yield from super().modify_tensors(data_torch, name, bid)
-@ModelBase.register("Qwen2_5OmniModel")
-class Qwen25OmniModel(Qwen2VLVisionModel):
- has_vision_encoder = True
+class Qwen25AudioModel(MmprojModel):
has_audio_encoder = True
def __init__(self, *args, **kwargs):
self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["num_mel_bins"])
self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams_audio.get("layer_norm_eps", 1e-5))
- def get_vision_config(self) -> dict[str, Any] | None:
- return self.global_config["thinker_config"].get("vision_config")
-
- def get_audio_config(self) -> dict[str, Any] | None:
- return self.global_config["thinker_config"].get("audio_config")
-
def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
# SinusoidsPositionEmbedding
assert self.hparams_audio is not None
# this tensor is left unused in transformers code
# https://github.com/huggingface/transformers/blob/6e3063422c4b1c014aa60c32b9254fd2902f0f28/src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py#L1809
return
- yield from super().modify_tensors(data_torch, name, bid)
+ yield from MmprojModel.modify_tensors(self, data_torch, name, bid)
+
+ return # skip other tensors
+
+
+@ModelBase.register("Qwen2_5OmniModel")
+class Qwen25OmniModel(Qwen2VLVisionModel, Qwen25AudioModel):
+ has_audio_encoder = True
+ has_vision_encoder = True
+
+ def get_vision_config(self) -> dict[str, Any] | None:
+ return self.global_config["thinker_config"].get("vision_config")
+
+ def get_audio_config(self) -> dict[str, Any] | None:
+ return self.global_config["thinker_config"].get("audio_config")
+
+ def set_gguf_parameters(self):
+ super().set_gguf_parameters()
+ self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN25O)
+
+ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+ if "visual." in name:
+ yield from Qwen2VLVisionModel.modify_tensors(self, data_torch, name, bid)
+ elif "audio_tower." in name:
+ yield from Qwen25AudioModel.modify_tensors(self, data_torch, name, bid)
+ return # skip other tensors
@ModelBase.register("InternVisionModel")
class Qwen3VLVisionModel(MmprojModel):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
- assert self.hparams_vision is not None
+ if self.hparams_vision is None:
+ logger.info("No vision config found, skipping vision tensor processing")
+ return
+
# Compute image_size if not present
if "image_size" not in self.hparams_vision:
# For Qwen3VL/Qwen3VLMoe, compute from num_position_embeddings
def set_gguf_parameters(self):
super().set_gguf_parameters()
- self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN3VL)
+ # in case mixed modalities, the arch will be handled by subclass
+ if not self.has_audio_encoder:
+ self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN3VL)
self.gguf_writer.add_vision_use_gelu(True)
if self.hparams_vision is not None:
return
if name.startswith("visual."):
- yield from super().modify_tensors(data_torch, name, bid)
- return
+ yield from MmprojModel.modify_tensors(self, data_torch, name, bid)
+ return # skip other tensors
- # Fall back to parent class for other tensors
- yield from super().modify_tensors(data_torch, name, bid)
+
+@ModelBase.register("Qwen3OmniMoeForConditionalGeneration")
+class Qwen3OmniMmprojModel(Qwen3VLVisionModel, Qwen25AudioModel):
+ has_audio_encoder = True
+ has_vision_encoder = True
+
+ def get_vision_config(self) -> dict[str, Any] | None:
+ if self.has_vision_encoder:
+ return self.global_config["thinker_config"].get("vision_config")
+ else:
+ return None
+
+ def get_audio_config(self) -> dict[str, Any] | None:
+ if self.has_audio_encoder:
+ return self.global_config["thinker_config"].get("audio_config")
+ else:
+ return None
+
+ def set_gguf_parameters(self):
+ if self.has_vision_encoder:
+ Qwen3VLVisionModel.set_gguf_parameters(self)
+ self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.QWEN3VL)
+ if self.has_audio_encoder:
+ Qwen25AudioModel.set_gguf_parameters(self)
+ self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.QWEN3A)
+
+ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+ if "visual." in name:
+ if not self.has_vision_encoder:
+ raise ValueError(f"Model does not have vision encoder, but found tensor {name}")
+ # need to transform vision tensor naming, so that modify_tensors() logic can be used correctly
+ name = name.replace("thinker.visual.", "model.visual.")
+ if ".merger_list." in name:
+ name = name.replace(".merger_list.", ".deepstack_merger_list.")
