk.{bid}.ssm_dt", MODEL_TENSOR.SSM_A: "blk.{bid}.ssm_a", MODEL_TENSOR.SSM_D: "blk.{bid}.ssm_d", MODEL_TENSOR.SSM_OUT: "blk.{bid}.ssm_out", MODEL_TENSOR.TIME_MIX_W1: "blk.{bid}.time_mix_w1", MODEL_TENSOR.TIME_MIX_W2: "blk.{bid}.time_mix_w2", MODEL_TENSOR.TIME_MIX_LERP_X: "blk.{bid}.time_mix_lerp_x", MODEL_TENSOR.TIME_MIX_LERP_K: "blk.{bid}.time_mix_lerp_k", MODEL_TENSOR.TIME_MIX_LERP_V: "blk.{bid}.time_mix_lerp_v", MODEL_TENSOR.TIME_MIX_LERP_R: "blk.{bid}.time_mix_lerp_r", MODEL_TENSOR.TIME_MIX_LERP_G: "blk.{bid}.time_mix_lerp_g", MODEL_TENSOR.TIME_MIX_LERP_W: "blk.{bid}.time_mix_lerp_w", MODEL_TENSOR.TIME_MIX_FIRST: "blk.{bid}.time_mix_first", MODEL_TENSOR.TIME_MIX_DECAY: "blk.{bid}.time_mix_decay", MODEL_TENSOR.TIME_MIX_DECAY_W1: "blk.{bid}.time_mix_decay_w1", MODEL_TENSOR.TIME_MIX_DECAY_W2: "blk.{bid}.time_mix_decay_w2", MODEL_TENSOR.TIME_MIX_KEY: "blk.{bid}.time_mix_key", MODEL_TENSOR.TIME_MIX_VALUE: "blk.{bid}.time_mix_value", MODEL_TENSOR.TIME_MIX_RECEPTANCE: "blk.{bid}.time_mix_receptance", MODEL_TENSOR.TIME_MIX_GATE: "blk.{bid}.time_mix_gate", MODEL_TENSOR.TIME_MIX_LN: "blk.{bid}.time_mix_ln", MODEL_TENSOR.TIME_MIX_OUTPUT: "blk.{bid}.time_mix_output", MODEL_TENSOR.CHANNEL_MIX_LERP_K: "blk.{bid}.channel_mix_lerp_k", MODEL_TENSOR.CHANNEL_MIX_LERP_R: "blk.{bid}.channel_mix_lerp_r", MODEL_TENSOR.CHANNEL_MIX_KEY: "blk.{bid}.channel_mix_key", MODEL_TENSOR.CHANNEL_MIX_RECEPTANCE: "blk.{bid}.channel_mix_receptance", MODEL_TENSOR.CHANNEL_MIX_VALUE: "blk.{bid}.channel_mix_value", MODEL_TENSOR.ATTN_Q_A: "blk.{bid}.attn_q_a", MODEL_TENSOR.ATTN_Q_B: "blk.{bid}.attn_q_b", MODEL_TENSOR.ATTN_KV_A_MQA: "blk.{bid}.attn_kv_a_mqa", MODEL_TENSOR.ATTN_KV_B: "blk.{bid}.attn_kv_b", MODEL_TENSOR.ATTN_Q_A_NORM: "blk.{bid}.attn_q_a_norm", MODEL_TENSOR.ATTN_KV_A_NORM: "blk.{bid}.attn_kv_a_norm", MODEL_TENSOR.ATTN_SUB_NORM: "blk.{bid}.attn_sub_norm", MODEL_TENSOR.FFN_SUB_NORM: "blk.{bid}.ffn_sub_norm", MODEL_TENSOR.DEC_ATTN_NORM: "dec.blk.{bid}.attn_norm", MODEL_TENSOR.DEC_ATTN_Q: "dec.blk.{bid}.attn_q", MODEL_TENSOR.DEC_ATTN_K: "dec.blk.{bid}.attn_k", MODEL_TENSOR.DEC_ATTN_V: "dec.blk.{bid}.attn_v", MODEL_TENSOR.DEC_ATTN_OUT: "dec.blk.{bid}.attn_o", MODEL_TENSOR.DEC_ATTN_REL_B: "dec.blk.{bid}.attn_rel_b", MODEL_TENSOR.DEC_CROSS_ATTN_NORM: "dec.blk.{bid}.cross_attn_norm", MODEL_TENSOR.DEC_CROSS_ATTN_Q: "dec.blk.{bid}.cross_attn_q", MODEL_TENSOR.DEC_CROSS_ATTN_K: "dec.blk.{bid}.cross_attn_k", MODEL_TENSOR.DEC_CROSS_ATTN_V: "dec.blk.{bid}.cross_attn_v", MODEL_TENSOR.DEC_CROSS_ATTN_OUT: "dec.blk.{bid}.cross_attn_o", MODEL_TENSOR.DEC_CROSS_ATTN_REL_B: "dec.blk.{bid}.cross_attn_rel_b", MODEL_TENSOR.DEC_FFN_NORM: "dec.blk.{bid}.ffn_norm", MODEL_TENSOR.DEC_FFN_GATE: "dec.blk.{bid}.ffn_gate", MODEL_TENSOR.DEC_FFN_DOWN: "dec.blk.{bid}.ffn_down", MODEL_TENSOR.DEC_FFN_UP: "dec.blk.