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constants.py
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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_DESCRIPTION = "general.base_model.{id}.description"
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...)
# Dataset Source
DATASET_COUNT = "general.dataset.count"
DATASET_NAME = "general.dataset.{id}.name"
DATASET_AUTHOR = "general.dataset.{id}.author"
DATASET_VERSION = "general.dataset.{id}.version"
DATASET_ORGANIZATION = "general.dataset.{id}.organization"
DATASET_DESCRIPTION = "general.dataset.{id}.description"
DATASET_URL = "general.dataset.{id}.url" # Model Website/Paper
DATASET_DOI = "general.dataset.{id}.doi"
DATASET_UUID = "general.dataset.{id}.uuid"
DATASET_REPO_URL = "general.dataset.{id}.repo_url" # Model Source Repository (git/svn/etc...)
# Array based KV stores
TAGS = "general.tags"
LANGUAGES = "general.languages"
class LLM:
VOCAB_SIZE = "{arch}.vocab_size"
CONTEXT_LENGTH = "{arch}.context_length"
EMBEDDING_LENGTH = "{arch}.embedding_length"
FEATURES_LENGTH = "{arch}.features_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"
EXPERT_WEIGHTS_NORM = "{arch}.expert_weights_norm"
EXPERT_GATING_FUNC = "{arch}.expert_gating_func"
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"
TOKEN_SHIFT_COUNT = "{arch}.token_shift_count"
INTERLEAVE_MOE_LAYER_STEP = "{arch}.interleave_moe_layer_step"
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"
GROUPNORM_EPS = "{arch}.attention.group_norm_epsilon"
GROUPNORM_GROUPS = "{arch}.attention.group_norm_groups"
CAUSAL = "{arch}.attention.causal"
Q_LORA_RANK = "{arch}.attention.q_lora_rank"
KV_LORA_RANK = "{arch}.attention.kv_lora_rank"
DECAY_LORA_RANK = "{arch}.attention.decay_lora_rank"
ICLR_LORA_RANK = "{arch}.attention.iclr_lora_rank"
VALUE_RESIDUAL_MIX_LORA_RANK = "{arch}.attention.value_residual_mix_lora_rank"
GATE_LORA_RANK = "{arch}.attention.gate_lora_rank"
REL_BUCKETS_COUNT = "{arch}.attention.relative_buckets_count"
SLIDING_WINDOW = "{arch}.attention.sliding_window"
SCALE = "{arch}.attention.scale"
KEY_LENGTH_MLA = "{arch}.attention.key_length_mla"
VALUE_LENGTH_MLA = "{arch}.attention.value_length_mla"
class Rope:
DIMENSION_COUNT = "{arch}.rope.dimension_count"
DIMENSION_SECTIONS = "{arch}.rope.dimension_sections"
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 PosNet:
EMBEDDING_LENGTH = "{arch}.posnet.embedding_length"
BLOCK_COUNT = "{arch}.posnet.block_count"
class ConvNext:
EMBEDDING_LENGTH = "{arch}.convnext.embedding_length"
BLOCK_COUNT = "{arch}.convnext.block_count"
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"
EOT_ID = "tokenizer.ggml.eot_token_id"