+ name = name.replace(".ln_q", ".norm")
+ name = name.replace(".mlp.0", ".linear_fc1")
+ name = name.replace(".mlp.2", ".linear_fc2")
+ elif ".merger." in name:
+ name = name.replace(".ln_q", ".norm")
+ name = name.replace(".mlp.0", ".linear_fc1")
+ name = name.replace(".mlp.2", ".linear_fc2")
+ yield from Qwen3VLVisionModel.modify_tensors(self, data_torch, name, bid)
+ elif "audio_tower." in name:
+ if not self.has_audio_encoder:
+ raise ValueError(f"Model does not have audio encoder, but found tensor {name}")
+ if "conv2d" in name and name.endswith(".bias"):
+ # transform conv2d bias [n_embd] --> [1, 1, n_embd]
+ data_torch = data_torch.unsqueeze(-1).unsqueeze(-1)
+ yield from Qwen25AudioModel.modify_tensors(self, data_torch, name, bid)
+
+
+@ModelBase.register("Qwen3ASRForConditionalGeneration")
+class Qwen3ASRMmprojModel(Qwen3OmniMmprojModel):
+ has_audio_encoder = True
+ has_vision_encoder = False
@ModelBase.register("Glm4vForConditionalGeneration", "Glm4vMoeForConditionalGeneration", "GlmOcrForConditionalGeneration")
def set_gguf_parameters(self):
super().set_gguf_parameters()
-
- # Handle MRoPE (Multi-axis Rotary Position Embedding) for Qwen3-VL
- vision_config = self.hparams.get("vision_config", {})
+ if "thinker_config" in self.hparams:
+ vision_config = self.hparams["thinker_config"].get("vision_config", {})
+ else:
+ vision_config = self.hparams.get("vision_config", {})
deepstack_layer_num = len(vision_config.get("deepstack_visual_indexes", []))
self.gguf_writer.add_num_deepstack_layers(deepstack_layer_num)
yield from super().modify_tensors(data_torch, name, bid)
+@ModelBase.register("Qwen3OmniMoeForConditionalGeneration")
+class Qwen3OmniMoeTextModel(Qwen3VLMoeTextModel):
+ model_arch = gguf.MODEL_ARCH.QWEN3VLMOE
+
+ def set_vocab(self):
+ super().set_vocab()
+ # correct BOS/EOS tokens
+ with open(self.dir_model / "tokenizer_config.json", "r", encoding="utf-8") as f:
+ tokenizer_config = json.load(f)
+ added_tokens = tokenizer_config.get("added_tokens_decoder", {})
+ for token_id, data in added_tokens.items():
+ if data.get("content") == "<|im_end|>":
+ self.gguf_writer.add_bos_token_id(int(token_id))
+ self.gguf_writer.add_eos_token_id(int(token_id))
+ break
+
+ def set_gguf_parameters(self):
+ super().set_gguf_parameters()
+ self.gguf_writer.add_num_deepstack_layers(0)
+
+ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+ # Skip vision and audio tensors - they go in the mmproj file
+ if "visual." in name or "audio_tower." in name \
+ or "talker." in name or "code2wav." in name:
+ return
+
+ name = name.replace("thinker.", "")
+ yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Qwen3ASRForConditionalGeneration")
+class Qwen3ASRTextModel(Qwen3VLTextModel):
+ model_arch = gguf.MODEL_ARCH.QWEN3VL
+
+ def set_gguf_parameters(self):
+ super().set_gguf_parameters()
+ self.gguf_writer.add_num_deepstack_layers(0)
+
+ def set_vocab(self):
+ super().set_vocab()
+ # fix chat template, use correct chatml format
+ self.gguf_writer.add_chat_template("{% for message in messages %}{{'<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}")
+ # correct BOS/EOS tokens
+ with open(self.dir_model / "tokenizer_config.json", "r", encoding="utf-8") as f:
+ tokenizer_config = json.load(f)
+ added_tokens = tokenizer_config.get("added_tokens_decoder", {})
+ for token_id, data in added_tokens.items():
+ if data.get("content") == "<|im_end|>":
