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index 06c2c50ad..b13483f9f 100644
--- a/conversion/__init__.py
+++ b/conversion/__init__.py
@@ -27,6 +27,8 @@ TEXT_MODEL_MAP: dict[str, str] = {
"BaichuanForCausalLM": "baichuan",
"BailingMoeForCausalLM": "bailingmoe",
"BailingMoeV2ForCausalLM": "bailingmoe",
+ "BailingMoeV3ForCausalLM": "bailing_hybrid",
+ "BailingMoeV3Model": "bailing_hybrid",
"BambaForCausalLM": "granite",
"BertForMaskedLM": "bert",
"BertForSequenceClassification": "bert",
diff --git a/conversion/bailing_hybrid.py b/conversion/bailing_hybrid.py
new file mode 100644
index 000000000..c2765c146
--- /dev/null
+++ b/conversion/bailing_hybrid.py
@@ -0,0 +1,221 @@
+from __future__ import annotations
+
+import math
+from typing import Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+ from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf, logger
+
+
+@ModelBase.register("BailingMoeV3Model", "BailingMoeV3ForCausalLM")
+class BailingHybridModel(TextModel):
+ """Ling 3.0 flash (inclusionAI): hybrid KDA + gated MLA MoE, `bailing_hybrid`.
+
+ NOT the `bailingmoe2` stack (Ling 2.0) despite the shared family name -- only
+ the MoE router carries over. The KDA block comes from Kimi Linear and the MLA
+ block from Kimi's no-Q-compression variant, so conversion mirrors
+ conversion/kimi_linear.py. The differences that matter here:
+
+ * The attention module is `attention.`, not `self_attn.`. None of Kimi's
+ tensor mappings match; bailing-hybrid entries were added alongside them.
+
+ * `attention.g_proj.weight` exists on BOTH layer types with different
+ shapes and different meanings: on KDA layers it is the full-rank output
+ gate {n_embd, d_inner}, on MLA layers the head-wise attention gate
+ {n_embd, n_head}. A name->enum table cannot express that, so the KDA one
+ is renamed to `g_full_proj` here. Without this the loader silently binds
+ the wrong tensor.
+
+ * A_log is stored as +exp(A_log), NOT Kimi's -exp(A_log). config sets
+ kda_safe_gate=true, which changes the decay to
+ g = kda_lower_bound * sigmoid(exp(A_log) * (f(x) + dt_bias))
+ so the sign lives in kda_lower_bound (-5.0), written as a KV below.
+ Verified against fla ops/kda/gate.py (naive ref and Triton kernel agree).
+
+ * `no_kda_lora: true` -> full-rank f_proj / g_proj, so SSM_F / SSM_G
+ replace Kimi's SSM_F_{A,B} / SSM_G_{A,B} pairs.
+
+ Config fields that look load-bearing and are NOT (verified by grepping
+ modeling_bailing_moe_v3.py): expert_swiglu_limit_list and
+ share_expert_swiglu_limit_list (populated with non-zero values for the last
+ few layers, yet BailingMoeV3MLP.forward is a plain SwiGLU), use_qk_norm,
+ linear_silu, group_norm_size, max_window_layers, mtp_use_kda, use_mla_nope,
+ use_nGPT, scale_router_input, seq_aux. partial_rotary_factor is overwritten
+ to 1.0 by the rotary module itself, so rotary_dim == qk_rope_head_dim.
+ """
+
+ model_arch = gguf.MODEL_ARCH.BAILING_HYBRID
+
+ _experts: list[dict[str, Tensor]] | None = None
+
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ # the MTP/nextn head is a real block in the checkpoint; include it unless
+ # --no-mtp, matching glm/command_r. llama.cpp marks it TENSOR_SKIP.
+ if (n_nextn := int(self.hparams.get("num_nextn_predict_layers", 0) or 0)) > 0 and not self.no_mtp:
+ self.block_count = self.hparams["num_hidden_layers"] + n_nextn
+ # tensor_map was built from the old block_count in super().__init__(),
+ # so it must be rebuilt or every layer-42 tensor fails to map.
+ self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+ def _is_kda_layer(self, bid: int) -> bool:
+ """KDA everywhere except the last layer of each group, and the MTP head.
+
+ Mirrors modeling_bailing_moe_v3.py:1006 --
+ MLA if (layer_idx + 1) % layer_group_size == 0
+ or layer_idx >= num_hidden_layers // group_size * group_size
+ The second clause is what puts the MTP head (layer 42) on MLA.
+ """
+ group = self.hparams["layer_group_size"]
+ n_layer = self.hparams["num_hidden_layers"]
+ is_mla = ((bid + 1) % group == 0) or (bid >= n_layer // group * group)
+ return not is_mla
+
+ def set_gguf_parameters(self):
+ hparams = self.hparams
+
+ # MLA KV cache requires the attention be converted to MQA (1 KV group).
+ hparams["num_key_value_heads"] = 1
+
+ super().set_gguf_parameters()
+ self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+
+ assert hparams.get("no_kda_lora"), \
+ "no_kda_lora is false: this checkpoint uses low-rank KDA gates, which " \
+ "map to SSM_F_A/SSM_F_B (kimi-linear), not SSM_F/SSM_G"
+ assert hparams.get("kda_safe_gate"), \
+ "kda_safe_gate is false: llama.cpp's bailing-hybrid graph only implements " \
+ "the safe-gate decay form"
+
+ # Per-layer KV head count: 0 marks a KDA (recurrent) layer, which is how
+ # llama.cpp tells the two branches apart.
+ # NOTE: this array must be block_count long, NOT num_hidden_layers -- the
+ # loader validates it against n_layer_all and rejects the model outright
+ # if the MTP/nextn block has no entry. The MTP head is MLA, so it gets 1.
+ _num_kv_heads = [0 if self._is_kda_layer(il) else 1 for il in range(self.block_count)]
+ assert any(_num_kv_heads), "no MLA layers found -- layer_group_size indexing is wrong"
+ assert len(_num_kv_heads) == self.block_count
+ self.gguf_writer.add_head_count_kv(_num_kv_heads)
+ logger.info(f"bailing-hybrid: {sum(1 for x in _num_kv_heads if x)} MLA / "
+ f"{sum(1 for x in _num_kv_heads if not x)} KDA layers")
+
+ # ---- KDA ----
+ self.gguf_writer.add_ssm_conv_kernel(hparams["short_conv_kernel_size"])
+ self.gguf_writer.add_kda_head_dim(hparams["head_dim"])
+ self.gguf_writer.add_kda_lower_bound(float(hparams["kda_lower_bound"]))
+
+ # ---- MLA ----
+ # q_lora_rank is null (no Q compression), so add_q_lora_rank is skipped.