{bid}.ffn_up", MODEL_TENSOR.DEC_OUTPUT_NORM: "dec.output_norm", MODEL_TENSOR.ENC_ATTN_NORM: "enc.blk.{bid}.attn_norm", MODEL_TENSOR.ENC_ATTN_Q: "enc.blk.{bid}.attn_q", MODEL_TENSOR.ENC_ATTN_K: "enc.blk.{bid}.attn_k", MODEL_TENSOR.ENC_ATTN_V: "enc.blk.{bid}.attn_v", MODEL_TENSOR.ENC_ATTN_OUT: "enc.blk.{bid}.attn_o", MODEL_TENSOR.ENC_ATTN_REL_B: "enc.blk.{bid}.attn_rel_b", MODEL_TENSOR.ENC_FFN_NORM: "enc.blk.{bid}.ffn_norm", MODEL_TENSOR.ENC_FFN_GATE: "enc.blk.{bid}.ffn_gate", MODEL_TENSOR.ENC_FFN_DOWN: "enc.blk.{bid}.ffn_down", MODEL_TENSOR.ENC_FFN_UP: "enc.blk.{bid}.ffn_up", MODEL_TENSOR.ENC_OUTPUT_NORM: "enc.output_norm", MODEL_TENSOR.CLS: "cls", MODEL_TENSOR.CLS_OUT: "cls.output", } MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_ARCH.LLAMA: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.ATTN_ROT_EMBD, MODEL_TENSOR.FFN_GATE_INP, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, MODEL_TENSOR.FFN_GATE_EXP, MODEL_TENSOR.FFN_DOWN_EXP, MODEL_TENSOR.FFN_UP_EXP, ], MODEL_ARCH.GROK: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.ATTN_ROT_EMBD, MODEL_TENSOR.ATTN_OUT_NORM, MODEL_TENSOR.FFN_GATE_INP, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, MODEL_TENSOR.FFN_GATE_EXP, MODEL_TENSOR.FFN_DOWN_EXP, MODEL_TENSOR.FFN_UP_EXP, MODEL_TENSOR.LAYER_OUT_NORM, ], MODEL_ARCH.GPTNEOX: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_QKV, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.FALCON: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_NORM_2, MODEL_TENSOR.ATTN_QKV, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.BAICHUAN: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.ATTN_ROT_EMBD, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.STARCODER: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.POS_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_QKV, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.BERT: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.TOKEN_EMBD_NORM, MODEL_TENSOR.TOKEN_TYPES, MODEL_TENSOR.POS_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.ATTN_OUT_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, MODEL_TENSOR.LAYER_OUT_NORM, MODEL_TENSOR.CLS, MODEL_TENSOR.CLS_OUT, ], MODEL_ARCH.NOMIC_BERT: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.TOKEN_EMBD_NORM, MODEL_TENSOR.TOKEN_TYPES, MODEL_TENSOR.POS_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.ATTN_OUT_NORM, MODEL_TENSOR.ATTN_QKV, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, MODEL_TENSOR.LAYER_OUT_NORM, ], MODEL_ARCH.JINA_BERT_V2: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.TOKEN_EMBD_NORM, MODEL_TENSOR.TOKEN_TYPES, MODEL_TENSOR.ATTN_NORM_2, MODEL_TENSOR.ATTN_OUT_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_Q_NORM, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_K_NORM, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_UP, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.LAYER_OUT_NORM, MODEL_TENSOR.CLS, ], MODEL_ARCH.MPT: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_QKV, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, MODEL_TENSOR.FFN_ACT, MODEL_TENSOR.ATTN_Q_NORM, MODEL_TENSOR.ATTN_K_NORM, MODEL_TENSOR.POS_EMBD, ], MODEL_ARCH.GPTJ: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.REFACT: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.BLOOM: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.TOKEN_EMBD_NORM, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_QKV, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.STABLELM: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, MODEL_TENSOR.ATTN_Q_NORM, MODEL_TENSOR.ATTN_K_NORM, ], MODEL_ARCH.QWEN: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_QKV, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.ATTN_ROT_EMBD, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.QWEN2: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.QWEN2MOE: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_GATE_INP, MODEL_TENSOR.FFN_GATE_EXP, MODEL_TENSOR.FFN_DOWN_EXP, MODEL_TENSOR.FFN_UP_EXP, MODEL_TENSOR.FFN_GATE_INP_SHEXP, MODEL_TENSOR.FFN_GATE_SHEXP, MODEL_TENSOR.FFN_DOWN_SHEXP, MODEL_TENSOR.FFN_UP_SHEXP, ], MODEL_ARCH.PLAMO: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.ATTN_ROT_EMBD, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.GPT2: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.POS_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_QKV, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.PHI2: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_QKV, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.PHI3: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_QKV, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.CODESHELL: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.POS_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_QKV, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.ATTN_ROT_EMBD, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.ORION: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.ATTN_ROT_EMBD, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.INTERNLM2: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.ATTN_ROT_EMBD, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.MINICPM: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.ATTN_ROT_EMBD, MODEL_TENSOR.FFN_GATE_INP, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, MODEL_TENSOR.FFN_GATE_EXP, MODEL_TENSOR.FFN_DOWN_EXP, MODEL_TENSOR.FFN_UP_EXP, ], MODEL_ARCH.MINICPM3: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_Q_A, MODEL_TENSOR.ATTN_Q_B, MODEL_TENSOR.ATTN_KV_A_MQA, MODEL_TENSOR.ATTN_KV_B, MODEL_TENSOR.ATTN_Q_A_NORM, MODEL_TENSOR.ATTN_KV_A_NORM, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.GEMMA: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, MODEL_TENSOR.FFN_NORM, ], MODEL_ARCH.GEMMA2: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_POST_NORM, MODEL_TENSOR.FFN_PRE_NORM, MODEL_TENSOR.FFN_POST_NORM, ], MODEL_ARCH.STARCODER2: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.ATTN_ROT_EMBD, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.RWKV6: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.TOKEN_EMBD_NORM, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_NORM_2, MODEL_TENSOR.TIME_MIX_W1, MODEL_TENSOR.TIME_MIX_W2, MODEL_TENSOR.TIME_MIX_LERP_X, MODEL_TENSOR.TIME_MIX_LERP_K, MODEL_TENSOR.TIME_MIX_LERP_V, MODEL_TENSOR.TIME_MIX_LERP_R, MODEL_TENSOR.TIME_MIX_LERP_G, MODEL_TENSOR.TIME_MIX_LERP_W, MODEL_TENSOR.TIME_MIX_FIRST, MODEL_TENSOR.TIME_MIX_DECAY, MODEL_TENSOR.TIME_MIX_DECAY_W1, MODEL_TENSOR.TIME_MIX_DECAY_W2, MODEL_TENSOR.TIME_MIX_KEY, MODEL_TENSOR.TIME_MIX_VALUE, MODEL_TENSOR.TIME_MIX_RECEPTANCE, MODEL_TENSOR.TIME_MIX_GATE, MODEL_TENSOR.TIME_MIX_LN, MODEL_TENSOR.TIME_MIX_OUTPUT, MODEL_TENSOR.CHANNEL_MIX_LERP_K, MODEL_TENSOR.CHANNEL_MIX_LERP_R, MODEL_TENSOR.CHANNEL_MIX_KEY, MODEL_TENSOR.CHANNEL_MIX_RECEPTANCE, MODEL_TENSOR.CHANNEL_MIX_VALUE, ], MODEL_ARCH.MAMBA: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.SSM_IN, MODEL_TENSOR.SSM_CONV1D, MODEL_TENSOR.SSM_X, MODEL_TENSOR.SSM_DT, MODEL_TENSOR.SSM_A, MODEL_TENSOR.SSM_D, MODEL_TENSOR.SSM_OUT, ], MODEL_ARCH.XVERSE: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.ATTN_ROT_EMBD, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.COMMAND_R: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, MODEL_TENSOR.ATTN_K_NORM, MODEL_TENSOR.ATTN_Q_NORM, ], MODEL_ARCH.DBRX: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_QKV, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.ATTN_OUT_NORM, MODEL_TENSOR.FFN_GATE_INP, MODEL_TENSOR.FFN_GATE_EXP, MODEL_TENSOR.FFN_DOWN_EXP, MODEL_TENSOR.FFN_UP_EXP, ], MODEL_ARCH.OLMO: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.OLMOE: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_Q_NORM, MODEL_TENSOR.ATTN_K_NORM, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_GATE_INP, MODEL_TENSOR.FFN_GATE_EXP, MODEL_TENSOR.FFN_UP_EXP, MODEL_TENSOR.FFN_DOWN_EXP, ], MODEL_ARCH.OPENELM: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_QKV, MODEL_TENSOR.ATTN_Q_NORM, MODEL_TENSOR.ATTN_K_NORM, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.ARCTIC: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.ATTN_ROT_EMBD, MODEL_TENSOR.FFN_GATE_INP, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, MODEL_TENSOR.FFN_NORM_EXP, MODEL_TENSOR.FFN_GATE_EXP, MODEL_TENSOR.FFN_DOWN_EXP, MODEL_TENSOR.FFN_UP_EXP, ], MODEL_ARCH.DEEPSEEK2: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_Q_A, MODEL_TENSOR.ATTN_Q_B, MODEL_TENSOR.ATTN_KV_A_MQA, MODEL_TENSOR.ATTN_KV_B, MODEL_TENSOR.ATTN_Q_A_NORM, MODEL_TENSOR.ATTN_KV_A_NORM, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.ATTN_ROT_EMBD, MODEL_TENSOR.FFN_GATE_INP, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, MODEL_TENSOR.FFN_GATE_EXP, MODEL_TENSOR.FFN_DOWN_EXP, MODEL_TENSOR.FFN_UP_EXP, MODEL_TENSOR.FFN_GATE_SHEXP, MODEL_TENSOR.FFN_DOWN_SHEXP, MODEL_TENSOR.FFN_UP_SHEXP, ], MODEL_ARCH.CHATGLM : [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_QKV, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.BITNET: [ MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, MODEL_TENSOR.ATTN_SUB_NORM, MODEL_TENSOR.FFN_SUB_NORM, ], MODEL_ARCH.T5: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.DEC_ATTN_NORM, MODEL_TENSOR.DEC_ATTN_Q, MODEL_TENSOR.DEC_ATTN_K, MODEL_TENSOR.DEC_ATTN_V, MODEL_TENSOR.DEC_ATTN_OUT, MODEL_TENSOR.DEC_ATTN_REL_B, MODEL_TENSOR.DEC_CROSS_ATTN_NORM, MODEL_TENSOR.DEC_CROSS_ATTN_Q, MODEL_TENSOR.DEC_CROSS_ATTN_K, MODEL_TENSOR.DEC_CROSS_ATTN_V, MODEL_TENSOR.DEC_CROSS_ATTN_OUT, MODEL_TENSOR.DEC_CROSS_ATTN_REL_B, MODEL_TENSOR.DEC_FFN_NORM, MODEL_TENSOR.DEC_FFN_GATE, MODEL_TENSOR.DEC_FFN_DOWN, MODEL_TENSOR.DEC_FFN_UP, MODEL_TENSOR.DEC_OUTPUT_NORM, MODEL_TENSOR.ENC_ATTN_NORM, MODEL_TENSOR.ENC_ATTN_Q, MODEL_TENSOR.ENC_ATTN_K, MODEL_TENSOR.ENC_ATTN_V, MODEL_TENSOR.ENC_ATTN_OUT, MODEL_TENSOR.ENC_ATTN_REL_B, MODEL_TENSOR.ENC_FFN_NORM, MODEL_TENSOR.ENC_FFN_GATE, MODEL_TENSOR.ENC_FFN_DOWN, MODEL_TENSOR.ENC_FFN_UP, MODEL_TENSOR.ENC_OUTPUT_NORM, ], MODEL_ARCH.T5ENCODER: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ENC_ATTN_NORM, MODEL_TENSOR.ENC_ATTN_Q, MODEL_TENSOR.ENC_ATTN_K, MODEL_TENSOR.ENC_ATTN_V, MODEL_TENSOR.ENC_ATTN_OUT, MODEL_TENSOR.ENC_ATTN_REL_B, MODEL_TENSOR.ENC_FFN_NORM, MODEL_TENSOR.ENC_FFN_GATE, MODEL_TENSOR.ENC_FFN_DOWN, MODEL_TENSOR.ENC_FFN_UP, MODEL_TENSOR.ENC_OUTPUT_NORM, ], MODEL_ARCH.JAIS: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_QKV, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.NEMOTRON: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.ATTN_ROT_EMBD, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.EXAONE: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.ATTN_ROT_EMBD, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.GRANITE: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.GRANITE_MOE: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_GATE_INP, MODEL_TENSOR.FFN_GATE_EXP, MODEL_TENSOR.FFN_DOWN_EXP, MODEL_TENSOR.FFN_UP_EXP, ], MODEL_ARCH.CHAMELEON: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_Q_NORM, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_K_NORM, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], # TODO } # tensors that will