EOM_ID = "tokenizer.ggml.eom_token_id"
UNK_ID = "tokenizer.ggml.unknown_token_id"
SEP_ID = "tokenizer.ggml.seperator_token_id"
PAD_ID = "tokenizer.ggml.padding_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
FIM_PRE_ID = "tokenizer.ggml.fim_pre_token_id"
FIM_SUF_ID = "tokenizer.ggml.fim_suf_token_id"
FIM_MID_ID = "tokenizer.ggml.fim_mid_token_id"
FIM_PAD_ID = "tokenizer.ggml.fim_pad_token_id"
FIM_REP_ID = "tokenizer.ggml.fim_rep_token_id"
FIM_SEP_ID = "tokenizer.ggml.fim_sep_token_id"
# deprecated:
PREFIX_ID = "tokenizer.ggml.prefix_token_id"
SUFFIX_ID = "tokenizer.ggml.suffix_token_id"
MIDDLE_ID = "tokenizer.ggml.middle_token_id"
class Adapter:
TYPE = "adapter.type"
LORA_ALPHA = "adapter.lora.alpha"
class ClipVision:
PROJECTOR_TYPE = "clip.projector_type"
HAS_VISION_ENCODER = "clip.has_vision_encoder"
HAS_LLAVA_PROJECTOR = "clip.has_llava_projector"
IMAGE_SIZE = "clip.vision.image_size"
PATCH_SIZE = "clip.vision.patch_size"
EMBEDDING_LENGTH = "clip.vision.embedding_length"
FEED_FORWARD_LENGTH = "clip.vision.feed_forward_length"
PROJECTION_DIM = "clip.vision.projection_dim"
BLOCK_COUNT = "clip.vision.block_count"
IMAGE_MEAN = "clip.vision.image_mean"
IMAGE_STD = "clip.vision.image_std"
USE_GELU = "clip.use_gelu"
class Attention:
HEAD_COUNT = "clip.vision.attention.head_count"
LAYERNORM_EPS = "clip.vision.attention.layer_norm_epsilon"
#
# recommended mapping of model tensor names for storage in gguf
#
class GGUFType:
MODEL = "model"
ADAPTER = "adapter"
CLIP_VISION = "clip-vision"
class MODEL_ARCH(IntEnum):
CLIP_VISION = auto() # dummy arch for clip.cpp
LLAMA = auto()
LLAMA4 = auto()
DECI = 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()
QWEN2VL = auto()
QWEN3 = auto()
QWEN3MOE = auto()
PHI2 = auto()
PHI3 = auto()
PHIMOE = auto()
PLAMO = auto()
CODESHELL = auto()
ORION = auto()
INTERNLM2 = auto()
MINICPM = auto()
MINICPM3 = auto()
GEMMA = auto()
GEMMA2 = auto()
GEMMA3 = auto()
STARCODER2 = auto()
RWKV6 = auto()
RWKV6QWEN2 = auto()
RWKV7 = auto()
ARWKV7 = auto()
MAMBA = auto()
XVERSE = auto()
COMMAND_R = auto()
COHERE2 = auto()
DBRX = auto()
OLMO = auto()
OLMO2 = auto()
OLMOE = auto()
OPENELM = auto()
ARCTIC = auto()
DEEPSEEK = auto()
DEEPSEEK2 = auto()
CHATGLM = auto()
GLM4 = auto()
BITNET = auto()
T5 = auto()
T5ENCODER = auto()
JAIS = auto()
NEMOTRON = auto()
EXAONE = auto()
GRANITE = auto()
GRANITE_MOE = auto()
CHAMELEON = auto()
WAVTOKENIZER_DEC = auto()
PLM = auto()
BAILINGMOE = auto()
class VISION_PROJECTOR_TYPE(IntEnum):
MLP = auto()
LDP = auto()
LDPV2 = auto()
RESAMPLER = auto()
GLM_EDGE = auto()
MERGER = auto()
GEMMA3 = 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()
