Commit f830688e9 for llama.cpp
commit f830688e91214cccfa25ed0a2b9a708ee5a855c3
Author: Toby <25832191+aetherbird@users.noreply.github.com>
Date: Thu Sep 24 02:57:31 2026 -0400
model : add Ling 3.0 VL support (#29151)
* model : fold Ling 3.0 VL into the BailingMoeV3 architecture
Assisted-by: Scout
* model : keep shared NORM rope list intact when gating bailingmoe3 on mrope sections
---------
Co-authored-by: aetherbird <aetherbird@users.noreply.github.com>
diff --git a/conversion/__init__.py b/conversion/__init__.py
index f966373f1..85db1d643 100644
--- a/conversion/__init__.py
+++ b/conversion/__init__.py
@@ -28,6 +28,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
"BailingMoeForCausalLM": "bailingmoe",
"BailingMoeV2ForCausalLM": "bailingmoe",
"BailingMoeV3ForCausalLM": "bailingmoe3",
+ "BailingMoeV3VLForConditionalGeneration": "bailingmoe3",
"BambaForCausalLM": "granite",
"BertForMaskedLM": "bert",
"BertForSequenceClassification": "bert",
@@ -302,6 +303,7 @@ MMPROJ_MODEL_MAP: dict[str, str] = {
"Gemma4ForConditionalGeneration": "gemma",
"Gemma4UnifiedForConditionalGeneration": "gemma",
"Glm4vForConditionalGeneration": "qwen3vl",
+ "BailingMoeV3VLForConditionalGeneration": "bailingmoe3",
"Glm4vMoeForConditionalGeneration": "qwen3vl",
"Glm5vForConditionalGeneration": "kimivl",
"GlmOcrForConditionalGeneration": "qwen3vl",
diff --git a/conversion/bailingmoe3.py b/conversion/bailingmoe3.py
index 20bba23e5..36b931564 100644
--- a/conversion/bailingmoe3.py
+++ b/conversion/bailingmoe3.py
@@ -9,7 +9,9 @@ import torch
if TYPE_CHECKING:
from torch import Tensor
-from .base import ModelBase, TextModel, gguf
+from .base import ModelBase, MmprojModel, TextModel, gguf
+
+from .qwen3vl import Qwen3VLVisionModel
@ModelBase.register("BailingMoeV3ForCausalLM")
@@ -74,7 +76,7 @@ class BailingMoeV3Model(TextModel):
self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
self.gguf_writer.add_expert_shared_feed_forward_length(self.hparams["moe_shared_expert_intermediate_size"])
- self.gguf_writer.add_expert_shared_count(self.hparams["num_shared_experts"])
+ self.gguf_writer.add_expert_shared_count(self.hparams.get("num_shared_experts", 1))
self.gguf_writer.add_leading_dense_block_count(self.hparams["first_k_dense_replace"])
self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
self.gguf_writer.add_expert_weights_norm(self.hparams["norm_topk_prob"])
@@ -191,3 +193,111 @@ class BailingMoeV3Model(TextModel):
experts = [name for layer in self._experts for name in layer]
if experts:
raise ValueError(f"Unprocessed experts: {experts}")
+
+
+@ModelBase.register("BailingMoeV3VLForConditionalGeneration")
+@ModelBase.example("inclusionAI/Ling-3.0-flash-VL")
+class BailingMoeV3VLModel(BailingMoeV3Model):
+ model_arch = gguf.MODEL_ARCH.BAILINGMOE3
+
+ def index_tensors(self, remote_hf_model_id: str | None = None):
+ # hoist text_config before the shared BailingMoeV3 logic runs:
+ # ModelBase.__init__ calls this with the raw VL config, where the text
+ # dims still live under text_config
+ if "text_config" in self.hparams:
+ self.hparams = {**self.hparams, **self.hparams["text_config"]}
+ return super().index_tensors(remote_hf_model_id=remote_hf_model_id)
+
+ def set_gguf_parameters(self):
+ super().set_gguf_parameters()
+ mrope_section = self.hparams.get("mrope_section")
+ if mrope_section is None:
+ raise ValueError("BailingMoeV3VL requires mrope_section in the config")
+ if sum(mrope_section[:3]) * 2 != self.hparams["qk_rope_head_dim"]:
+ raise ValueError(