not be serialized MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_ARCH.LLAMA: [ MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, ], MODEL_ARCH.BAICHUAN: [ MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, ], MODEL_ARCH.QWEN: [ MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, ], MODEL_ARCH.CODESHELL: [ MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, ], MODEL_ARCH.ORION: [ MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, ], MODEL_ARCH.STARCODER2: [ MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, ], MODEL_ARCH.XVERSE: [ MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, ], MODEL_ARCH.DEEPSEEK2: [ MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, ], MODEL_ARCH.CHATGLM: [ MODEL_TENSOR.ROPE_FREQS, ], MODEL_ARCH.NEMOTRON: [ MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, ], } # # types # class TokenType(IntEnum): NORMAL = 1 UNKNOWN = 2 CONTROL = 3 USER_DEFINED = 4 UNUSED = 5 BYTE = 6 class RopeScalingType(Enum): NONE = 'none' LINEAR = 'linear' YARN = 'yarn' class PoolingType(IntEnum): NONE = 0 MEAN = 1 CLS = 2 class GGMLQuantizationType(IntEnum): F32 = 0 F16 = 1 Q4_0 = 2 Q4_1 = 3 Q5_0 = 6 Q5_1 = 7 Q8_0 = 8 Q8_1 = 9 Q2_K = 10 Q3_K = 11 Q4_K = 12 Q5_K = 13 Q6_K = 14 Q8_K = 15 IQ2_XXS = 16 IQ2_XS = 17 IQ3_XXS = 18 IQ1_S = 19 IQ4_NL = 20 IQ3_S = 21 IQ2_S = 22 IQ4_XS = 23 I8 = 24 I16 = 25 I32 = 26 I64 = 27 F64 = 28 IQ1_M = 29 BF16 = 30 Q4_0_4_4 = 31 Q4_0_4_8 = 32 Q4_0_8_8 = 33 TQ1_0 = 34 TQ2_0 = 35 # TODO: add GGMLFileType from ggml_ftype in ggml.h # from llama_ftype in llama.h # ALL VALUES SHOULD BE THE SAME HERE AS THEY ARE OVER THERE. class LlamaFileType(IntEnum): ALL_F32 = 0 MOSTLY_F16 = 1 # except 1d tensors MOSTLY_Q4_0 = 2 # except 1d tensors MOSTLY_Q4_1 = 3 # except 1d tensors # MOSTLY_Q4_1_SOME_F16 = 4 # tok_embeddings.weight and output.weight are F16 # MOSTLY_Q4_2 = 5 # support has been removed # MOSTLY_Q4_3 = 6 # support has been removed MOSTLY_Q8_0 = 7 # except 1d tensors MOSTLY_Q5_0 = 8 # except 1d tensors MOSTLY_Q5_1 = 9 # except 1d tensors MOSTLY_Q2_K = 10 # except 1d tensors MOSTLY_Q3_K_S = 11 # except 1d tensors MOSTLY_Q3_K_M = 12 # except 1d tensors MOSTLY_Q3_K_L = 13 # except 1d tensors MOSTLY_Q4_K_S = 14 # except 1d tensors MOSTLY_Q4_K_M = 15 # except 1d tensors MOSTLY_Q5_K_S = 16 # except 1d tensors MOSTLY_Q5_K_M = 17 # except 1d tensors MOSTLY_Q6_K = 18 # except 1d tensors MOSTLY_IQ2_XXS = 19 # except 1d tensors MOSTLY_IQ2_XS = 20 # except 1d tensors MOSTLY_Q2_K_S = 21 # except 1d tensors MOSTLY_IQ3_XS = 22 # except 1d tensors MOSTLY_IQ3_XXS = 23 # except 1d tensors MOSTLY_IQ1_S = 24 # except 1d tensors MOSTLY_IQ4_NL = 25 # except 1d tensors MOSTLY_IQ3_S = 26 # except 1d tensors MOSTLY_IQ3_M = 27 # except 1d tensors MOSTLY_IQ2_S = 28 # except 1d tensors MOSTLY_IQ2_M = 29 # except 1d tensors MOSTLY_IQ4_XS = 30 # except 1d tensors MOSTLY_IQ1_M = 31 # except 1d tensors MOSTLY_BF16 = 32 # except 1d tensors MOSTLY_Q4_0_4_4 = 33 # except 1d tensors MOSTLY_Q4_0_4_8 = 34 # except 1d tensors MOSTLY_Q4_0_8_8 = 35 # except 1d tensors MOSTLY_TQ1_0 = 36 # except 1d tensors MOSTLY_TQ2_0 = 37 # except 1d tensors GUESSED = 1024 # not specified in the model file class GGUFEndian(IntEnum): LITTLE = 0 BIG = 1 class GGUFValueType(IntEnum): UINT8 = 0 INT8 = 1 UINT16 = 2 INT16 = 3 UINT32 = 4 INT32 = 5 FLOAT32 = 6 BOOL = 7 STRING = 8 ARRAY = 9 UINT64 = 10 INT64 = 11 FLOAT64 = 12 @staticmethod def get_type(val: Any) -> GGUFValueType: if isinstance(val, (str, bytes, bytearray)): return GGUFValueType.STRING elif isinstance(val, list): return GGUFValueType.ARRAY elif isinstance(val, float): return GGUFValueType.FLOAT32 elif isinstance(val, bool): return GGUFValueType.BOOL elif isinstance(val, int): return GGUFValueType.INT32 # TODO: need help with 64-bit types in Python else: raise ValueError(f"Unknown type: {type(val)}") # Items here are (block size, type size) QK_K = 256 GGML_QUANT_SIZES: dict[GGMLQuantizationType, tuple[int, int]] = { GGMLQuantizationType.F32: (1, 4), GGMLQuantizationType.F16: (1, 2), GGMLQuantizationType.Q4_0: (32, 2 + 16), GGMLQuantizationType.Q4_1: (32, 2 + 2 + 16), GGMLQuantizationType.Q5_0: (32, 2 + 4 + 16), GGMLQuantizationType.Q5_1: (32, 2 + 2 + 4 + 16), GGMLQuantizationType.Q8_0: (32, 2 + 32), GGMLQuantizationType.Q8_1: (32, 4 + 4 + 32), GGMLQuantizationType.Q2_K: (256, 2 + 2 + QK_K // 16 + QK_K // 4), GGMLQuantizationType.Q3_K: (256, 2 + QK_K // 4 + QK_K // 8 + 12), GGMLQuantizationType.Q4_K: (256, 2 + 2 + QK_K // 2 + 12), GGMLQuantizationType.Q5_K: (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12), GGMLQuantizationType.Q6_K: (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16), GGMLQuantizationType.Q8_K: (256, 4 + QK_K + QK_K // 8), GGMLQuantizationType.IQ2_XXS: (256, 2 + QK_K // 4), GGMLQuantizationType.IQ2_XS: (256, 2 + QK_K // 4 + QK_K // 32), GGMLQuantizationType.IQ3_XXS: (256, 2 + QK_K // 4 + QK_K // 8), GGMLQuantizationType.IQ1_S: (256, 2 + QK_K // 8 + QK_K // 16), GGMLQuantizationType.IQ4_NL: (32, 2 + 16), GGMLQuantizationType.IQ3_S: (256, 2 + QK_K // 4 + QK_K // 8 + QK_K // 32 + 4), GGMLQuantizationType.IQ2_S: (256, 2 + QK_K // 4 + QK_K // 16), GGMLQuantizationType.IQ4_XS: (256, 2 + 2 + QK_K // 2 + QK_K // 64), GGMLQuantizationType.I8: (1, 1), GGMLQuantizationType.I16: (1, 2), GGMLQuantizationType.I32: (1, 4), GGMLQuantizationType.I64: (1, 8), GGMLQuantizationType.F64: (1, 8), GGMLQuantizationType.IQ1_M: (256, QK_K // 8 + QK_K // 16 + QK_K // 32), GGMLQuantizationType.BF16: (1, 2), GGMLQuantizationType.Q4_0_4_4:(32, 2 + 16), GGMLQuantizationType.Q4_0_4_8:(32, 2 + 16), GGMLQuantizationType.Q4_0_8_8:(32, 