+ self.gguf_writer.add_bos_token_id(int(token_id))
+ self.gguf_writer.add_eos_token_id(int(token_id))
+ break
+
+ def modify_tensors(self, data_torch, name, bid):
+ # qwen3-omni
+ name = name.replace("thinker.", "")
+
+ # Skip vision and audio tensors - they go in the mmproj file
+ if "visual." in name or "audio_tower." in name \
+ or "talker." in name or "code2wav." in name:
+ return
+
+ yield from super().modify_tensors(data_torch, name, bid)
+
+
class _LinearAttentionVReorderBase(Qwen3NextModel):
model_arch = gguf.MODEL_ARCH.QWEN3NEXT # overridden by subclasses
"""reorders V heads from grouped to tiled order for ggml broadcast
A_ENC_INP_PROJ = auto() # gemma4
A_ENC_CONV1D = auto()
A_ENC_CONV1D_NORM = auto() # gemma3n
+ A_ENC_CONV2D = auto()
+ A_ENC_CONV_OUT = auto()
A_PRE_NORM = auto()
A_POST_NORM = auto()
A_ENC_LAYER_PRE_NORM = auto() # gemma3n
MODEL_TENSOR.A_ENC_EMBD_TO_LOGITS: "a.embd_to_logits",
MODEL_TENSOR.A_ENC_INP_PROJ: "a.input_projection",
MODEL_TENSOR.A_ENC_CONV1D: "a.conv1d.{bid}",
+ MODEL_TENSOR.A_ENC_CONV2D: "a.conv2d.{bid}",
+ MODEL_TENSOR.A_ENC_CONV_OUT: "a.conv_out",
MODEL_TENSOR.A_ENC_CONV1D_NORM: "a.conv1d.{bid}.norm",
MODEL_TENSOR.A_PRE_NORM: "a.pre_ln",
MODEL_TENSOR.A_POST_NORM: "a.post_ln",
MODEL_TENSOR.A_ENC_EMBD_TO_LOGITS,
MODEL_TENSOR.A_ENC_INP_PROJ,
MODEL_TENSOR.A_ENC_CONV1D,
+ MODEL_TENSOR.A_ENC_CONV2D,
+ MODEL_TENSOR.A_ENC_CONV_OUT,
MODEL_TENSOR.A_ENC_CONV1D_NORM,
MODEL_TENSOR.A_PRE_NORM,
MODEL_TENSOR.A_POST_NORM,
ULTRAVOX = "ultravox"
INTERNVL = "internvl"
QWEN2A = "qwen2a" # audio
+ QWEN3A = "qwen3a" # audio
GLMA = "glma" # audio
QWEN25O = "qwen2.5o" # omni
VOXTRAL = "voxtral"
"conformer.subsample_conv_projection.input_proj_linear", # gemma4
),
+ MODEL_TENSOR.A_ENC_CONV2D: (
+ "audio_tower.conv2d{bid}", # qwen3omni
+ ),
+
+ MODEL_TENSOR.A_ENC_CONV_OUT: (
+ "audio_tower.conv_out", # qwen3omni
+ ),
+
MODEL_TENSOR.A_PRE_NORM: (),
MODEL_TENSOR.A_POST_NORM: (
MODEL_TENSOR.A_MMPROJ: (
"audio.multi_modal_projector.linear_{bid}", # ultravox, meralion
- "audio_adapter.model.{bid}" # lfm2
+ "audio_adapter.model.{bid}", # lfm2
+ "audio_tower.proj{bid}", # qwen3omni
),
MODEL_TENSOR.A_MMPROJ_FC: (
models/pixtral.cpp
models/qwen2vl.cpp
models/qwen3vl.cpp
+ models/qwen3a.cpp
models/step3vl.cpp
models/siglip.cpp
models/whisper-enc.cpp
// ultravox
#define TN_CONV1D "a.conv1d.%d.%s"
+#define TN_CONV2D "a.conv2d.%d.%s"
+#define TN_CONV_OUT "a.conv_out.%s"
#define TN_MM_AUDIO_MLP "mm.a.mlp.%d.%s"
#define TN_MM_AUDIO_FC "mm.a.fc.%s" // fully connected layer
#define TN_MM_NORM_PRE "mm.a.norm_pre.%s"
PROJECTOR_TYPE_INTERNVL,
PROJECTOR_TYPE_LLAMA4,
PROJECTOR_TYPE_QWEN2A,
+ PROJECTOR_TYPE_QWEN3A,
PROJECTOR_TYPE_GLMA,
PROJECTOR_TYPE_QWEN25O, // will be replaced by QWEN2A or QWEN25VL depending on clip_ctx
PROJECTOR_TYPE_VOXTRAL,
{ PROJECTOR_TYPE_INTERNVL, "internvl"},
{ PROJECTOR_TYPE_LLAMA4, "llama4"},
{ PROJECTOR_TYPE_QWEN2A, "qwen2a"},
+ { PROJECTOR_TYPE_QWEN3A, "qwen3a"},
{ PROJECTOR_TYPE_GLMA, "glma"},
{ PROJECTOR_TYPE_QWEN25O, "qwen2.5o"},
{ PROJECTOR_TYPE_VOXTRAL, "voxtral"},
ggml_tensor * conv1d_1_b = nullptr;
ggml_tensor * conv1d_2_w = nullptr;
ggml_tensor * conv1d_2_b = nullptr;
+ ggml_tensor * conv_out_w = nullptr;
+ ggml_tensor * conv_out_b = nullptr;
ggml_tensor * mm_norm_pre_w = nullptr;
ggml_tensor * mm_norm_pre_b = nullptr;
ggml_tensor * mm_norm_mid_w = nullptr;
+ // qwen3a