+ f"mrope_section {mrope_section[:3]} counts rope pairs and must sum to"
+ f" qk_rope_head_dim / 2 = {self.hparams['qk_rope_head_dim'] // 2}"
+ )
+ # mrope_section is [t, h, w]; pad to the 4-wide sections array
+ self.gguf_writer.add_rope_dimension_sections(list(mrope_section[:3]) + [0])
+
+ @classmethod
+ def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+ name, gen = item
+
+ # Skip projector tensors; the vision tower is skipped by TextModel.filter_tensors
+ if name.startswith("linear_proj"):
+ return None
+
+ return super().filter_tensors(item)
+
+
+@ModelBase.register("BailingMoeV3VLForConditionalGeneration")
+@ModelBase.example("inclusionAI/Ling-3.0-flash-VL")
+class BailingMoeV3VLVisionModel(Qwen3VLVisionModel):
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ assert self.hparams_vision is not None
+
+ if self.hparams_vision.get("disable_merger_proj") is not True:
+ raise ValueError("BailingMoeV3VL requires disable_merger_proj=true")
+
+ # out_hidden_size is the vision encoder output (post spatial merge, pre linear_proj)
+ self.image_emb_dim = self.hparams_vision.get("out_hidden_size")
+ if self.image_emb_dim is None:
+ raise ValueError("BailingMoeV3VL vision config requires out_hidden_size")
+
+ def set_gguf_parameters(self):
+ assert self.hparams_vision is not None
+ MmprojModel.set_gguf_parameters(self) # skip Qwen3VLVisionModel parameters
+ self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.LING3VL)
+ self.gguf_writer.add_vision_use_gelu(True)
+
+ merge_size = self.hparams_vision.get("spatial_merge_size")
+ if merge_size is not None:
+ self.gguf_writer.add_vision_spatial_merge_size(int(merge_size))
+
+ rms_norm_eps = self.global_config.get("text_config", {}).get("rms_norm_eps", 1e-6)
+ self.gguf_writer.add_vision_attention_layernorm_eps(rms_norm_eps)
+
+ @classmethod
+ def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+ name, gen = item
+
+ if name.startswith("lm_head."):
+ return None
+
+ if name.startswith("linear_proj"):
+ # top-level projector MLP: linear_proj.0 -> mm.0, linear_proj.2 -> mm.2
+ parts = name.split(".")
+ if len(parts) != 3:
+ raise ValueError(f"Unexpected linear_proj tensor: {name}")
+ idx, suffix = int(parts[1]), parts[2]
+ name = f"mm.{idx}.{suffix}"
+ # the qwen3vl filter keeps only visual.*; skip it for the renamed projector tensors
+ return MmprojModel.filter_tensors((name, gen))
+
+ if name.startswith("model.visual."):
+ name = name.replace("model.visual.", "visual.", 1)
+
+ if not name.startswith("visual."):
+ return None
+
+ return super().filter_tensors((name, gen))
+
+ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+ assert self.hparams_vision is not None
+
+ if name.startswith("mm.0.") or name.startswith("mm.2."):
+ # top-level projector MLP (linear_proj.0 / linear_proj.2, renamed by filter_tensors)
+ yield (name, data_torch)
+ return
+
+ if name == "visual.merger.norm.weight" or name == "visual.merger.norm.bias":
+ # the merger is norm-only for Ling: per-patch LayerNorm before the spatial merge
+ new_name = f"mm.input_norm.{name.split('.')[-1]}"
+ yield (new_name, data_torch)
+ return
+
+ # Ling has no patch bias; the Conv3D split below matches the stock qwen3vl path
+ yield from Qwen3VLVisionModel.modify_tensors(self, data_torch, name, bid)
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 27c83516e..f585adcee 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -650,6 +650,7 @@ class VISION_PROJECTOR_TYPE(IntEnum):
GEMMA3N = auto()
GEMMA3 = auto()
QWEN3VL = auto()
+ LING3VL = auto()
STEP3VL = auto()
COGVLM = auto()
