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gguf converter for mimo audio

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Xuan Son Nguyen
2026-07-27 20:04:55 +02:00
parent 7ef790f90a
commit b668302604
4 changed files with 238 additions and 9 deletions
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conversion/mimo.py
+112 -9
View File
@@ -1,8 +1,9 @@
from __future__ import annotations
import json
import re
from typing import Callable, TYPE_CHECKING
from typing import Any, Callable, Iterable, TYPE_CHECKING
import torch
@@ -229,7 +230,13 @@ class MimoV2Model(TextModel):
@ModelBase.register("MiMoV2ForCausalLM")
class MiMoV2VisionModel(MmprojModel):
class MiMoV2VisionAudioModel(MmprojModel):
has_audio_encoder = True
_audio_tok_hparams: dict[str, Any] | None = None
_rvq_codebook_sizes: list[int] | None = None
_code_embd: dict[int, Tensor] | None = None
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
assert self.hparams_vision is not None
@@ -253,10 +260,22 @@ class MiMoV2VisionModel(MmprojModel):
self.visual_token_window_size = int(hp.get("visual_token_window_size", -1))
self.use_sink = bool(hp.get("use_sink", False))
def get_audio_config(self) -> dict[str, Any] | None:
if self._audio_tok_hparams is None:
path = self.dir_model / "audio_tokenizer" / "config.json"
with open(path, "r", encoding="utf-8") as f:
cfg = json.load(f)
# aliases so MmprojModel.find_aparam() / n_block_keys can resolve them
cfg["hidden_size"] = cfg["d_model"]
cfg["intermediate_size"] = cfg["encoder_ffn_dim"]
cfg["num_attention_heads"] = cfg["encoder_attention_heads"]
self._audio_tok_hparams = cfg
return self._audio_tok_hparams
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MIMOVL)
self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.MIMOVL)
self.gguf_writer.add_vision_use_silu(True)
self.gguf_writer.add_vision_head_count_kv(self.num_kv_heads)
self.gguf_writer.add_vision_spatial_merge_size(self.spatial_merge_size)
@@ -266,19 +285,43 @@ class MiMoV2VisionModel(MmprojModel):
self.gguf_writer.add_vision_min_pixels(int(self.preprocessor_config["min_pixels"]))
self.gguf_writer.add_vision_max_pixels(int(self.preprocessor_config["max_pixels"]))
assert self.hparams_audio is not None
self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.MIMO_AUDIO)
self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["n_mels"])
self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams_audio.get("layer_norm_eps", 1e-5))
assert self._rvq_codebook_sizes is not None
self.gguf_writer.add_audio_rvq_num_quantizers(len(self._rvq_codebook_sizes))
self.gguf_writer.add_audio_rvq_codebook_size(self._rvq_codebook_sizes)
n_layer = self.hparams_audio["encoder_layers"]
swa_per_block = self.hparams_audio.get("swa_per_block", 1)
if self.hparams_audio.get("hybrid_attention") and swa_per_block > 1:
wa_pattern = [0 if i % swa_per_block < swa_per_block - 1 else -1 for i in range(n_layer)]
else:
wa_pattern = [-1] * n_layer
self.gguf_writer.add_audio_wa_pattern_mode(wa_pattern)
self.gguf_writer.add_audio_window_size(int(self.hparams_audio["encoder_attn_window_size"][0]))
audio_cfg = self.global_config["audio_config"]
self.gguf_writer.add_audio_local_block_count(int(audio_cfg["input_local_layers"]))
self.gguf_writer.add_audio_local_group_size(int(audio_cfg["group_size"]))
def tensor_force_quant(self, name, new_name, bid, n_dims):
# Sinks must be F32: any sink-style softmax/mask add in ggml requires
# F32, and we fold sinks into a host-built F32 mask at encode time.
if new_name.endswith(".attn_sinks"):
# for audio encoder: keep codebook in F32
if new_name in (
gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.A_ENC_RVQ_CODEBOOK] + ".weight",
gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.A_MM_CODE_EMBD] + ".weight",
):
return gguf.GGMLQuantizationType.F32
return super().tensor_force_quant(name, new_name, bid, n_dims)
@classmethod
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
name, _ = item
if not name.startswith("visual."):
return None
return super().filter_tensors(item)
if name.startswith("visual.") or name.startswith("speech_embeddings.") or name.startswith("audio_encoder."):
return super().filter_tensors(item)
return None
def modify_tensors(self, data_torch, name, bid):
# Conv3D patch embed: split along the temporal axis (kt=2) into two Conv2D
@@ -292,4 +335,64 @@ class MiMoV2VisionModel(MmprojModel):
yield (embd_name + ".weight.1", data_torch[:, :, 1, ...])