+ assert hparams.get("q_lora_rank") is None, \
+ "q_lora_rank is set: the graph builds a single wide q_proj and has no q_a/q_b path"
+ kv_lora_rank = hparams["kv_lora_rank"]
+ qk_rope_head_dim = hparams["qk_rope_head_dim"]
+ qk_nope_head_dim = hparams["qk_nope_head_dim"]
+ self.gguf_writer.add_kv_lora_rank(kv_lora_rank)
+ self.gguf_writer.add_rope_dimension_count(qk_rope_head_dim)
+ self.gguf_writer.add_key_length(kv_lora_rank + qk_rope_head_dim)
+ self.gguf_writer.add_key_length_mla(qk_nope_head_dim + qk_rope_head_dim)
+ self.gguf_writer.add_value_length_mla(hparams["v_head_dim"])
+
+ # ---- MoE (noaux_tc grouped top-k, bit-exact bailingmoe2) ----
+ self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
+ self.gguf_writer.add_expert_shared_count(hparams["num_shared_experts"])
+ self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
+ self.gguf_writer.add_expert_weights_scale(hparams["routed_scaling_factor"])
+ self.gguf_writer.add_expert_weights_norm(hparams["norm_topk_prob"])
+ self.gguf_writer.add_expert_group_count(hparams["n_group"])
+ self.gguf_writer.add_expert_group_used_count(hparams["topk_group"])
+
+ score = hparams.get("score_function", hparams.get("scoring_func"))
+ assert score == "sigmoid", f"unexpected router score function {score!r}"
+ self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
+
+ if (n_nextn := int(hparams.get("num_nextn_predict_layers", 0) or 0)) > 0 and not self.no_mtp:
+ self.gguf_writer.add_nextn_predict_layers(n_nextn)
+
+ def prepare_tensors(self):
+ super().prepare_tensors()
+ if self._experts is not None:
+ experts = [k for d in self._experts for k in d.keys()]
+ if len(experts) > 0:
+ raise ValueError(f"Unprocessed experts: {experts}")
+
+ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+ # KDA conv1d: HF [d_inner, d_conv] -> numpy (1, d_inner, 1, d_conv),
+ # which GGUF reverses into ggml ne = [d_conv, 1, d_inner, 1]. Memory
+ # layout is preserved either way (d_conv changes fastest).
+ if name.endswith((".q_conv1d.weight", ".k_conv1d.weight", ".v_conv1d.weight")):
+ if data_torch.ndim == 2:
+ d_inner, d_conv = data_torch.shape
+ data_torch = data_torch.reshape(1, d_inner, 1, d_conv)
+ elif data_torch.ndim == 3:
+ d_inner, _, d_conv = data_torch.shape
+ data_torch = data_torch.reshape(1, d_inner, 1, d_conv)
+
+ # A_log is 1-D [n_head] here (kimi's is [1,H,1,1], solar_open2's
+ # [1,1,64,1] -- third layout in three ports). Store +exp(A_log): the
+ # negation lives in kda_lower_bound, unlike kimi which bakes in -exp().
+ if name.endswith(".A_log"):
+ data_torch = torch.exp(data_torch.float())
+ data_torch = data_torch.reshape(1, 1, -1, 1)
+
+ if name.endswith(".dt_bias"):
+ name = name.rpartition(".dt_bias")[0] + ".dt_proj.bias"
+
+ # llama.cpp asks for `blk.N.exp_probs_b.bias`, but `mlp.gate.expert_bias`
+ # has no .weight/.bias suffix for map_tensor_name to strip, so it would be
+ # written as a bare `blk.N.exp_probs_b` and the load fails on a missing
+ # tensor. Same fix as bailingmoe/afmoe/grovemoe.
+ if name.endswith(".expert_bias"):
+ name = name.replace(".expert_bias", ".expert_bias.bias")
+
+ # Disambiguate the two g_proj tensors (see the class docstring).
+ if name.endswith(".attention.g_proj.weight"):
+ assert bid is not None
+ if self._is_kda_layer(bid):
+ name = name.replace(".attention.g_proj.", ".attention.g_full_proj.")
+
+ # merge the routed experts into one 3-D tensor per projection
+ if ".mlp.experts." in name:
+ n_experts = self.hparams["num_experts"]
+ assert bid is not None
+
+ if self._experts is None:
+ self._experts = [{} for _ in range(self.block_count)]
+
+ self._experts[bid][name] = data_torch
+
+ if len(self._experts[bid]) >= n_experts * 3:
+ for wid, tname in [("gate_proj", gguf.MODEL_TENSOR.FFN_GATE_EXP),
+ ("down_proj", gguf.MODEL_TENSOR.FFN_DOWN_EXP),
+ ("up_proj", gguf.MODEL_TENSOR.FFN_UP_EXP)]:
+ datas: list[Tensor] = []
+ for xid in range(n_experts):
+ ename = f"model.layers.{bid}.mlp.experts.{xid}.{wid}.weight"
+ datas.append(self._experts[bid][ename])
+ del self._experts[bid][ename]
+ data_torch = torch.stack(datas, dim=0)
+ new_name = self.format_tensor_name(tname, bid)
+ yield from super().modify_tensors(data_torch, new_name, bid)
+ return
+
+ # MLA absorption needs kv_b split, with k_b transposed
+ if name.endswith("kv_b_proj.weight"):
+ name_kb = name.replace("kv_b_proj", "k_b_proj")
+ name_vb = name.replace("kv_b_proj", "v_b_proj")
+ n_head_kv = self.hparams["num_attention_heads"]
+ v_head_dim = self.hparams["v_head_dim"]
+ qk_nope_head_dim = self.hparams["qk_nope_head_dim"]
+ assert data_torch.shape[0] == n_head_kv * (v_head_dim + qk_nope_head_dim)
+ kv_b = data_torch.view(n_head_kv, v_head_dim + qk_nope_head_dim, data_torch.shape[-1])
+ k_b, v_b = torch.split(kv_b, [qk_nope_head_dim, v_head_dim], dim=1)
+ k_b = k_b.transpose(1, 2)
+ yield from super().modify_tensors(k_b, name_kb, bid)
+ yield from super().modify_tensors(v_b, name_vb, bid)
+ return
+
+ yield from super().modify_tensors(data_torch, name, bid)
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 8516222cc..fb6b8a038 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -245,7 +245,9 @@ class Keys:
DT_B_C_RMS = "{arch}.ssm.dt_b_c_rms"
class KDA:
- HEAD_DIM = "{arch}.kda.head_dim"
+ HEAD_DIM = "{arch}.kda.head_dim"
+ # bailing-hybrid safe-gate: g = LOWER_BOUND * sigmoid(exp(A_log) * (f(x) + dt_bias))
+ LOWER_BOUND = "{arch}.kda.lower_bound"
class WKV:
HEAD_SIZE = "{arch}.wkv.head_size"
@@ -568,6 +570,7 @@ class MODEL_ARCH(IntEnum):
LLAMA_EMBED = auto()
MAINCODER = auto()
KIMI_LINEAR = auto()
+ BAILING_HYBRID = auto()
TALKIE = auto()