@@ -1407,6 +1408,7 @@ VISION_PROJECTOR_TYPE_NAMES: dict[VISION_PROJECTOR_TYPE, str] = {
VISION_PROJECTOR_TYPE.MERGER: "qwen2vl_merger",
VISION_PROJECTOR_TYPE.GEMMA3: "gemma3",
VISION_PROJECTOR_TYPE.QWEN3VL: "qwen3vl_merger",
+ VISION_PROJECTOR_TYPE.LING3VL: "ling3vl",
VISION_PROJECTOR_TYPE.STEP3VL: "step3vl",
}
@@ -5829,6 +5831,7 @@ class VisionProjectorType:
QWEN25VL = "qwen2.5vl_merger"
EXAONE4_5 = "exaone4_5"
QWEN3VL = "qwen3vl_merger"
+ LING3VL = "ling3vl"
STEP3VL = "step3vl"
ULTRAVOX = "ultravox"
INTERNVL = "internvl"
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index e195f50d0..3801e5cbe 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -2955,7 +2955,6 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_GRANITE_SWA:
case LLM_ARCH_CHAMELEON:
case LLM_ARCH_BAILINGMOE:
- case LLM_ARCH_BAILINGMOE3:
case LLM_ARCH_NEO_BERT:
case LLM_ARCH_SMOLLM3:
case LLM_ARCH_ARCEE:
@@ -2970,6 +2969,10 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_DOTS3NOTE:
case LLM_ARCH_NANBEIGE:
case LLM_ARCH_POCKETTTS:
+ return LLAMA_ROPE_TYPE_NORM;
+ case LLM_ARCH_BAILINGMOE3:
+ // VL files carry mrope sections; text-only files keep NORM rope
+ return model->hparams.use_mrope() ? LLAMA_ROPE_TYPE_MROPE : LLAMA_ROPE_TYPE_NORM;
// HY_V4 rotates consecutive pairs, matching the reference implementation
case LLM_ARCH_HY_V4:
return LLAMA_ROPE_TYPE_NORM;
diff --git a/src/models/bailingmoe3.cpp b/src/models/bailingmoe3.cpp
index e208c7d5a..907b25c67 100644
--- a/src/models/bailingmoe3.cpp
+++ b/src/models/bailingmoe3.cpp
@@ -15,6 +15,7 @@ void llama_model_bailingmoe3::load_arch_hparams(llama_model_loader & ml) {
hparams.kda_safe_gate = true;
}
ml.get_key(LLM_KV_KDA_GATE_LOWER_BOUND, hparams.kda_gate_lower_bound);
+ ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
ml.get_key_or_arr(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_arr, hparams.n_layer_all);
ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
@@ -233,6 +234,10 @@ llama_model_bailingmoe3::graph::graph(const llama_model & model, const llm_graph
const int64_t d_conv = hparams.ssm_d_conv;
const int64_t n_seqs = ubatch.n_seqs;
const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+ const bool use_mrope = hparams.use_mrope();
+ int sections[4];
+ std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
const int64_t qk_head_dim = hparams.n_embd_head_k_mla();
const int64_t v_head_dim = hparams.n_embd_head_v_mla();
const int64_t qk_rope_head_dim = hparams.n_rot();
@@ -326,10 +331,17 @@ llama_model_bailingmoe3::graph::graph(const llama_model & model, const llm_graph
ggml_row_size(kv_all->type, kv_lora_rank + qk_rope_head_dim),
ggml_row_size(kv_all->type, kv_lora_rank));
- q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
- ext_factor, attn_factor, beta_fast, beta_slow);
- k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
- ext_factor, attn_factor, beta_fast, beta_slow);
+ if (use_mrope) {
+ q_pe = ggml_rope_multi(ctx0, q_pe, inp_pos, nullptr, n_rot, sections, rope_type,
+ n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
+ k_pe = ggml_rope_multi(ctx0, k_pe, inp_pos, nullptr, n_rot, sections, rope_type,
+ n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
+ } else {
+ q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow);
+ k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow);
+ }
kv = build_norm(kv, layer.attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);
q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
@@ -482,10 +494,21 @@ llama_model_bailingmoe3::graph_mtp::graph_mtp(const llama_model & model, const l