return
if m := re.match(r"^speech_embeddings\.(\d+)\.weight$", name):
if self._code_embd is None:
self._code_embd = {}
self._code_embd[int(m.group(1))] = data_torch
n_channels = int(self.global_config["audio_config"]["audio_channels"])
if len(self._code_embd) < n_channels:
return
merged = torch.stack([self._code_embd.pop(i) for i in range(n_channels)], dim=0)
yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MM_CODE_EMBD), merged)
return
if "conv1.bias" in name or "conv2.bias" in name:
# transpose conv1/conv2 bias so it broadcasts against [n_frames, C_out, 1]
data_torch = data_torch.unsqueeze(-1)
if name == "audio_encoder.projection.mlp.0.weight":
yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MMPROJ, 1), data_torch)
return
if name == "audio_encoder.projection.mlp.2.weight":
yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MMPROJ, 2), data_torch)
return
yield from super().modify_tensors(data_torch, name, bid)
def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
# note: audio encoder is in its own subdir "audio_tokenizer"
from safetensors.torch import load_file
tok_dir = self.dir_model / "audio_tokenizer"
state_dict = load_file(tok_dir / "model.safetensors")
codebook_re = re.compile(r"^encoder\.quantizer\.vq\.layers\.(\d+)\._codebook\.embed$")
codebooks: dict[int, Tensor] = {}
# EMA/training-only RVQ buffers - not needed for inference (nearest-codebook
# lookup only reads "_codebook.embed")
skip_suffixes = (
"_codebook.cluster_size",
"_codebook.embed_avg",
"_codebook.inited",
)
for name, tensor in state_dict.items():
if name.endswith(skip_suffixes):
continue
if m := codebook_re.match(name):
codebooks[int(m.group(1))] = tensor
continue
yield name, tensor
# gather codebooks and merge into 3D tensor, similar to MoE MLP tensors
n_q = len(codebooks)
ordered = [codebooks[i] for i in range(n_q)]
self._rvq_codebook_sizes = [int(cb.shape[0]) for cb in ordered]
max_bins = max(self._rvq_codebook_sizes)
dim = ordered[0].shape[1]
merged = ordered[0].new_zeros(n_q, max_bins, dim)
for i, cb in enumerate(ordered):
merged[i, : cb.shape[0], :] = cb
yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_ENC_RVQ_CODEBOOK), merged)
gguf-py/gguf/constants.py
+49
View File
@@ -374,6 +374,12 @@ class Keys:
CONV_KERNEL_SIZE = "clip.audio.conv_kernel_size"
MAX_POS_EMB = "clip.audio.max_pos_emb"
FEATURE_LAYERS = "clip.audio.feature_layer" # Granite Speech Plus
RVQ_NUM_QUANTIZERS = "clip.audio.rvq.num_quantizers"
RVQ_CODEBOOK_SIZE = "clip.audio.rvq.codebook_size"
WA_PATTERN_MODE = "clip.audio.wa_pattern_mode" # per-layer -1 (full) / 0 (windowed)
WINDOW_SIZE = "clip.audio.window_size"