MELLUM = auto()
NANBEIGE = auto()
@@ -685,6 +688,8 @@ class MODEL_TENSOR(IntEnum):
SSM_BETA = auto() # Kimi Linear qwen3.5
SSM_G_A = auto() # Kimi Linear
SSM_G_B = auto() # Kimi Linear
+ SSM_F = auto() # bailing-hybrid (full-rank forget gate)
+ SSM_G = auto() # bailing-hybrid (full-rank output gate)
TIME_MIX_W0 = auto()
TIME_MIX_W1 = auto()
TIME_MIX_W2 = auto()
@@ -1240,6 +1245,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.LLAMA_EMBED: "llama-embed",
MODEL_ARCH.MAINCODER: "maincoder",
MODEL_ARCH.KIMI_LINEAR: "kimi-linear",
+ MODEL_ARCH.BAILING_HYBRID: "bailing-hybrid",
MODEL_ARCH.TALKIE: "talkie",
MODEL_ARCH.MELLUM: "mellum",
MODEL_ARCH.NANBEIGE: "nanbeige",
@@ -1355,6 +1361,8 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
MODEL_TENSOR.SSM_BETA: "blk.{bid}.ssm_beta", # Kimi Linear qwen3.5
MODEL_TENSOR.SSM_G_A: "blk.{bid}.ssm_g_a", # Kimi Linear
MODEL_TENSOR.SSM_G_B: "blk.{bid}.ssm_g_b", # Kimi Linear
+ MODEL_TENSOR.SSM_F: "blk.{bid}.ssm_f", # bailing-hybrid
+ MODEL_TENSOR.SSM_G: "blk.{bid}.ssm_g", # bailing-hybrid
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",
@@ -4749,6 +4757,47 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
+ MODEL_ARCH.BAILING_HYBRID: [
+ 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_GATE,
+ MODEL_TENSOR.ATTN_KV_A_MQA,
+ MODEL_TENSOR.ATTN_KV_A_NORM,
+ MODEL_TENSOR.ATTN_KV_B,
+ MODEL_TENSOR.ATTN_K_B,
+ MODEL_TENSOR.ATTN_V_B,
+ MODEL_TENSOR.SSM_CONV1D_Q,
+ MODEL_TENSOR.SSM_CONV1D_K,
+ MODEL_TENSOR.SSM_CONV1D_V,
+ MODEL_TENSOR.SSM_F,
+ MODEL_TENSOR.SSM_G,
+ MODEL_TENSOR.SSM_BETA,
+ MODEL_TENSOR.SSM_A,
+ MODEL_TENSOR.SSM_DT,
+ MODEL_TENSOR.SSM_NORM,
+ MODEL_TENSOR.FFN_NORM,
+ MODEL_TENSOR.FFN_GATE,
+ MODEL_TENSOR.FFN_DOWN,
+ MODEL_TENSOR.FFN_UP,
+ MODEL_TENSOR.FFN_GATE_INP,
+ MODEL_TENSOR.FFN_GATE_EXP,
+ MODEL_TENSOR.FFN_DOWN_EXP,
+ MODEL_TENSOR.FFN_UP_EXP,
+ MODEL_TENSOR.FFN_EXP_PROBS_B,
+ MODEL_TENSOR.FFN_GATE_SHEXP,
+ MODEL_TENSOR.FFN_DOWN_SHEXP,
+ MODEL_TENSOR.FFN_UP_SHEXP,
+ MODEL_TENSOR.NEXTN_EH_PROJ,
+ MODEL_TENSOR.NEXTN_ENORM,
+ MODEL_TENSOR.NEXTN_HNORM,
+ MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
+ ],
MODEL_ARCH.KIMI_LINEAR: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
index 39da9f2c0..ff6960091 100644
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -1091,6 +1091,9 @@ class GGUFWriter:
def add_kda_head_dim(self, value: int) -> None:
self.add_uint32(Keys.KDA.HEAD_DIM.format(arch=self.arch), value)
+ def add_kda_lower_bound(self, value: float) -> None:
+ self.add_float32(Keys.KDA.LOWER_BOUND.format(arch=self.arch), value)
+
def add_tokenizer_model(self, model: str) -> None:
self.add_string(Keys.Tokenizer.MODEL, model)
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index 7892342e4..5618ca1c1 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -269,6 +269,7 @@ class TensorNameMap:
"layers.{bid}.self_attn.q_proj", # qwen3-embedding
"backbone.layers.{bid}.mixer.q_proj", # nemotron-h
"model.blocks.{bid}.attn.attn_query", # talkie
+ "model.layers.{bid}.attention.q_proj", # bailing-hybrid
),
# Attention key
@@ -290,6 +291,7 @@ class TensorNameMap:
"layers.{bid}.self_attn.k_proj", # qwen3-embedding
"backbone.layers.{bid}.mixer.k_proj", # nemotron-h
"model.blocks.{bid}.attn.attn_key", # talkie
+ "model.layers.{bid}.attention.k_proj", # bailing-hybrid
),
# Attention value
@@ -310,6 +312,7 @@ class TensorNameMap:
"layers.{bid}.self_attn.v_proj", # qwen3-embedding
"backbone.layers.{bid}.mixer.v_proj", # nemotron-h
"model.blocks.{bid}.attn.attn_value", # talkie
+ "model.layers.{bid}.attention.v_proj", # bailing-hybrid
),
# Attention output
@@ -349,6 +352,7 @@ class TensorNameMap:
"backbone.layers.{bid}.mixer.o_proj", # nemotron-h
"model.layers.{bid}.self_attn.language_expert_dense", # cogvlm
"model.blocks.{bid}.attn.attn_resid", # talkie
+ "model.layers.{bid}.attention.o_proj", # bailing-hybrid
),
# Attention output norm
@@ -385,6 +389,7 @@ class TensorNameMap:
"model.layers.{bid}.self_attn.gate_proj", # afmoe
"model.layers.{bid}.linear_attn.in_proj_z", # qwen3.5
"model.layers.{bid}.self_attn.g_proj", # step3.5 head-wise attention gate
+ "model.layers.{bid}.attention.g_proj", # bailing-hybrid
),
# Feed-forward norm
@@ -832,6 +837,7 @@ class TensorNameMap:
"model.layers.{bid}.linear_attn.dt_proj", # qwen3next
"backbone.layers.{bid}.mixer.dt", # nemotron-h-moe
"model.layers.{bid}.self_attn.dt_proj", # kimi
+ "model.layers.{bid}.attention.dt_proj", # bailing-hybrid
),
MODEL_TENSOR.SSM_DT_NORM: (
@@ -846,6 +852,7 @@ class TensorNameMap:
"model.layers.layers.{bid}.mixer.A_log", # plamo2
"model.layers.{bid}.linear_attn.A_log", # qwen3next
"model.layers.{bid}.self_attn.A_log", # kimi
+ "model.layers.{bid}.attention.A_log", # bailing-hybrid
),
MODEL_TENSOR.SSM_B_NORM: (
@@ -872,6 +879,7 @@ class TensorNameMap:
"model.layers.{bid}.linear_attn.norm", # qwen3next
"backbone.layers.{bid}.mixer.norm", # mamba2
"model.layers.{bid}.self_attn.o_norm", # kimi
+ "model.layers.{bid}.attention.o_norm", # bailing-hybrid
),
MODEL_TENSOR.SSM_OUT: (
@@ -893,12 +901,15 @@ class TensorNameMap:
# Kimi Linear KDA (using SSM_ prefix for consistency)
MODEL_TENSOR.SSM_CONV1D_Q: (
"model.layers.{bid}.self_attn.q_conv1d",
+ "model.layers.{bid}.attention.q_conv1d", # bailing-hybrid
),
MODEL_TENSOR.SSM_CONV1D_K: (
"model.layers.{bid}.self_attn.k_conv1d",
+ "model.layers.{bid}.attention.k_conv1d", # bailing-hybrid
),
MODEL_TENSOR.SSM_CONV1D_V: (
"model.layers.{bid}.self_attn.v_conv1d",
+ "model.layers.{bid}.attention.v_conv1d", # bailing-hybrid
),
MODEL_TENSOR.SSM_F_A: (
"model.layers.{bid}.self_attn.f_a_proj",
@@ -909,6 +920,7 @@ class TensorNameMap:
MODEL_TENSOR.SSM_BETA: (
"model.layers.{bid}.linear_attn.in_proj_b", # qwen3.5
"model.layers.{bid}.self_attn.b_proj", # Kimi Linear
+ "model.layers.{bid}.attention.b_proj", # bailing-hybrid
),
MODEL_TENSOR.SSM_G_A: (
"model.layers.{bid}.self_attn.g_a_proj",
@@ -916,6 +928,12 @@ class TensorNameMap:
MODEL_TENSOR.SSM_G_B: (
"model.layers.{bid}.self_attn.g_b_proj",
),
+ MODEL_TENSOR.SSM_F: (
+ "model.layers.{bid}.attention.f_proj", # bailing-hybrid (full-rank)
+ ),
+ MODEL_TENSOR.SSM_G: (
+ "model.layers.{bid}.attention.g_full_proj", # bailing-hybrid (renamed by converter)
+ ),
MODEL_TENSOR.TIME_MIX_W0: (
"model.layers.{bid}.attention.w0", # rwkv7
),
@@ -1099,20 +1117,24 @@ class TensorNameMap:
MODEL_TENSOR.ATTN_KV_A_MQA: (
"model.layers.{bid}.self_attn.kv_a_proj_with_mqa", # deepseek2
"layers.{bid}.attention.wkv_a_with_mqa", # mistral-large
+ "model.layers.{bid}.attention.kv_a_proj_with_mqa", # bailing-hybrid
),
MODEL_TENSOR.ATTN_KV_B: (
"model.layers.{bid}.self_attn.kv_b_proj", # deepseek2
+ "model.layers.{bid}.attention.kv_b_proj", # bailing-hybrid
),
MODEL_TENSOR.ATTN_K_B: (
"model.layers.{bid}.self_attn.k_b_proj", # deepseek2
"layers.{bid}.attention.k_b_proj", # mistral-large
+ "model.layers.{bid}.attention.k_b_proj", # bailing-hybrid
),
MODEL_TENSOR.ATTN_V_B: (
"model.layers.{bid}.self_attn.v_b_proj", # deepseek2
"layers.{bid}.attention.v_b_proj", # mistral-large
+ "model.layers.{bid}.attention.v_b_proj", # bailing-hybrid
),
MODEL_TENSOR.ATTN_Q_A_NORM: (
@@ -1123,6 +1145,7 @@ class TensorNameMap:
MODEL_TENSOR.ATTN_KV_A_NORM: (
"model.layers.{bid}.self_attn.kv_a_layernorm", # deepseek2
"layers.{bid}.attention.kv_a_norm", # mistral-large
+ "model.layers.{bid}.attention.kv_a_layernorm", # bailing-hybrid
),
MODEL_TENSOR.ATTN_SUB_NORM: (
@@ -2564,6 +2587,7 @@ class TensorNameMap:
MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM: (
"model.layers.{bid}.shared_head.norm",
+ "model.layers.{bid}.final_layernorm", # bailing-hybrid
),
}
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index 836cfade2..a942d3bf0 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -141,6 +141,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_LLAMA_EMBED, "llama-embed" },
{ LLM_ARCH_MAINCODER, "maincoder" },
{ LLM_ARCH_KIMI_LINEAR, "kimi-linear" },
+ { LLM_ARCH_BAILING_HYBRID, "bailing-hybrid" },
{ LLM_ARCH_TALKIE, "talkie" },
{ LLM_ARCH_MELLUM, "mellum" },
{ LLM_ARCH_NANBEIGE, "nanbeige" },
@@ -303,7 +304,8 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_SSM_GROUP_COUNT, "%s.ssm.group_count" },
{ LLM_KV_SSM_DT_B_C_RMS, "%s.ssm.dt_b_c_rms" },
- { LLM_KV_KDA_HEAD_DIM, "%s.kda.head_dim" },
+ { LLM_KV_KDA_HEAD_DIM, "%s.kda.head_dim" },
+ { LLM_KV_KDA_LOWER_BOUND, "%s.kda.lower_bound" },
{ LLM_KV_WKV_HEAD_SIZE, "%s.wkv.head_size" },
@@ -455,6 +457,8 @@ static const std::map<llm_tensor, const char *> LLM_TENSOR_NAMES = {
{ LLM_TENSOR_SSM_BETA, "blk.%d.ssm_beta" },
{ LLM_TENSOR_SSM_G_A, "blk.%d.ssm_g_a" },
{ LLM_TENSOR_SSM_G_B, "blk.%d.ssm_g_b" },
+ { LLM_TENSOR_SSM_F, "blk.%d.ssm_f" },
+ { LLM_TENSOR_SSM_G, "blk.%d.ssm_g" },
{ LLM_TENSOR_SSM_NORM, "blk.%d.ssm_norm" },
{ LLM_TENSOR_ATTN_Q_A_NORM, "blk.%d.attn_q_a_norm" },
{ LLM_TENSOR_ATTN_KV_A_NORM, "blk.%d.attn_kv_a_norm" },
@@ -748,6 +752,8 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
{LLM_TENSOR_SSM_BETA, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_SSM_G_A, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_SSM_G_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+ {LLM_TENSOR_SSM_F, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+ {LLM_TENSOR_SSM_G, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_TIME_MIX_LERP_X, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{LLM_TENSOR_TIME_MIX_LN, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{LLM_TENSOR_CHANNEL_MIX_LERP_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
@@ -967,6 +973,7 @@ bool llm_arch_is_hybrid(const llm_arch & arch) {
case LLM_ARCH_NEMOTRON_H_MOE:
case LLM_ARCH_QWEN3NEXT:
case LLM_ARCH_KIMI_LINEAR:
+ case LLM_ARCH_BAILING_HYBRID:
case LLM_ARCH_QWEN35:
case LLM_ARCH_QWEN35MOE:
case LLM_ARCH_DEEPSEEK4:
@@ -1027,6 +1034,7 @@ bool llm_arch_supports_sm_tensor(const llm_arch & arch) {
case LLM_ARCH_MINIMAX_M3:
case LLM_ARCH_MISTRAL4:
case LLM_ARCH_KIMI_LINEAR:
+ case LLM_ARCH_BAILING_HYBRID:
case LLM_ARCH_QWEN3TTS:
return false;
default:
diff --git a/src/llama-arch.h b/src/llama-arch.h
index 49c2a6ac3..e16e16a8c 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -143,6 +143,7 @@ enum llm_arch {
LLM_ARCH_LLAMA_EMBED,
LLM_ARCH_MAINCODER,
LLM_ARCH_KIMI_LINEAR,
+ LLM_ARCH_BAILING_HYBRID,
LLM_ARCH_TALKIE,
LLM_ARCH_MELLUM,
LLM_ARCH_EAGLE3,
@@ -309,6 +310,7 @@ enum llm_kv {
LLM_KV_SSM_DT_B_C_RMS,
LLM_KV_KDA_HEAD_DIM,
+ LLM_KV_KDA_LOWER_BOUND,
LLM_KV_WKV_HEAD_SIZE,
@@ -483,6 +485,8 @@ enum llm_tensor {
LLM_TENSOR_SSM_BETA, // kimi: beta mixing coefficient and qwen3.5
LLM_TENSOR_SSM_G_A, // kimi: output gate projection A
LLM_TENSOR_SSM_G_B, // kimi: output gate projection B
+ LLM_TENSOR_SSM_F, // bailing-hybrid: full-rank forget gate (no_kda_lora)
+ LLM_TENSOR_SSM_G, // bailing-hybrid: full-rank output gate (no_kda_lora)
LLM_TENSOR_TIME_MIX_W0,
LLM_TENSOR_TIME_MIX_W1,
LLM_TENSOR_TIME_MIX_W2,
diff --git a/src/llama-hparams.h b/src/llama-hparams.h
index 6e8336c98..ac32aaa27 100644
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -163,6 +163,10 @@ struct llama_hparams {
// for Kimi Linear KDA
uint32_t n_embd_head_kda = 0;
+ // bailing-hybrid KDA safe gate. 0.0f means "not a safe-gate model", i.e. use
+ // the kimi form g = -exp(A_log)*softplus(.) instead.