FFN_EXP_PROBS_B = 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_W0 = auto()
TIME_MIX_W1 = auto()
TIME_MIX_W2 = auto()
TIME_MIX_A0 = auto()
TIME_MIX_A1 = auto()
TIME_MIX_A2 = auto()
TIME_MIX_V0 = auto()
TIME_MIX_V1 = auto()
TIME_MIX_V2 = auto()
TIME_MIX_G1 = auto()
TIME_MIX_G2 = auto()
TIME_MIX_K_K = auto()
TIME_MIX_K_A = auto()
TIME_MIX_R_K = 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_FUSED = 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_K_B = auto()
ATTN_V_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
CONV1D = auto()
CONVNEXT_DW = auto()
CONVNEXT_NORM = auto()
CONVNEXT_PW1 = auto()
CONVNEXT_PW2 = auto()
CONVNEXT_GAMMA = auto()
POSNET_CONV1 = auto()
POSNET_CONV2 = auto()
POSNET_NORM = auto()
POSNET_NORM1 = auto()
POSNET_NORM2 = auto()
POSNET_ATTN_NORM = auto()
POSNET_ATTN_Q = auto()
POSNET_ATTN_K = auto()
POSNET_ATTN_V = auto()
POSNET_ATTN_OUT = auto()
# vision
V_MMPROJ = auto()
V_MMPROJ_FC = auto()
V_MMPROJ_MLP = auto()
V_MMPROJ_PEG = auto()
V_ENC_EMBD_CLS = auto()
V_ENC_EMBD_PATCH = auto()
V_ENC_EMBD_POS = auto()
V_ENC_ATTN_Q = auto()
V_ENC_ATTN_K = auto()
V_ENC_ATTN_V = auto()
V_ENC_INPUT_NORM = auto()
V_ENC_OUTPUT = auto()
V_ENC_OUTPUT_NORM = auto()
V_ENC_FFN_UP = auto()
V_ENC_FFN_DOWN = auto()
V_PRE_NORM = auto()
V_POST_NORM = auto()
V_MM_INP_PROJ = auto() # gemma3
V_MM_SOFT_EMB_NORM = auto() # gemma3
V_RESMPL_POS_EMBD_K = auto() # minicpmv
V_RESMPL_ATTN_Q = auto() # minicpmv
V_RESMPL_ATTN_K = auto() # minicpmv
V_RESMPL_ATTN_V = auto() # minicpmv
V_RESMPL_ATTN_OUT = auto() # minicpmv
V_RESMPL_KV = auto() # minicpmv
V_RESMPL_KV_NORM = auto() # minicpmv
V_RESMPL_POST_NORM = auto() # minicpmv
V_RESMPL_Q_NORM = auto() # minicpmv
V_RESMPL_PROJ = auto() # minicpmv
V_RESMPL_QUERY = auto() # minicpmv
MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.CLIP_VISION: "clip", # dummy arch for clip.cpp
MODEL_ARCH.LLAMA: "llama",
MODEL_ARCH.LLAMA4: "llama4",
MODEL_ARCH.DECI: "deci",
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.QWEN2VL: "qwen2vl",
MODEL_ARCH.QWEN3: "qwen3",
MODEL_ARCH.QWEN3MOE: "qwen3moe",
MODEL_ARCH.PHI2: "phi2",
MODEL_ARCH.PHI3: "phi3",
MODEL_ARCH.PHIMOE: "phimoe",
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.GEMMA3: "gemma3",
MODEL_ARCH.STARCODER2: "starcoder2",
MODEL_ARCH.RWKV6: "rwkv6",
MODEL_ARCH.RWKV6QWEN2: "rwkv6qwen2",
MODEL_ARCH.RWKV7: "rwkv7",
MODEL_ARCH.ARWKV7: "arwkv7",
MODEL_ARCH.MAMBA: "mamba",
MODEL_ARCH.XVERSE: "xverse",
MODEL_ARCH.COMMAND_R: "command-r",
MODEL_ARCH.COHERE2: "cohere2",
MODEL_ARCH.DBRX: "dbrx",
MODEL_ARCH.OLMO: "olmo",
MODEL_ARCH.OLMO2: "olmo2",
MODEL_ARCH.OLMOE: "olmoe",