+ ggml_tensor * conv2d_1_w = nullptr;
+ ggml_tensor * conv2d_1_b = nullptr;
+ ggml_tensor * conv2d_2_w = nullptr;
+ ggml_tensor * conv2d_2_b = nullptr;
+ ggml_tensor * conv2d_3_w = nullptr;
+ ggml_tensor * conv2d_3_b = nullptr;
+
// cogvlm
ggml_tensor * mm_post_fc_norm_w = nullptr;
ggml_tensor * mm_post_fc_norm_b = nullptr;
{
builder = std::make_unique<clip_graph_glm4v>(ctx, img);
} break;
+ case PROJECTOR_TYPE_QWEN3A:
+ {
+ builder = std::make_unique<clip_graph_qwen3a>(ctx, img);
+ } break;
case PROJECTOR_TYPE_YOUTUVL:
{
builder = std::make_unique<clip_graph_youtuvl>(ctx, img);
} break;
case PROJECTOR_TYPE_ULTRAVOX:
case PROJECTOR_TYPE_QWEN2A:
+ case PROJECTOR_TYPE_QWEN3A:
case PROJECTOR_TYPE_GLMA:
case PROJECTOR_TYPE_VOXTRAL:
case PROJECTOR_TYPE_MERALION:
model.mm_fc_w = get_tensor(string_format(TN_MM_AUDIO_FC, "weight"));
model.mm_fc_b = get_tensor(string_format(TN_MM_AUDIO_FC, "bias"));
} break;
+ case PROJECTOR_TYPE_QWEN3A:
+ {
+ model.conv2d_1_w = get_tensor(string_format(TN_CONV2D, 1, "weight"));
+ model.conv2d_1_b = get_tensor(string_format(TN_CONV2D, 1, "bias"));
+ model.conv2d_2_w = get_tensor(string_format(TN_CONV2D, 2, "weight"));
+ model.conv2d_2_b = get_tensor(string_format(TN_CONV2D, 2, "bias"));
+ model.conv2d_3_w = get_tensor(string_format(TN_CONV2D, 3, "weight"));
+ model.conv2d_3_b = get_tensor(string_format(TN_CONV2D, 3, "bias"));
+ model.conv_out_w = get_tensor(string_format(TN_CONV_OUT, "weight")); // no bias
+ model.mm_1_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 1, "weight"));
+ model.mm_1_b = get_tensor(string_format(TN_MM_AUDIO_MLP, 1, "bias"));
+ model.mm_2_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 2, "weight"));
+ model.mm_2_b = get_tensor(string_format(TN_MM_AUDIO_MLP, 2, "bias"));
+ } break;
case PROJECTOR_TYPE_VOXTRAL:
{
model.conv1d_1_w = get_tensor(string_format(TN_CONV1D, 1, "weight"));
n_patches /= 2;
}
} break;
+ case PROJECTOR_TYPE_QWEN3A:
+ {
+ // 3x stride-2 conv2d: each step is floor((n-1)/2)+1
+ int n = img->nx;
+ n = (n - 1) / 2 + 1;
+ n = (n - 1) / 2 + 1;
+ n = (n - 1) / 2 + 1;
+ n_patches = n;
+ } break;
case PROJECTOR_TYPE_GLMA:
{
n_patches = img->nx;
case PROJECTOR_TYPE_INTERNVL:
case PROJECTOR_TYPE_NEMOTRON_V2_VL:
case PROJECTOR_TYPE_QWEN2A:
+ case PROJECTOR_TYPE_QWEN3A:
case PROJECTOR_TYPE_GLMA:
case PROJECTOR_TYPE_ULTRAVOX:
case PROJECTOR_TYPE_LFM2:
return ctx->model.mm_model_proj->ne[1];
case PROJECTOR_TYPE_QWEN2A:
return ctx->model.mm_fc_w->ne[1];
- case PROJECTOR_TYPE_GLMA:
+ case PROJECTOR_TYPE_QWEN3A:
return ctx->model.mm_2_w->ne[1];
+ case PROJECTOR_TYPE_GLMA:
case PROJECTOR_TYPE_LFM2:
case PROJECTOR_TYPE_KIMIVL:
case PROJECTOR_TYPE_PADDLEOCR:
switch (ctx->proj_type()) {
case PROJECTOR_TYPE_ULTRAVOX:
case PROJECTOR_TYPE_QWEN2A:
+ case PROJECTOR_TYPE_QWEN3A:
case PROJECTOR_TYPE_GLMA:
case PROJECTOR_TYPE_VOXTRAL:
case PROJECTOR_TYPE_MERALION:
const mobilenetv5_block & block);
};
+struct clip_graph_qwen3a : clip_graph {
+ clip_graph_qwen3a(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
+ ggml_cgraph * build() override;
+};
+
struct clip_graph_kimik25 : clip_graph {
clip_graph_kimik25(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
--- /dev/null
+#include "models.h"
+
+ggml_cgraph * clip_graph_qwen3a::build() {
+ ggml_tensor * inp = build_inp_raw(1);
+
+ // conv2d block
+ // TODO: do we need to split by chunks of n_window each like on transformers impl?