LOCAL_BLOCK_COUNT = "clip.audio.local_block_count" # mimo-v2.5: input_local_transformer layer count
LOCAL_GROUP_SIZE = "clip.audio.local_group_size" # mimo-v2.5: input_local_transformer grouping size
class Attention:
HEAD_COUNT = "clip.audio.attention.head_count"
@@ -942,6 +948,9 @@ class MODEL_TENSOR(IntEnum):
A_ENC_FFN_SCALE_1 = auto() # gemma3n
A_ENC_FFN_GATE_1 = auto() # lfm2, gemma3n
A_ENC_FFN_DOWN_1 = auto() # lfm2, gemma3n
A_ENC_DOWNSAMPLE_CONV = auto() # mimo-audio-tokenizer: post-transformer downsample conv
A_ENC_DOWNSAMPLE_NORM = auto() # mimo-audio-tokenizer: post-transformer downsample norm
A_ENC_RVQ_CODEBOOK = auto() # mimo-audio-tokenizer: residual vector quantizer codebook, per quantizer index
A_MMPROJ = auto()
A_MMPROJ_FC = auto()
A_MM_NORM_PRE = auto()
@@ -950,6 +959,17 @@ class MODEL_TENSOR(IntEnum):
A_MM_HARD_EMB_NORM = auto() # gemma3n
A_MM_SOFT_EMB_NORM = auto() # gemma3n
A_MM_INP_PROJ = auto() # gemma3n
A_MM_CODE_EMBD = auto() # mimo: text-side RVQ code embedding table ("text codebook"), merged 3D [n_channels, vocab, dim]
A_MM_LOCAL_ATTN_Q = auto() # mimo: input_local_transformer (LLM-side connector)
A_MM_LOCAL_ATTN_K = auto()
A_MM_LOCAL_ATTN_V = auto()
A_MM_LOCAL_ATTN_OUT = auto()
A_MM_LOCAL_FFN_GATE = auto()
A_MM_LOCAL_FFN_UP = auto()
A_MM_LOCAL_FFN_DOWN = auto()
A_MM_LOCAL_LN1 = auto()
A_MM_LOCAL_LN2 = auto()
A_MM_LOCAL_NORM = auto() # final norm after all input_local_transformer layers
A_PER_DIM_K_SCALE = auto() # gemma4
A_PER_DIM_SCALE = auto() # gemma4
# nextn/mtp
@@ -1528,6 +1548,9 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
MODEL_TENSOR.A_ENC_FFN_UP_1: "a.blk.{bid}.ffn_up_1",
MODEL_TENSOR.A_ENC_FFN_GATE_1: "a.blk.{bid}.ffn_gate_1",
MODEL_TENSOR.A_ENC_FFN_DOWN_1: "a.blk.{bid}.ffn_down_1",
MODEL_TENSOR.A_ENC_DOWNSAMPLE_CONV: "a.downsample.conv",
MODEL_TENSOR.A_ENC_DOWNSAMPLE_NORM: "a.downsample.norm",
MODEL_TENSOR.A_ENC_RVQ_CODEBOOK: "a.rvq.codebook",
MODEL_TENSOR.A_MMPROJ: "mm.a.mlp.{bid}",
MODEL_TENSOR.A_MMPROJ_FC: "mm.a.fc",
MODEL_TENSOR.A_MM_NORM_PRE: "mm.a.norm_pre",
@@ -1536,6 +1559,17 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
MODEL_TENSOR.A_MM_SOFT_EMB_NORM: "mm.a.soft_emb_norm", # gemma3n
MODEL_TENSOR.A_MM_EMBEDDING: "mm.a.embedding", # gemma3n
MODEL_TENSOR.A_MM_HARD_EMB_NORM: "mm.a.hard_emb_norm", # gemma3n
MODEL_TENSOR.A_MM_CODE_EMBD: "mm.a.code_embd",
MODEL_TENSOR.A_MM_LOCAL_ATTN_Q: "mm.a.local_blk.{bid}.attn_q",
MODEL_TENSOR.A_MM_LOCAL_ATTN_K: "mm.a.local_blk.{bid}.attn_k",
MODEL_TENSOR.A_MM_LOCAL_ATTN_V: "mm.a.local_blk.{bid}.attn_v",
MODEL_TENSOR.A_MM_LOCAL_ATTN_OUT: "mm.a.local_blk.{bid}.attn_out",