+ float f_kda_lower_bound = 0.0f;
+
bool ssm_dt_b_c_rms = false;
float f_clamp_kqv = 0.0f;
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index dda311c47..02ed42bcc 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -312,6 +312,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
return new llama_model_mimo2(params);
case LLM_ARCH_KIMI_LINEAR:
return new llama_model_kimi_linear(params);
+ case LLM_ARCH_BAILING_HYBRID:
+ return new llama_model_bailing_hybrid(params);
case LLM_ARCH_STEP35:
return new llama_model_step35(params);
default:
@@ -2572,6 +2574,11 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
return LLAMA_ROPE_TYPE_NONE;
// use what we call a normal RoPE, operating on pairs of consecutive head values
+ // bailing-hybrid: config rope_interleave=true. The reference de-interleaves
+ // (view(d/2,2).transpose) before a rotate_half, which is exactly pairwise
+ // rotation in stored order -- i.e. NORM, not the NEOX that DeepSeek-style
+ // MLA normally uses. The non-interleaved branch upstream is a literal 1/0.
+ case LLM_ARCH_BAILING_HYBRID:
case LLM_ARCH_LLAMA:
case LLM_ARCH_LLADA:
case LLM_ARCH_LLAMA4:
diff --git a/src/llama-model.h b/src/llama-model.h
index 6b9e94a0a..8c50adb2c 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -510,6 +510,11 @@ struct llama_layer {
struct ggml_tensor * ssm_g_b = nullptr;
struct ggml_tensor * ssm_o_norm = nullptr;
+ // full-rank KDA forget/output gates (bailing-hybrid, no_kda_lora=true):
+ // single matmuls that replace the ssm_{f,g}_{a,b} low-rank pairs above
+ struct ggml_tensor * ssm_f = nullptr;
+ struct ggml_tensor * ssm_g = nullptr;
+
// DSA (deepseek sparse attention)
struct ggml_tensor * indexer_k_norm = nullptr;
struct ggml_tensor * indexer_k_norm_b = nullptr;
diff --git a/src/models/bailing-hybrid.cpp b/src/models/bailing-hybrid.cpp
new file mode 100644
index 000000000..a625412f0
--- /dev/null
+++ b/src/models/bailing-hybrid.cpp
@@ -0,0 +1,575 @@
+#include "models.h"
+#include "llama-memory-recurrent.h"
+
+// Ling 3.0 flash (inclusionAI/Ling-3.0-flash) -- model_type "bailing_hybrid",
+// BailingMoeV3ForCausalLM. 127.5B total / 5.1B active.
+//
+// 42 layers: 35 KDA (Kimi Delta Attention) + 7 gated MLA, MLA at every layer
+// where (il + 1) % layer_group_size == 0 with layer_group_size 6, i.e. layers
+// 5/11/17/23/29/35/41 -- MLA is LAST in each group (solar_open2 is the
+// opposite, softmax-first). Layer 42 is an MTP/nextn head and is skipped.
+//
+// Derived from src/models/kimi-linear.cpp, which already has both halves: the
+// KDA block, and MLA without Q compression at exactly this geometry
+// (qk_rope 64 / qk_nope 128 / qk_head 192). The MoE router is bit-exact
+// bailingmoe2 (noaux_tc grouped top-k + sigmoid + expert_bias), which
+// build_moe_ffn handles from hparams with no code here.
+//
+// Deltas against kimi-linear, all verified against modeling_bailing_moe_v3.py
+// and the fla kernels rather than inferred:
+//
+// 1. SAFE GATE. config kda_safe_gate=true, kda_lower_bound=-5.0 replaces
+// g = -exp(A_log) * softplus(f(x) + dt_bias) [kimi]
+// with
+// g = lower_bound * sigmoid(exp(A_log) * (f(x) + dt_bias))
+// Confirmed identical in fla's naive reference (ops/kda/gate.py:57-69) and
+// its Triton kernel (ops/kda/gate.py:116-119). Because g is built here and
+// handed to GGML_OP_GATED_DELTA_NET as an input, the kernel is untouched --
+// same shape of fix as solar_open2's beta = 2*sigmoid(.). Note the
+// converter must store +exp(A_log), NOT kimi's -exp(A_log): the sign now
+// lives in lower_bound. Getting this wrong is a silent quality
+// regression, never a crash.
+//
+// 2. no_kda_lora=true -> f_proj / g_proj are FULL-RANK {n_embd, d_inner}
+// single matmuls, not kimi's low-rank f_a/f_b, g_a/g_b pairs.
+//
+// 3. A_log is 1-D [n_head]. Kimi's is [1,H,1,1], solar_open2's is [1,1,64,1]
+// -- third layout in three ports. The converter reshapes it.
+//
+// 4. MLA carries a HEAD-WISE sigmoid output gate: g_proj {n_embd, n_head},
+// one scalar per head broadcast across v_head_dim, applied to the SDPA
+// result before dense/o_proj. solar_open2's gate is elementwise and
+// full-width -- do not copy that broadcast.
+//
+// 5. MLA USES RoPE, unlike kimi (rotary_emb=None there). rope_interleave=true
+// resolves to llama.cpp's NORM rope: the reference de-interleaves with
+// view(d/2,2).transpose before a rotate_half, which is pairwise rotation
+// in stored order. theta 6e6 over the 64-dim rope slice only.
+//
+// Vestigial config fields, verified unreferenced by grepping the reference:
+// expert_swiglu_limit_list / share_expert_swiglu_limit_list (BailingMoeV3MLP is
+// a plain SwiGLU -- these are populated with non-zero values for the last few
+// layers and are still dead), use_qk_norm, linear_silu, max_window_layers,
+// mtp_use_kda, use_mla_nope, use_nGPT, scale_router_input, seq_aux.
+// partial_rotary_factor is overwritten to 1.0 by the rotary module itself.
+
+void llama_model_bailing_hybrid::load_arch_hparams(llama_model_loader & ml) {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+ ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA, hparams.n_embd_head_k_mla_impl);
+ ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla_impl);
+ ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);
+ ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv);
+ ml.get_key(LLM_KV_KDA_HEAD_DIM, hparams.n_embd_head_kda);
+ ml.get_key(LLM_KV_KDA_LOWER_BOUND, hparams.f_kda_lower_bound, false);
+
+ // KDA layers are marked with n_head_kv == 0 (same convention as Kimi Linear,
+ // solar_open2 and Jamba); MLA layers carry the real KV head count, which the
+ // converter forces to 1 so the MLA KV cache can be used.
+ for (uint32_t i = 0; i < hparams.n_layer(); ++i) {
+ hparams.is_recr_impl[i] = hparams.n_head_kv(i) == 0;
+ }
+
+ ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
+ ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
+ ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false);
+ ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
+ ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
+ ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func);
+ ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
+
+ GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_all");
+
+ // The safe gate is what this arch is; a GGUF without it was converted by
+ // something that did not understand the model.