+ {
+ inp = ggml_conv_2d(ctx0, model.conv2d_1_w, inp, 2, 2, 1, 1, 1, 1);
+ inp = ggml_add(ctx0, inp, model.conv2d_1_b);
+ inp = ggml_gelu_erf(ctx0, inp);
+
+ inp = ggml_conv_2d(ctx0, model.conv2d_2_w, inp, 2, 2, 1, 1, 1, 1);
+ inp = ggml_add(ctx0, inp, model.conv2d_2_b);
+ inp = ggml_gelu_erf(ctx0, inp);
+
+ inp = ggml_conv_2d(ctx0, model.conv2d_3_w, inp, 2, 2, 1, 1, 1, 1);
+ inp = ggml_add(ctx0, inp, model.conv2d_3_b);
+ inp = ggml_gelu_erf(ctx0, inp);
+
+ // inp [n_pos, n_mels/8, channels, 1] (W, H, C, N)
+ cb(inp, "after_conv_blocks", -1);
+
+ const int64_t n_pos_after_conv = inp->ne[0];
+ const int64_t n_mel_after_conv = inp->ne[1]; // 128/8 = 16
+
+ inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 0, 2, 3, 1));
+ inp = ggml_reshape_2d(ctx0, inp, n_pos_after_conv, n_mel_after_conv * inp->ne[3]); // [n_pos, 7680]
+ inp = ggml_cont(ctx0, ggml_transpose(ctx0, inp)); // [7680, n_pos]
+
+ // project to n_embd
+ inp = ggml_mul_mat(ctx0, model.conv_out_w, inp);
+ if (model.conv_out_b) {
+ inp = ggml_add(ctx0, inp, model.conv_out_b);
+ }
+ cb(inp, "after_conv_out", -1);
+ }
+
+ auto n_pos = inp->ne[1];
+
+ ggml_tensor * pos_embd_selected = ggml_view_2d(
+ ctx0, model.position_embeddings,
+ model.position_embeddings->ne[0], n_pos,
+ model.position_embeddings->nb[1], 0
+ );
+ ggml_tensor * cur = build_vit(
+ inp, n_pos,
+ NORM_TYPE_NORMAL,
+ hparams.ffn_op,
+ pos_embd_selected,
+ nullptr);
+
+ cb(cur, "after_transformer", -1);
+
+ // projector
+ cur = build_ffn(cur,
+ model.mm_1_w, model.mm_1_b,
+ nullptr, nullptr,
+ model.mm_2_w, model.mm_2_b,
+ FFN_GELU_ERF,
+ -1);
+
+ cb(cur, "projected", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+}
// set preprocessor
switch (proj) {
case PROJECTOR_TYPE_QWEN2A:
+ case PROJECTOR_TYPE_QWEN3A:
case PROJECTOR_TYPE_QWEN25O:
{
// <|audio_bos|> ... (embeddings) ... <|audio_eos|>
}
bool mtmd_decode_use_mrope(mtmd_context * ctx) {
+ if (ctx->ctx_v == nullptr && ctx->proj_type_a() == PROJECTOR_TYPE_QWEN3A) {
+ // qwen3-asr
+ return true;
+ }
switch (ctx->proj_type_v()) {
case PROJECTOR_TYPE_QWEN2VL:
case PROJECTOR_TYPE_QWEN25VL:
overview / pkg / ggml / sources / llama.cpp / commitdiff