ggml_row_size(kv_all->type, kv_lora_rank + qk_rope_head_dim),
ggml_row_size(kv_all->type, kv_lora_rank));
- q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
- ext_factor, attn_factor, beta_fast, beta_slow);
- k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
- ext_factor, attn_factor, beta_fast, beta_slow);
+ const bool use_mrope = hparams.use_mrope();
+ int sections[4];
+ std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
+
+ if (use_mrope) {
+ q_pe = ggml_rope_multi(ctx0, q_pe, inp_pos, nullptr, n_rot, sections, rope_type,
+ n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
+ k_pe = ggml_rope_multi(ctx0, k_pe, inp_pos, nullptr, n_rot, sections, rope_type,
+ n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
+ } else {
+ q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow);
+ k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow);
+ }
kv = build_norm(kv, layer.attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);
q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
diff --git a/tests/test-llama-archs.cpp b/tests/test-llama-archs.cpp
index 27d00b0df..5a8a196c1 100644
--- a/tests/test-llama-archs.cpp
+++ b/tests/test-llama-archs.cpp
@@ -333,7 +333,13 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
ms.add_kv(LLM_KV_ATTENTION_INDEXER_BLOCK_SIZE, uint32_t(4));
ms.add_kv(LLM_KV_ATTENTION_INDEXER_LOCAL_BLOCKS, uint32_t(1));
- ms.add_kv(LLM_KV_ROPE_DIMENSION_SECTIONS, std::vector<uint32_t>({n_embd_head/4, n_embd_head/4, n_embd_head/4, n_embd_head/4}));
+ // mrope sections count rope pairs; Ling 3.0 VL files carry [t, h, w] sections
+ // summing to n_rot / 2 (n_rot is 64 in this fixture)
+ if (arch == LLM_ARCH_BAILINGMOE3) {
+ ms.add_kv(LLM_KV_ROPE_DIMENSION_SECTIONS, std::vector<uint32_t>({8, 12, 12, 0}));
+ } else {
+ ms.add_kv(LLM_KV_ROPE_DIMENSION_SECTIONS, std::vector<uint32_t>({n_embd_head/4, n_embd_head/4, n_embd_head/4, n_embd_head/4}));
+ }
if (arch == LLM_ARCH_HY_V4) {
ms.add_kv(LLM_KV_HYPER_CONNECTION_COUNT, uint32_t(4));
diff --git a/tools/mtmd/CMakeLists.txt b/tools/mtmd/CMakeLists.txt
index 907468e87..91db32c40 100644
--- a/tools/mtmd/CMakeLists.txt
+++ b/tools/mtmd/CMakeLists.txt
@@ -55,6 +55,7 @@ add_library(mtmd
models/qwen2vl.cpp
models/minimax-m3.cpp
models/qwen3vl.cpp
+ models/ling3vl.cpp
models/mimovl.cpp
models/qwen3a.cpp
models/mimo-audio.cpp
diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h
index 72148a4d9..d18c76bf8 100644
--- a/tools/mtmd/clip-impl.h
+++ b/tools/mtmd/clip-impl.h
@@ -450,6 +450,7 @@ enum projector_type {
PROJECTOR_TYPE_GLM_EDGE,
PROJECTOR_TYPE_QWEN2VL,
PROJECTOR_TYPE_QWEN3VL,
+ PROJECTOR_TYPE_LING3VL,
PROJECTOR_TYPE_STEP3VL,
PROJECTOR_TYPE_GEMMA3,
PROJECTOR_TYPE_GEMMA3NV,
@@ -516,6 +517,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
{ PROJECTOR_TYPE_QWEN2VL, "qwen2vl_merger"},
{ PROJECTOR_TYPE_QWEN25VL, "qwen2.5vl_merger"},
{ PROJECTOR_TYPE_QWEN3VL, "qwen3vl_merger"},
+ { PROJECTOR_TYPE_LING3VL, "ling3vl"},
{ PROJECTOR_TYPE_STEP3VL, "step3vl"},
{ PROJECTOR_TYPE_GEMMA3, "gemma3"},
{ PROJECTOR_TYPE_GEMMA3NV, "gemma3nv"},
diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index feceb7ff7..62d146e1d 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -976,6 +976,10 @@ static std::unique_ptr<clip_graph> clip_get_graph_builder(clip_ctx * ctx, const
{
builder = std::make_unique<clip_graph_qwen3vl>(ctx, img);
} break;
+ case PROJECTOR_TYPE_LING3VL:
+ {
+ builder = std::make_unique<clip_graph_ling3vl>(ctx, img);
+ } break;
case PROJECTOR_TYPE_EXAONE4_5:
{
builder = std::make_unique<clip_graph_exaone4_5>(ctx, img);
@@ -1661,6 +1665,7 @@ struct clip_model_loader {
case PROJECTOR_TYPE_QWEN2VL:
case PROJECTOR_TYPE_QWEN25VL:
case PROJECTOR_TYPE_QWEN3VL:
+ case PROJECTOR_TYPE_LING3VL:
{
hparams.n_merge = 2; // default value for Qwen 2 and 2.5
hparams.image_resize_algo = RESIZE_ALGO_BICUBIC;
@@ -2488,6 +2493,15 @@ struct clip_model_loader {
model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight"));
model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"));
} break;
+ case PROJECTOR_TYPE_LING3VL:
+ {
+ model.mm_input_norm_w = get_tensor(TN_MM_INP_NORM); // merger.norm
+ model.mm_input_norm_b = get_tensor(TN_MM_INP_NORM_B); // merger.norm
+ model.mm_0_w = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight")); // linear_proj.0
+ model.mm_0_b = get_tensor(string_format(TN_LLAVA_PROJ, 0, "bias"));
+ model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight")); // linear_proj.2
+ model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"));
+ } break;
case PROJECTOR_TYPE_MIMOVL:
{
model.mm_0_w = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight"));
@@ -4049,6 +4063,7 @@ int clip_n_output_tokens_x(const clip_ctx * ctx, const clip_image_f32 * img) {
case PROJECTOR_TYPE_QWEN2VL:
case PROJECTOR_TYPE_QWEN25VL:
case PROJECTOR_TYPE_QWEN3VL:
+ case PROJECTOR_TYPE_LING3VL:
case PROJECTOR_TYPE_EXAONE4_5:
case PROJECTOR_TYPE_MIMOVL:
case PROJECTOR_TYPE_GLM4V:
@@ -4075,6 +4090,7 @@ int clip_n_output_tokens_y(const clip_ctx * ctx, const clip_image_f32 * img) {
case PROJECTOR_TYPE_QWEN2VL:
case PROJECTOR_TYPE_QWEN25VL:
case PROJECTOR_TYPE_QWEN3VL:
+ case PROJECTOR_TYPE_LING3VL:
case PROJECTOR_TYPE_EXAONE4_5:
case PROJECTOR_TYPE_MIMOVL:
case PROJECTOR_TYPE_GLM4V:
@@ -4155,6 +4171,7 @@ int clip_n_output_tokens(const clip_ctx * ctx, const clip_image_f32 * img) {
case PROJECTOR_TYPE_QWEN2VL:
case PROJECTOR_TYPE_QWEN25VL:
case PROJECTOR_TYPE_QWEN3VL:
+ case PROJECTOR_TYPE_LING3VL:
case PROJECTOR_TYPE_EXAONE4_5:
case PROJECTOR_TYPE_MIMOVL:
case PROJECTOR_TYPE_MINIMAX_M3:
@@ -4795,6 +4812,7 @@ bool clip_encode(struct clip_ctx * ctx, struct clip_encode_params * params) {
} break;
case PROJECTOR_TYPE_QWEN2VL:
case PROJECTOR_TYPE_QWEN3VL:
+ case PROJECTOR_TYPE_LING3VL:
case PROJECTOR_TYPE_GLM4V:
{
const int merge_ratio = hparams.n_merge;
@@ -5959,6 +5977,8 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
case PROJECTOR_TYPE_QWEN3VL:
// main path + deepstack paths
return ctx->model.mm_1_b->ne[0] * (1 + ctx->model.n_deepstack_layers);
+ case PROJECTOR_TYPE_LING3VL:
+ return ctx->model.mm_1_b->ne[0];
case PROJECTOR_TYPE_MIMOVL:
return ctx->model.mm_1_w->ne[1];
case PROJECTOR_TYPE_STEP3VL:
@@ -6052,6 +6072,7 @@ int clip_model_n_temporal_merge(const struct clip_ctx * ctx) {
case PROJECTOR_TYPE_QWEN2VL:
case PROJECTOR_TYPE_QWEN25VL:
case PROJECTOR_TYPE_QWEN3VL:
+ case PROJECTOR_TYPE_LING3VL:
return 2;
default:
return 1;
diff --git a/tools/mtmd/models/ling3vl.cpp b/tools/mtmd/models/ling3vl.cpp
new file mode 100644
index 000000000..ef8eb3a83
--- /dev/null
+++ b/tools/mtmd/models/ling3vl.cpp
@@ -0,0 +1,86 @@
+#include "models.h"
+
+ggml_cgraph * clip_graph_ling3vl::build() {
+ // same vision tower as qwen3vl, but the merger is norm-only (no fc1/fc2) and
+ // the projector MLP lives at the top level (mm.0 / mm.2)
+ GGML_ASSERT(model.class_embedding == nullptr);
+ GGML_ASSERT(model.mm_input_norm_w != nullptr); // merger norm (pre spatial merge)
+
+ const int batch_size = 1;
+ const int n_pos = n_patches;
+