+ GGML_ASSERT(hparams.f_kda_lower_bound < 0.0f &&
+ "bailing-hybrid requires a negative kda.lower_bound (safe gate); re-convert this model");
+
+ switch (hparams.n_layer()) {
+ case 42: type = LLM_TYPE_A13B; break; // Ling-3.0-flash 127.5B-A5.1B
+ default: type = LLM_TYPE_UNKNOWN;
+ }
+}
+
+void llama_model_bailing_hybrid::load_arch_tensors(llama_model_loader &) {
+ LLAMA_LOAD_LOCALS;
+
+ tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+ output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+ output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
+
+ const int64_t head_dim_kda = hparams.n_embd_head_kda; // 128
+ const int64_t ssm_d_conv = hparams.ssm_d_conv; // 4
+ const int64_t d_inner = head_dim_kda * n_head; // 32 * 128 = 4096
+
+ for (int i = 0; i < n_layer_all; ++i) {
+ // The MTP/nextn head (layer 42) ships in the checkpoint but is not part
+ // of the main forward pass -- allocate nothing for it.
+ const int flags = (i >= n_layer) ? TENSOR_SKIP : 0;
+
+ auto & layer = layers[i];
+
+ layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
+
+ if (i < n_layer && hparams.is_recr(i)) {
+ // ---- KDA linear-attention layer ----
+ // conv1d weights are 4D in the GGUF but quantisation may drop the
+ // trailing 1, so accept 3D too (same dance as kimi-linear.cpp).
+ layer.ssm_q_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_Q, "weight", i), {ssm_d_conv, 1, d_inner, 1}, TENSOR_NOT_REQUIRED);
+ if (!layer.ssm_q_conv) {
+ layer.ssm_q_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_Q, "weight", i), {ssm_d_conv, 1, d_inner}, 0);
+ }
+ layer.ssm_k_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_K, "weight", i), {ssm_d_conv, 1, d_inner, 1}, TENSOR_NOT_REQUIRED);
+ if (!layer.ssm_k_conv) {
+ layer.ssm_k_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_K, "weight", i), {ssm_d_conv, 1, d_inner}, 0);
+ }
+ layer.ssm_v_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_V, "weight", i), {ssm_d_conv, 1, d_inner, 1}, TENSOR_NOT_REQUIRED);
+ if (!layer.ssm_v_conv) {
+ layer.ssm_v_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_V, "weight", i), {ssm_d_conv, 1, d_inner}, 0);
+ }
+
+ // num_kv_heads_for_linear_attn = 0 => K is full width, like Q/V
+ create_tensor_qkv(layer, i, n_embd, d_inner, d_inner, d_inner, 0);
+
+ // full-rank forget/output gates (no_kda_lora = true)
+ layer.ssm_f = create_tensor(tn(LLM_TENSOR_SSM_F, "weight", i), {n_embd, d_inner}, 0);
+ layer.ssm_g = create_tensor(tn(LLM_TENSOR_SSM_G, "weight", i), {n_embd, d_inner}, 0);
+
+ layer.ssm_beta = create_tensor(tn(LLM_TENSOR_SSM_BETA, "weight", i), {n_embd, n_head}, 0);
+
+ // stored as +exp(A_log) by the converter; the negation lives in
+ // kda.lower_bound. Converter emits ggml ne = [1, n_head, 1, 1].
+ layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_head, 1, 1}, TENSOR_NOT_REQUIRED);
+ if (!layer.ssm_a) {
+ layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_head}, 0);
+ }
+
+ layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner}, 0);
+
+ layer.ssm_o_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {head_dim_kda}, 0);
+
+ layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {d_inner, n_embd}, 0);
+ } else {
+ // ---- gated MLA layer (also the shape of the skipped MTP head) ----
+ const int64_t kv_lora_rank = hparams.n_lora_kv;
+ const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla(); // 192
+ const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla(); // 128
+ const int64_t qk_rope_head_dim = hparams.n_rot(); // 64
+
+ // q_lora_rank is null in config => no Q compression, one wide q_proj
+ layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_head * n_embd_head_k_mla}, flags);
+
+ layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, flags);
+ layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + qk_rope_head_dim}, flags);
+
+ // legacy GGUFs keep kv_b fused (MLA KV cache disabled)
+ layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i),
+ {kv_lora_rank, n_head * (n_embd_head_k_mla - qk_rope_head_dim + n_embd_head_v_mla)},
+ flags | TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL);
+ if (!layer.wkv_b) {
+ layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_k_mla - qk_rope_head_dim, kv_lora_rank, n_head}, flags);
+ layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, flags);
+ }
+
+ // head-wise gate: ONE scalar per head, not per output element
+ layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_head}, flags);
+
+ layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_embd_head_v_mla, n_embd}, flags);
+ }
+
+ layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
+
+ const int64_t n_ff_exp = hparams.n_ff_exp;
+
+ if ((uint32_t) i < hparams.n_layer_dense_lead) {
+ // first_k_dense_replace = 2 -> layers 0 and 1 are dense
+ layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, flags);
+ layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, flags);
+ layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, flags);
+ } else {
+ layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, flags);
+ layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, flags);
+ layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, flags);
+ layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, flags);
+
+ const int64_t n_ff_shexp = n_ff_exp * (hparams.n_expert_shared > 0 ? hparams.n_expert_shared : 1);
+ layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, flags);
+ layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, flags);
+ layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_shexp}, flags);
+
+ layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, flags);
+ }
+
+ // MTP/nextn head: preserved but unused. These MUST be created even though
+ // nothing reads them -- the loader throws if n_created < n_tensors, so an
+ // unclaimed tensor in the GGUF fails the load outright. Ling has no nextn
+ // embed_tokens / shared_head.head (it borrows the main model's), and names
+ // its final norm `final_layernorm` -> NEXTN_SHARED_HEAD_NORM.
+ if (i >= n_layer) {
+ layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), {2 * n_embd, n_embd}, flags);
+ layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), {n_embd}, flags);
+ layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), {n_embd}, flags);
+ layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), {n_embd}, flags);
+ }
+ }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_bailing_hybrid::build_arch_graph(const llm_graph_params & params) const {
+ return std::make_unique<graph>(*this, params);
+}
+
+// Causal conv1d over Q/K/V. Copied from kimi-linear.cpp -- qkv selects which of
+// the three conv states to read/write (0=Q, 1=K, 2=V).