2 + 16), GGMLQuantizationType.TQ1_0: (256, 2 + 4 * 13), GGMLQuantizationType.TQ2_0: (256, 2 + 64), } # Aliases for backward compatibility. # general KEY_GENERAL_ARCHITECTURE = Keys.General.ARCHITECTURE KEY_GENERAL_QUANTIZATION_VERSION = Keys.General.QUANTIZATION_VERSION KEY_GENERAL_ALIGNMENT = Keys.General.ALIGNMENT KEY_GENERAL_NAME = Keys.General.NAME KEY_GENERAL_AUTHOR = Keys.General.AUTHOR KEY_GENERAL_URL = Keys.General.URL KEY_GENERAL_DESCRIPTION = Keys.General.DESCRIPTION KEY_GENERAL_LICENSE = Keys.General.LICENSE KEY_GENERAL_SOURCE_URL = Keys.General.SOURCE_URL KEY_GENERAL_FILE_TYPE = Keys.General.FILE_TYPE # LLM KEY_VOCAB_SIZE = Keys.LLM.VOCAB_SIZE KEY_CONTEXT_LENGTH = Keys.LLM.CONTEXT_LENGTH KEY_EMBEDDING_LENGTH = Keys.LLM.EMBEDDING_LENGTH KEY_BLOCK_COUNT = Keys.LLM.BLOCK_COUNT KEY_FEED_FORWARD_LENGTH = Keys.LLM.FEED_FORWARD_LENGTH KEY_USE_PARALLEL_RESIDUAL = Keys.LLM.USE_PARALLEL_RESIDUAL KEY_TENSOR_DATA_LAYOUT = Keys.LLM.TENSOR_DATA_LAYOUT # attention KEY_ATTENTION_HEAD_COUNT = Keys.Attention.HEAD_COUNT KEY_ATTENTION_HEAD_COUNT_KV = Keys.Attention.HEAD_COUNT_KV KEY_ATTENTION_MAX_ALIBI_BIAS = Keys.Attention.MAX_ALIBI_BIAS KEY_ATTENTION_CLAMP_KQV = Keys.Attention.CLAMP_KQV KEY_ATTENTION_LAYERNORM_EPS = Keys.Attention.LAYERNORM_EPS KEY_ATTENTION_LAYERNORM_RMS_EPS = Keys.Attention.LAYERNORM_RMS_EPS # RoPE KEY_ROPE_DIMENSION_COUNT = Keys.Rope.DIMENSION_COUNT KEY_ROPE_FREQ_BASE = Keys.Rope.FREQ_BASE KEY_ROPE_SCALING_TYPE = Keys.Rope.SCALING_TYPE KEY_ROPE_SCALING_FACTOR = Keys.Rope.SCALING_FACTOR KEY_ROPE_SCALING_ORIG_CTX_LEN = Keys.Rope.SCALING_ORIG_CTX_LEN KEY_ROPE_SCALING_FINETUNED = Keys.Rope.SCALING_FINETUNED # SSM KEY_SSM_CONV_KERNEL = Keys.SSM.CONV_KERNEL KEY_SSM_INNER_SIZE = Keys.SSM.INNER_SIZE KEY_SSM_STATE_SIZE = Keys.SSM.STATE_SIZE KEY_SSM_TIME_STEP_RANK = Keys.SSM.TIME_STEP_RANK KEY_SSM_DT_B_C_RMS = Keys.SSM.DT_B_C_RMS # tokenization KEY_TOKENIZER_MODEL = Keys.Tokenizer.MODEL KEY_TOKENIZER_PRE = Keys.Tokenizer.PRE KEY_TOKENIZER_LIST = Keys.Tokenizer.LIST KEY_TOKENIZER_TOKEN_TYPE = Keys.Tokenizer.TOKEN_TYPE KEY_TOKENIZER_SCORES = Keys.Tokenizer.SCORES KEY_TOKENIZER_MERGES = Keys.Tokenizer.MERGES KEY_TOKENIZER_BOS_ID = Keys.Tokenizer.BOS_ID KEY_TOKENIZER_EOS_ID = Keys.Tokenizer.EOS_ID KEY_TOKENIZER_UNK_ID = Keys.Tokenizer.UNK_ID KEY_TOKENIZER_SEP_ID = Keys.Tokenizer.SEP_ID KEY_TOKENIZER_PAD_ID = Keys.Tokenizer.PAD_ID KEY_TOKENIZER_CLS_ID = Keys.Tokenizer.CLS_ID KEY_TOKENIZER_MASK_ID = Keys.Tokenizer.MASK_ID KEY_TOKENIZER_HF_JSON = Keys.Tokenizer.HF_JSON KEY_TOKENIZER_RWKV = Keys.Tokenizer.RWKV KEY_TOKENIZER_PRIFIX_ID = Keys.Tokenizer.PREFIX_ID KEY_TOKENIZER_SUFFIX_ID = Keys.Tokenizer.SUFFIX_ID KEY_TOKENIZER_MIDDLE_ID = Keys.Tokenizer.MIDDLE_ID KEY_TOKENIZER_EOT_ID = Keys.Tokenizer.EOT_ID KEY_TOKENIZER_EOM_ID = Keys.Tokenizer.EOM_ID