MODEL_TENSOR.A_MM_LOCAL_FFN_GATE: "mm.a.local_blk.{bid}.ffn_gate",
MODEL_TENSOR.A_MM_LOCAL_FFN_UP: "mm.a.local_blk.{bid}.ffn_up",
MODEL_TENSOR.A_MM_LOCAL_FFN_DOWN: "mm.a.local_blk.{bid}.ffn_down",
MODEL_TENSOR.A_MM_LOCAL_LN1: "mm.a.local_blk.{bid}.ln1",
MODEL_TENSOR.A_MM_LOCAL_LN2: "mm.a.local_blk.{bid}.ln2",
MODEL_TENSOR.A_MM_LOCAL_NORM: "mm.a.local_norm",
MODEL_TENSOR.A_PER_DIM_K_SCALE: "a.blk.{bid}.per_dim_k_scale", # gemma4
MODEL_TENSOR.A_PER_DIM_SCALE: "a.blk.{bid}.per_dim_scale", # gemma4
# lfm2 audio
@@ -1737,10 +1771,24 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.A_ENC_FFN_UP_1,
MODEL_TENSOR.A_ENC_FFN_GATE_1,
MODEL_TENSOR.A_ENC_FFN_DOWN_1,
MODEL_TENSOR.A_ENC_DOWNSAMPLE_CONV,
MODEL_TENSOR.A_ENC_DOWNSAMPLE_NORM,
MODEL_TENSOR.A_ENC_RVQ_CODEBOOK,
MODEL_TENSOR.A_MMPROJ,
MODEL_TENSOR.A_MMPROJ_FC,
MODEL_TENSOR.A_MM_NORM_PRE,
MODEL_TENSOR.A_MM_NORM_MID,
MODEL_TENSOR.A_MM_CODE_EMBD,
MODEL_TENSOR.A_MM_LOCAL_ATTN_Q,
MODEL_TENSOR.A_MM_LOCAL_ATTN_K,
MODEL_TENSOR.A_MM_LOCAL_ATTN_V,
MODEL_TENSOR.A_MM_LOCAL_ATTN_OUT,
MODEL_TENSOR.A_MM_LOCAL_FFN_GATE,
MODEL_TENSOR.A_MM_LOCAL_FFN_UP,
MODEL_TENSOR.A_MM_LOCAL_FFN_DOWN,
MODEL_TENSOR.A_MM_LOCAL_LN1,
MODEL_TENSOR.A_MM_LOCAL_LN2,
MODEL_TENSOR.A_MM_LOCAL_NORM,
MODEL_TENSOR.A_ENC_NORM_CONV,
MODEL_TENSOR.A_ENC_LINEAR_POS,
MODEL_TENSOR.A_ENC_POS_BIAS_U,
@@ -4781,6 +4829,7 @@ class VisionProjectorType:
MINICPMV4_6 = "minicpmv4_6"
GRANITE_SPEECH = "granite_speech" # audio
MIMOVL = "mimovl"
MIMO_AUDIO = "mimo_audio"
GRANITE4_VISION = "granite4_vision"
gguf-py/gguf/gguf_writer.py
+18
View File
@@ -1344,6 +1344,24 @@ class GGUFWriter:
def add_audio_num_mel_bins(self, value: int) -> None:
self.add_uint32(Keys.ClipAudio.NUM_MEL_BINS, value)
def add_audio_rvq_num_quantizers(self, value: int) -> None:
self.add_uint32(Keys.ClipAudio.RVQ_NUM_QUANTIZERS, value)
def add_audio_rvq_codebook_size(self, values: Sequence[int]) -> None:
self.add_array(Keys.ClipAudio.RVQ_CODEBOOK_SIZE, values)
def add_audio_wa_pattern_mode(self, modes: Sequence[int]) -> None:
self.add_array(Keys.ClipAudio.WA_PATTERN_MODE, modes)
def add_audio_window_size(self, value: int) -> None:
self.add_uint32(Keys.ClipAudio.WINDOW_SIZE, value)
def add_audio_local_block_count(self, value: int) -> None:
self.add_uint32(Keys.ClipAudio.LOCAL_BLOCK_COUNT, value)
def add_audio_local_group_size(self, value: int) -> None:
self.add_uint32(Keys.ClipAudio.LOCAL_GROUP_SIZE, value)
def add_audio_stack_factor(self, value: int) -> None:
self.add_uint32(Keys.ClipAudio.Projector.STACK_FACTOR, value)
gguf-py/gguf/tensor_mapping.py
+59
View File
@@ -2095,6 +2095,7 @@ class TensorNameMap:
"conformer.pre_encode.conv.{bid}", # lfm2