+static ggml_tensor * causal_conv1d(ggml_cgraph * gf, ggml_context * ctx0, ggml_tensor * conv_states_all,
+ ggml_tensor * conv_state_all, int64_t qkv, ggml_tensor * x, ggml_tensor * proj_w, ggml_tensor * conv_w,
+ int64_t d_conv, int64_t head_dim, int64_t n_head, int64_t n_seq_tokens, int64_t n_seqs,
+ int64_t n_tokens, int64_t kv_head) {
+ const int64_t d_inner = head_dim * n_head;
+ const int64_t conv_state_size = (d_conv - 1) * d_inner;
+ const int64_t n_embd_r_total = 3 * conv_state_size; // Q + K + V
+
+ ggml_tensor * conv_state_x = ggml_view_3d(ctx0, conv_state_all, d_conv - 1, d_inner, n_seqs,
+ (d_conv - 1) * ggml_element_size(conv_state_all),
+ n_embd_r_total * ggml_element_size(conv_state_all),
+ qkv * conv_state_size * ggml_element_size(conv_state_all));
+
+ ggml_tensor * x_proj = ggml_mul_mat(ctx0, proj_w, x);
+ ggml_tensor * x_3d = ggml_reshape_3d(ctx0, x_proj, d_inner, n_seq_tokens, n_seqs);
+
+ ggml_tensor * conv_x = ggml_concat(ctx0, conv_state_x, ggml_transpose(ctx0, x_3d), 0);
+
+ ggml_tensor * last_conv_x = ggml_view_3d(ctx0, conv_x, d_conv - 1, d_inner, n_seqs,
+ conv_x->nb[1], conv_x->nb[2], n_seq_tokens * conv_x->nb[0]);
+ ggml_build_forward_expand(gf,
+ ggml_cpy(ctx0, last_conv_x,
+ ggml_view_3d(ctx0, conv_states_all, d_conv - 1, d_inner, n_seqs,
+ (d_conv - 1) * ggml_element_size(conv_states_all),
+ n_embd_r_total * ggml_element_size(conv_states_all),
+ (kv_head * n_embd_r_total + qkv * conv_state_size) * ggml_element_size(conv_states_all))));
+
+ ggml_tensor * conv_weight = ggml_reshape_2d(ctx0, conv_w, d_conv, d_inner);
+
+ ggml_tensor * Xcur = ggml_ssm_conv(ctx0, conv_x, conv_weight);
+ Xcur = ggml_reshape_2d(ctx0, Xcur, d_inner, n_tokens);
+ Xcur = ggml_silu(ctx0, Xcur);
+
+ return ggml_reshape_4d(ctx0, Xcur, head_dim, n_head, n_seq_tokens, n_seqs);
+}
+
+llama_model_bailing_hybrid::graph::graph(const llama_model & model, const llm_graph_params & params) :
+ llm_build_delta_net_base(params), model(model) {
+ ggml_tensor * cur;
+ ggml_tensor * inpL;
+
+ inpL = build_inp_embd(model.tok_embd);
+ cb(inpL, "model.embed_tokens", -1);
+
+ // MLA layers are RoPE'd (unlike kimi-linear), so positions are needed.
+ ggml_tensor * inp_pos = build_inp_pos();
+
+ auto * inp_kv = !hparams.is_mla() ? build_inp_mem_hybrid() : nullptr;
+ auto * inp_k = hparams.is_mla() ? build_inp_mem_hybrid_k() : nullptr;
+ auto * inp_rs = hparams.is_mla() ? inp_k->get_recr() : inp_kv->get_recr();
+ auto * inp_attn_kv = !hparams.is_mla() ? inp_kv->get_attn() : nullptr;
+ auto * inp_attn_k = hparams.is_mla() ? inp_k->get_attn() : nullptr;
+
+ ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+ const int64_t n_head = hparams.n_head();
+ const int64_t head_dim = hparams.n_embd_head_kda;
+ const int64_t d_conv = hparams.ssm_d_conv;
+ const int64_t d_inner = n_head * head_dim;
+ const int64_t n_seqs = ubatch.n_seqs;
+ const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+ GGML_ASSERT(n_seqs != 0);
+ GGML_ASSERT(ubatch.equal_seqs());
+ GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
+
+ const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla(); // 192
+ const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla(); // 128
+ const int64_t kv_lora_rank = hparams.n_lora_kv; // 512
+ const int64_t n_embd_head_qk_rope = hparams.n_rot(); // 64
+ const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope; // 128
+
+ // scaling = qk_head_dim ** -0.5 over the FULL 192, not the nope part
+ const float kq_scale_mla = 1.0f / sqrtf((float) n_embd_head_k_mla);
+
+ const float kda_lower_bound = hparams.f_kda_lower_bound;
+
+ // NORM rope over the 64-dim rope slice only -- see the header comment.
+ const int rope_type = LLAMA_ROPE_TYPE_NORM;
+ const int n_rot = n_embd_head_qk_rope;
+ const float freq_base = hparams.rope_freq_base_train;
+ const float freq_scale = hparams.rope_freq_scale_train;
+ const float ext_factor = cparams.yarn_ext_factor;
+ const float attn_factor = cparams.yarn_attn_factor;
+ const float beta_fast = cparams.yarn_beta_fast;
+ const float beta_slow = cparams.yarn_beta_slow;
+ const int n_ctx_orig = cparams.n_ctx_orig_yarn;
+
+ for (int il = 0; il < n_layer; ++il) {
+ const auto & layer = model.layers[il];
+ ggml_tensor * inpSA = inpL;
+
+ cur = build_norm(inpL, layer.attn_norm, NULL, LLM_NORM_RMS, il);
+ cb(cur, "attn_norm", il);
+
+ ggml_build_forward_expand(gf, cur);
+
+ if (hparams.is_recr(il)) {
+ // ================= KDA linear-attention layer =================
+ const auto * mctx_cur = inp_rs->mctx;
+ const auto kv_head = mctx_cur->get_head();
+
+ ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
+ cb(conv_states_all, "conv_states_all", il);
+ ggml_tensor * conv_state_all = build_rs(inp_rs, conv_states_all, hparams.n_embd_r(), n_seqs);
+
+ ggml_tensor * Qcur = causal_conv1d(gf, ctx0, conv_states_all, conv_state_all, 0, cur, layer.wq, layer.ssm_q_conv, d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, kv_head);
+ ggml_tensor * Kcur = causal_conv1d(gf, ctx0, conv_states_all, conv_state_all, 1, cur, layer.wk, layer.ssm_k_conv, d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, kv_head);
+ ggml_tensor * Vcur = causal_conv1d(gf, ctx0, conv_states_all, conv_state_all, 2, cur, layer.wv, layer.ssm_v_conv, d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, kv_head);
+
+ // *** delta 1+2 vs kimi-linear ***
+ // full-rank f_proj (one matmul, not f_b(f_a(x))), then the safe gate
+ // g = lower_bound * sigmoid(exp(A_log) * (f(x) + dt_bias))
+ // ssm_a already holds +exp(A_log). dt_bias is added BEFORE the
+ // per-head A scaling and before the sigmoid -- fla adds the bias to
+ // the raw projection, then multiplies inside the sigmoid.
+ ggml_tensor * g1 = ggml_mul_mat(ctx0, layer.ssm_f, cur);
+ g1 = ggml_add(ctx0, g1, layer.ssm_dt_b);
+ g1 = ggml_reshape_3d(ctx0, g1, head_dim, n_head, n_tokens);
+
+ // A is per-head: [1, n_head, 1] broadcast over head_dim and tokens
+ ggml_tensor * A = ggml_reshape_3d(ctx0, layer.ssm_a, 1, n_head, 1);
+ g1 = ggml_mul(ctx0, g1, A);
+ g1 = ggml_sigmoid(ctx0, g1);
+ g1 = ggml_scale(ctx0, g1, kda_lower_bound);
+ cb(g1, "kda_g1_safe_gate", il);
+
+ g1 = ggml_reshape_4d(ctx0, g1, head_dim, n_head, n_seq_tokens, n_seqs);
+
+ // allow_neg_eigval is off here: plain sigmoid, no 2x (that is
+ // solar_open2's delta, not this model's).