MODEL_ARCH.OPENELM: "openelm",
MODEL_ARCH.ARCTIC: "arctic",
MODEL_ARCH.DEEPSEEK: "deepseek",
MODEL_ARCH.DEEPSEEK2: "deepseek2",
MODEL_ARCH.CHATGLM: "chatglm",
MODEL_ARCH.GLM4: "glm4",
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",
MODEL_ARCH.WAVTOKENIZER_DEC: "wavtokenizer-dec",
MODEL_ARCH.PLM: "plm",
MODEL_ARCH.BAILINGMOE: "bailingmoe",
}
VISION_PROJECTOR_TYPE_NAMES: dict[VISION_PROJECTOR_TYPE, str] = {
VISION_PROJECTOR_TYPE.MLP: "mlp",
VISION_PROJECTOR_TYPE.LDP: "ldp",
VISION_PROJECTOR_TYPE.LDPV2: "ldpv2",
VISION_PROJECTOR_TYPE.RESAMPLER: "resampler",
VISION_PROJECTOR_TYPE.GLM_EDGE: "adapter",
VISION_PROJECTOR_TYPE.MERGER: "qwen2vl_merger",
VISION_PROJECTOR_TYPE.GEMMA3: "gemma3",
}
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.FFN_EXP_PROBS_B: "blk.{bid}.exp_probs_b",
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_W0: "blk.{bid}.time_mix_w0",
MODEL_TENSOR.TIME_MIX_W1: "blk.{bid}.time_mix_w1",
MODEL_TENSOR.TIME_MIX_W2: "blk.{bid}.time_mix_w2",
MODEL_TENSOR.TIME_MIX_A0: "blk.{bid}.time_mix_a0",
MODEL_TENSOR.TIME_MIX_A1: "blk.{bid}.time_mix_a1",
MODEL_TENSOR.TIME_MIX_A2: "blk.{bid}.time_mix_a2",
MODEL_TENSOR.TIME_MIX_V0: "blk.{bid}.time_mix_v0",
MODEL_TENSOR.TIME_MIX_V1: "blk.{bid}.time_mix_v1",
MODEL_TENSOR.TIME_MIX_V2: "blk.{bid}.time_mix_v2",
MODEL_TENSOR.TIME_MIX_G1: "blk.{bid}.time_mix_g1",
MODEL_TENSOR.TIME_MIX_G2: "blk.{bid}.time_mix_g2",
MODEL_TENSOR.TIME_MIX_K_K: "blk.{bid}.time_mix_k_k",
MODEL_TENSOR.TIME_MIX_K_A: "blk.{bid}.time_mix_k_a",
MODEL_TENSOR.TIME_MIX_R_K: "blk.{bid}.time_mix_r_k",
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_FUSED: "blk.{bid}.time_mix_lerp_fused",
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_K_B: "blk.{bid}.attn_k_b",
MODEL_TENSOR.ATTN_V_B: "blk.{bid}.attn_v_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_TENSOR.CONV1D: "conv1d",
MODEL_TENSOR.CONVNEXT_DW: "convnext.{bid}.dw",
MODEL_TENSOR.CONVNEXT_NORM: "convnext.{bid}.norm",
MODEL_TENSOR.CONVNEXT_PW1: "convnext.{bid}.pw1",
MODEL_TENSOR.CONVNEXT_PW2: "convnext.{bid}.pw2",
MODEL_TENSOR.CONVNEXT_GAMMA: "convnext.{bid}.gamma",
MODEL_TENSOR.POSNET_CONV1: "posnet.{bid}.conv1",
MODEL_TENSOR.POSNET_CONV2: "posnet.{bid}.conv2",
MODEL_TENSOR.POSNET_NORM: "posnet.{bid}.norm",
MODEL_TENSOR.POSNET_NORM1: "posnet.{bid}.norm1",
MODEL_TENSOR.POSNET_NORM2: "posnet.{bid}.norm2",
MODEL_TENSOR.POSNET_ATTN_NORM: "posnet.{bid}.attn_norm",
MODEL_TENSOR.POSNET_ATTN_Q: "posnet.{bid}.attn_q",
MODEL_TENSOR.POSNET_ATTN_K: "posnet.{bid}.attn_k",
MODEL_TENSOR.POSNET_ATTN_V: "posnet.{bid}.attn_v",
MODEL_TENSOR.POSNET_ATTN_OUT: "posnet.{bid}.attn_output",
# vision
MODEL_TENSOR.V_MMPROJ: "mm.{bid}",