"model.audio_tower.subsample_conv_projection.conv_{bid}.conv", # gemma3n
"conformer.subsample_conv_projection.layer{bid}.conv", # gemma4
"encoder.conv{bid}", # mimo-audio-tokenizer
),
MODEL_TENSOR.A_ENC_CONV1D_NORM: (
@@ -2119,6 +2120,7 @@ class TensorNameMap:
MODEL_TENSOR.A_POST_NORM: (
"audio_tower.layer_norm", # ultravox
"audio_tower.ln_post", # qwen2omni
"encoder.layer_norm", # mimo-audio-tokenizer
),
MODEL_TENSOR.A_ENC_ATTN_Q: (
@@ -2127,6 +2129,7 @@ class TensorNameMap:
"conformer.layers.{bid}.attention.attn.q_proj", # gemma3n
"conformer.layers.{bid}.self_attn.q_proj", # gemma4
"encoder.layers.{bid}.attn.to_q", # granite_speech
"encoder.layers.{bid}.self_attn.q_proj", # mimo-audio-tokenizer
),
MODEL_TENSOR.A_ENC_ATTN_K: (
@@ -2135,6 +2138,7 @@ class TensorNameMap:
"conformer.layers.{bid}.attention.attn.k_proj", # gemma3n
"conformer.layers.{bid}.self_attn.k_proj", # gemma4
"encoder.layers.{bid}.attn.to_k", # granite_speech (split from to_kv)
"encoder.layers.{bid}.self_attn.k_proj", # mimo-audio-tokenizer
),
MODEL_TENSOR.A_ENC_ATTN_V: (
@@ -2143,6 +2147,7 @@ class TensorNameMap:
"conformer.layers.{bid}.attention.attn.v_proj", # gemma3n
"conformer.layers.{bid}.self_attn.v_proj", # gemma4
"encoder.layers.{bid}.attn.to_v", # granite_speech (split from to_kv)
"encoder.layers.{bid}.self_attn.v_proj", # mimo-audio-tokenizer
),
MODEL_TENSOR.A_ENC_ATTN_K_REL: (
@@ -2171,6 +2176,7 @@ class TensorNameMap:
"conformer.layers.{bid}.norm_self_att", # lfm2
"conformer.layers.{bid}.attention.pre_attn_norm", # gemma3n
"encoder.layers.{bid}.attn.pre_norm", # granite_speech
"encoder.layers.{bid}.self_attn_layer_norm", # mimo-audio-tokenizer
),
MODEL_TENSOR.A_ENC_OUTPUT: (
@@ -2179,6 +2185,7 @@ class TensorNameMap:
"conformer.layers.{bid}.attention.post", # gemma3n
"conformer.layers.{bid}.self_attn.post", # gemma4
"encoder.layers.{bid}.attn.to_out", # granite_speech
"encoder.layers.{bid}.self_attn.out_proj", # mimo-audio-tokenizer
),
MODEL_TENSOR.A_ENC_OUTPUT_NORM: (
@@ -2186,6 +2193,7 @@ class TensorNameMap:
"conformer.layers.{bid}.norm_out", # lfm2
"conformer.layers.{bid}.attention.post_norm", # gemma3n
"encoder.layers.{bid}.post_norm", # granite_speech
"encoder.layers.{bid}.final_layer_norm", # mimo-audio-tokenizer
),
MODEL_TENSOR.A_ENC_FFN_NORM: (
@@ -2210,6 +2218,7 @@ class TensorNameMap:
"conformer.layers.{bid}.ffw_layer_start.ffw_layer_1", # gemma3n
"conformer.layers.{bid}.feed_forward1.ffw_layer_1", # gemma4
"encoder.layers.{bid}.ff1.up_proj", # granite_speech
"encoder.layers.{bid}.fc1", # mimo-audio-tokenizer
),
MODEL_TENSOR.A_ENC_FFN_GATE: (),
@@ -2220,6 +2229,7 @@ class TensorNameMap:
"conformer.layers.{bid}.ffw_layer_start.ffw_layer_2", # gemma3n
"conformer.layers.{bid}.feed_forward1.ffw_layer_2", # gemma4
"encoder.layers.{bid}.ff1.down_proj", # granite_speech
"encoder.layers.{bid}.fc2", # mimo-audio-tokenizer
),
MODEL_TENSOR.A_ENC_FFN_UP_1: (
@@ -2243,6 +2253,19 @@ class TensorNameMap:
"encoder.layers.{bid}.ff2.pre_norm", # granite_speech
),
MODEL_TENSOR.A_ENC_DOWNSAMPLE_CONV: (
"encoder.down_sample_layer.0", # mimo-audio-tokenizer
),
MODEL_TENSOR.A_ENC_DOWNSAMPLE_NORM: (
"encoder.down_sample_norm", # mimo-audio-tokenizer
),
# note: the raw per-quantizer "encoder.quantizer.vq.layers.{i}._codebook.embed"
# tensors are merged (padded + stacked, like MoE experts) into this single 3D
# tensor in conversion code, so no raw-name mapping is registered here.
MODEL_TENSOR.A_ENC_RVQ_CODEBOOK: (),
MODEL_TENSOR.A_ENC_FFN_POST_NORM_1: (
"conformer.layers.{bid}.ffw_layer_end.post_layer_norm", # gemma3n
"conformer.layers.{bid}.feed_forward2.post_layer_norm", # gemma4
@@ -2294,6 +2317,42 @@ class TensorNameMap:
"audio.multi_modal_projector.ln_mid", # ultravox
),
# note: the raw per-channel "speech_embeddings.{i}" tensors are merged
# (stacked, like MoE experts) into this single 3D tensor in conversion
# code, so no raw-name mapping is registered here.
MODEL_TENSOR.A_MM_CODE_EMBD: (),
MODEL_TENSOR.A_MM_LOCAL_ATTN_Q: (
"audio_encoder.input_local_transformer.layers.{bid}.self_attn.q_proj", # mimo-v2.5
),
MODEL_TENSOR.A_MM_LOCAL_ATTN_K: (
"audio_encoder.input_local_transformer.layers.{bid}.self_attn.k_proj", # mimo-v2.5
),
MODEL_TENSOR.A_MM_LOCAL_ATTN_V: (
"audio_encoder.input_local_transformer.layers.{bid}.self_attn.v_proj", # mimo-v2.5
),
MODEL_TENSOR.A_MM_LOCAL_ATTN_OUT: (
"audio_encoder.input_local_transformer.layers.{bid}.self_attn.o_proj", # mimo-v2.5
),
MODEL_TENSOR.A_MM_LOCAL_FFN_GATE: (
"audio_encoder.input_local_transformer.layers.{bid}.mlp.gate_proj", # mimo-v2.5
),
MODEL_TENSOR.A_MM_LOCAL_FFN_UP: (
"audio_encoder.input_local_transformer.layers.{bid}.mlp.up_proj", # mimo-v2.5
),
MODEL_TENSOR.A_MM_LOCAL_FFN_DOWN: (
"audio_encoder.input_local_transformer.layers.{bid}.mlp.down_proj", # mimo-v2.5
),
MODEL_TENSOR.A_MM_LOCAL_LN1: (
"audio_encoder.input_local_transformer.layers.{bid}.input_layernorm", # mimo-v2.5
),
MODEL_TENSOR.A_MM_LOCAL_LN2: (
"audio_encoder.input_local_transformer.layers.{bid}.post_attention_layernorm", # mimo-v2.5
),
MODEL_TENSOR.A_MM_LOCAL_NORM: (
"audio_encoder.input_local_transformer.norm", # mimo-v2.5
),
MODEL_TENSOR.A_ENC_CONV_DW: (
"conformer.layers.{bid}.conv.depthwise_conv", # lfm2
"conformer.layers.{bid}.lconv1d.depthwise_conv1d", # gemma3n