+ norm_type norm_t = NORM_TYPE_NORMAL;
+
+ // vision M-RoPE, same layout as qwen3vl: [row, col, row, col] quarters
+ int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4};
+
+ ggml_tensor * inp = build_inp_with_temporal_merge();
+
+ // spatial merge
+ {
+ inp = ggml_permute(ctx0, inp, 1, 2, 0, 3); // [w, h, c, b] -> [c, w, h, b]
+ inp = ggml_cont_4d(
+ ctx0, inp,
+ n_embd * 2, n_patches_x / 2, n_patches_y, batch_size);
+ inp = ggml_reshape_4d(
+ ctx0, inp,
+ n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2));
+ inp = ggml_permute(ctx0, inp, 0, 2, 1, 3);
+ inp = ggml_cont_3d(
+ ctx0, inp,
+ n_embd, n_patches_x * n_patches_y, batch_size);
+ }
+
+ // add patch bias
+ if (model.patch_bias != nullptr) {
+ inp = ggml_add(ctx0, inp, model.patch_bias);
+ cb(inp, "patch_bias", -1);
+ }
+
+ // calculate absolute position embedding and apply
+ ggml_tensor * learned_pos_embd = resize_position_embeddings(GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ALIGN_CORNERS);
+ learned_pos_embd = ggml_cont_4d(
+ ctx0, learned_pos_embd,
+ n_embd * 2, n_patches_x / 2, n_patches_y, batch_size);
+ learned_pos_embd = ggml_reshape_4d(
+ ctx0, learned_pos_embd,
+ n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2));
+ learned_pos_embd = ggml_permute(ctx0, learned_pos_embd, 0, 2, 1, 3);
+ learned_pos_embd = ggml_cont_3d(
+ ctx0, learned_pos_embd,
+ n_embd, n_patches_x * n_patches_y, batch_size);
+
+ const int num_position_ids = n_pos * 4; // m-rope requires 4 dim per position
+ ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids);
+ ggml_set_name(positions, "positions");
+ ggml_set_input(positions);
+
+ ggml_tensor * inpL = build_vit(
+ inp, n_pos, norm_t, hparams.ffn_op, learned_pos_embd,
+ [&](ggml_tensor * c, const clip_layer &) {
+ return ggml_rope_multi(
+ ctx0, c, positions, nullptr,
+ d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1);
+ });
+
+ // multimodal projection (linear_proj MLP over the merged patches)
+ ggml_tensor * embeddings = inpL;
+
+ // per-patch merger norm, applied post-blocks before the 2x2 merge
+ // (merger.norm, LayerNorm over n_embd)
+ embeddings = build_norm(embeddings, model.mm_input_norm_w, model.mm_input_norm_b, norm_t, eps, -1);
+ cb(embeddings, "merger_norm", -1);
+
+ embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd * 4, n_pos / 4, batch_size);
+
+ embeddings = build_ffn(embeddings,
+ model.mm_0_w, model.mm_0_b,
+ nullptr, nullptr,
+ model.mm_1_w, model.mm_1_b,
+ ffn_op_type::FFN_GELU, -1);
+
+ // build the graph
+ ggml_build_forward_expand(gf, embeddings);
+
+ return gf;
+}
diff --git a/tools/mtmd/models/models.h b/tools/mtmd/models/models.h
index 5945c6d92..adb5ede30 100644
--- a/tools/mtmd/models/models.h
+++ b/tools/mtmd/models/models.h
@@ -50,6 +50,11 @@ struct clip_graph_qwen3vl : clip_graph_qwen2vl {
ggml_cgraph * build() override;
};
+struct clip_graph_ling3vl : clip_graph_qwen3vl {
+ clip_graph_ling3vl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph_qwen3vl(ctx, img) {}
+ ggml_cgraph * build() override;
+};
+
struct clip_graph_minimax_m3 : clip_graph {
clip_graph_minimax_m3(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
diff --git a/tools/mtmd/mtmd.cpp b/tools/mtmd/mtmd.cpp
index 00ecadcf4..2efa4d65d 100644
--- a/tools/mtmd/mtmd.cpp
+++ b/tools/mtmd/mtmd.cpp
@@ -694,6 +694,7 @@ struct mtmd_context {
case PROJECTOR_TYPE_QWEN2VL:
case PROJECTOR_TYPE_QWEN25VL:
case PROJECTOR_TYPE_QWEN3VL:
+ case PROJECTOR_TYPE_LING3VL:
case PROJECTOR_TYPE_MIMOVL:
{
// <|vision_start|> ... (image embeddings) ... <|vision_end|>