MODEL_TENSOR.V_MMPROJ_FC: "mm.model.fc",
MODEL_TENSOR.V_MMPROJ_MLP: "mm.model.mlp.{bid}",
MODEL_TENSOR.V_MMPROJ_PEG: "mm.model.peg.{bid}",
MODEL_TENSOR.V_ENC_EMBD_CLS: "v.class_embd",
MODEL_TENSOR.V_ENC_EMBD_PATCH: "v.patch_embd",
MODEL_TENSOR.V_ENC_EMBD_POS: "v.position_embd",
MODEL_TENSOR.V_ENC_ATTN_Q: "v.blk.{bid}.attn_q",
MODEL_TENSOR.V_ENC_ATTN_K: "v.blk.{bid}.attn_k",
MODEL_TENSOR.V_ENC_ATTN_V: "v.blk.{bid}.attn_v",
MODEL_TENSOR.V_ENC_INPUT_NORM: "v.blk.{bid}.ln1",
MODEL_TENSOR.V_ENC_OUTPUT: "v.blk.{bid}.attn_out",
MODEL_TENSOR.V_ENC_OUTPUT_NORM: "v.blk.{bid}.ln2",
MODEL_TENSOR.V_ENC_FFN_UP: "v.blk.{bid}.ffn_up",
MODEL_TENSOR.V_ENC_FFN_DOWN: "v.blk.{bid}.ffn_down",
MODEL_TENSOR.V_PRE_NORM: "v.pre_ln",
MODEL_TENSOR.V_POST_NORM: "v.post_ln",
MODEL_TENSOR.V_MM_INP_PROJ: "mm.input_projection",
MODEL_TENSOR.V_MM_SOFT_EMB_NORM: "mm.soft_emb_norm",
MODEL_TENSOR.V_RESMPL_POS_EMBD_K: "resampler.pos_embd_k",
MODEL_TENSOR.V_RESMPL_ATTN_Q: "resampler.attn.q",
MODEL_TENSOR.V_RESMPL_ATTN_K: "resampler.attn.k",
MODEL_TENSOR.V_RESMPL_ATTN_V: "resampler.attn.v",
MODEL_TENSOR.V_RESMPL_ATTN_OUT: "resampler.attn.out",
MODEL_TENSOR.V_RESMPL_KV: "resampler.kv",
MODEL_TENSOR.V_RESMPL_KV_NORM: "resampler.ln_kv",
MODEL_TENSOR.V_RESMPL_POST_NORM: "resampler.ln_post",
MODEL_TENSOR.V_RESMPL_Q_NORM: "resampler.ln_q",
MODEL_TENSOR.V_RESMPL_PROJ: "resampler.proj",
MODEL_TENSOR.V_RESMPL_QUERY: "resampler.query",
}
MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_ARCH.CLIP_VISION: [
MODEL_TENSOR.V_MMPROJ,
MODEL_TENSOR.V_MMPROJ_FC,
MODEL_TENSOR.V_MMPROJ_MLP,
MODEL_TENSOR.V_MMPROJ_PEG,
MODEL_TENSOR.V_ENC_EMBD_CLS,
MODEL_TENSOR.V_ENC_EMBD_PATCH,
MODEL_TENSOR.V_ENC_EMBD_POS,
MODEL_TENSOR.V_ENC_ATTN_Q,
MODEL_TENSOR.V_ENC_ATTN_K,
MODEL_TENSOR.V_ENC_ATTN_V,
MODEL_TENSOR.V_ENC_INPUT_NORM,
MODEL_TENSOR.V_ENC_OUTPUT,
MODEL_TENSOR.V_ENC_OUTPUT_NORM,
MODEL_TENSOR.V_ENC_FFN_UP,
MODEL_TENSOR.V_ENC_FFN_DOWN,
MODEL_TENSOR.V_PRE_NORM,
MODEL_TENSOR.V_POST_NORM,
MODEL_TENSOR.V_MM_INP_PROJ,
MODEL_TENSOR.V_MM_SOFT_EMB_NORM,
MODEL_TENSOR.V_RESMPL_POS_EMBD_K,
MODEL_TENSOR.V_RESMPL_ATTN_Q,
MODEL_TENSOR.V_RESMPL_ATTN_K,
MODEL_TENSOR.V_RESMPL_ATTN_V,
MODEL_TENSOR.V_RESMPL_ATTN_OUT,
MODEL_TENSOR.V_RESMPL_KV,
MODEL_TENSOR.V_RESMPL_KV_NORM,
MODEL_TENSOR.V_RESMPL_POST_NORM,
MODEL_TENSOR.V_RESMPL_Q_NORM,
MODEL_TENSOR.V_RESMPL_PROJ,
MODEL_TENSOR.V_RESMPL_QUERY,
],
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.LLAMA4: [
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_TENSOR.FFN_GATE_SHEXP,
MODEL_TENSOR.FFN_DOWN_SHEXP,
MODEL_TENSOR.FFN_UP_SHEXP,
],
MODEL_ARCH.DECI: [
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,