+ ggml_tensor * beta = ggml_mul_mat(ctx0, layer.ssm_beta, cur);
+ beta = ggml_reshape_4d(ctx0, beta, 1, n_head, n_seq_tokens, n_seqs);
+ beta = ggml_sigmoid(ctx0, beta);
+ cb(beta, "kda_beta", il);
+
+ cur = ggml_reshape_3d(ctx0, cur, cur->ne[0], n_seq_tokens, n_seqs);
+
+ ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il);
+ ggml_tensor * state = build_rs(inp_rs, ssm_states_all, hparams.n_embd_s(), n_seqs);
+ state = ggml_reshape_4d(ctx0, state, head_dim, head_dim, n_head, n_seqs);
+
+ const float eps_norm = hparams.f_norm_rms_eps;
+ Qcur = ggml_l2_norm(ctx0, Qcur, eps_norm);
+ Kcur = ggml_l2_norm(ctx0, Kcur, eps_norm);
+
+ auto attn_out = build_delta_net(Qcur, Kcur, Vcur, g1, beta, state, il);
+
+ ggml_tensor * output = ggml_cont(ctx0, attn_out.first);
+ ggml_tensor * new_state = attn_out.second;
+
+ ggml_build_forward_expand(gf,
+ ggml_cpy(ctx0, new_state,
+ ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs,
+ kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
+
+ // full-rank output gate, then RMSNorm(x) * sigmoid(g)
+ ggml_tensor * cur_2d = ggml_reshape_2d(ctx0, cur, cur->ne[0], n_seq_tokens * n_seqs);
+ ggml_tensor * g2 = ggml_mul_mat(ctx0, layer.ssm_g, cur_2d);
+ g2 = ggml_reshape_3d(ctx0, g2, head_dim, n_head, n_seq_tokens * n_seqs);
+
+ ggml_tensor * attn_out_final = ggml_reshape_3d(ctx0, output, head_dim, n_head, n_seq_tokens * n_seqs);
+ ggml_tensor * normed = build_norm(attn_out_final, layer.ssm_o_norm, nullptr, LLM_NORM_RMS, il);
+ ggml_tensor * gated = ggml_mul(ctx0, normed, ggml_sigmoid(ctx0, g2));
+
+ gated = ggml_cont_2d(ctx0, gated, d_inner, n_tokens);
+ cur = ggml_mul_mat(ctx0, layer.wo, gated);
+ cb(cur, "kda_out", il);
+ } else {
+ // ================= gated MLA layer =================
+ // q_proj is one wide matmul (q_lora_rank is null). Per head the
+ // layout is [nope(128) | rope(64)], matching the reference's
+ // split(q, [qk_nope_head_dim, qk_rope_head_dim], dim=-1).
+ ggml_tensor * Qcur = ggml_mul_mat(ctx0, layer.wq, cur);
+
+ ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, layer.wkv_a_mqa, cur);
+
+ ggml_tensor * kv_cmpr = ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens,
+ ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0);
+ ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens,
+ ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
+ ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
+ ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));
+
+ // *** delta 5: kimi applies no RoPE here; this model does ***
+ k_pe = ggml_rope_ext(ctx0, ggml_cont(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);
+ cb(k_pe, "k_pe", il);
+
+ kv_cmpr = build_norm(kv_cmpr, layer.attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);
+
+ ggml_tensor * attn_out = nullptr;
+
+ if (layer.wk_b && layer.wv_b) { // MLA KV cache enabled
+ ggml_tensor * q_nope =
+ ggml_view_3d(ctx0, Qcur, n_embd_head_qk_nope, n_head, n_tokens,
+ ggml_row_size(Qcur->type, n_embd_head_k_mla),
+ ggml_row_size(Qcur->type, n_embd_head_k_mla) * n_head, 0);
+
+ ggml_tensor * q_pe = ggml_view_3d(
+ ctx0, Qcur, n_embd_head_qk_rope, n_head, n_tokens,
+ ggml_row_size(Qcur->type, n_embd_head_k_mla),
+ ggml_row_size(Qcur->type, n_embd_head_k_mla) * n_head,
+ ggml_row_size(Qcur->type, n_embd_head_qk_nope));
+
+ q_pe = ggml_rope_ext(ctx0, ggml_cont(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);
+ cb(q_pe, "q_pe", il);
+
+ // {n_embd_head_qk_nope, n_tokens, n_head}
+ q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
+
+ ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, layer.wk_b, q_nope);
+ q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);
+
+ // note: rope must go first for in-place context shifting
+ Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0);
+ cb(Qcur, "Qcur", il);
+
+ kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens);
+
+ ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0);
+ ggml_tensor * Vcur = kv_cmpr;
+
+ // wo is applied after the head-wise gate, so pass null here
+ attn_out = build_attn(inp_attn_k, nullptr, NULL, layer.wo_s,
+ Qcur, Kcur, Vcur, nullptr, nullptr, layer.wv_b, kq_scale_mla, il);
+ } else { // MLA KV cache disabled -- fall back to MHA
+ ggml_tensor * q_pe = ggml_view_3d(
+ ctx0, Qcur, n_embd_head_qk_rope, n_head, n_tokens,
+ ggml_row_size(Qcur->type, n_embd_head_k_mla),
+ ggml_row_size(Qcur->type, n_embd_head_k_mla) * n_head,
+ ggml_row_size(Qcur->type, n_embd_head_qk_nope));
+ q_pe = ggml_rope_ext(ctx0, ggml_cont(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);
+
+ ggml_tensor * q_nope =
+ ggml_view_3d(ctx0, Qcur, n_embd_head_qk_nope, n_head, n_tokens,
+ ggml_row_size(Qcur->type, n_embd_head_k_mla),
+ ggml_row_size(Qcur->type, n_embd_head_k_mla) * n_head, 0);
+
+ // rebuild Q as [nope | rope] to match the K layout below
+ Qcur = ggml_concat(ctx0, ggml_cont(ctx0, q_nope), q_pe, 0);
+
+ ggml_tensor * kv = ggml_mul_mat(ctx0, layer.wkv_b, kv_cmpr);
+ const int64_t kv_per_head = n_embd_head_qk_nope + n_embd_head_v_mla;
+
+ ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens,
+ ggml_row_size(kv->type, kv_per_head),
+ ggml_row_size(kv->type, kv_per_head * n_head), 0);
+ ggml_tensor * Vcur = ggml_view_3d(ctx0, kv, n_embd_head_v_mla, n_head, n_tokens,
+ ggml_row_size(kv->type, kv_per_head),
+ ggml_row_size(kv->type, kv_per_head * n_head),
+ ggml_row_size(kv->type, n_embd_head_qk_nope));
+ Vcur = ggml_cont(ctx0, Vcur);
+
+ // k_pe is shared across heads (MQA) -> broadcast before concat
+ ggml_tensor * k_pe_target = ggml_new_tensor_3d(ctx0, k_pe->type, n_embd_head_qk_rope, n_head, n_tokens);
+ ggml_tensor * k_pe_repeated = ggml_repeat(ctx0, k_pe, k_pe_target);
+ ggml_tensor * Kcur = ggml_concat(ctx0, ggml_cont(ctx0, k_nope), k_pe_repeated, 0);
+
+ attn_out = build_attn(inp_attn_kv, nullptr, NULL, layer.wo_s,
+ Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale_mla, il);
+ }
+ cb(attn_out, "attn_out", il);
+
+ // *** delta 4: HEAD-WISE sigmoid gate ***
+ // g_proj is {n_embd, n_head}: one scalar per head, broadcast across
+ // v_head_dim. Reshaping attn_out to [head_dim, n_head, n_tokens] and
+ // the gate to [1, n_head, n_tokens] makes ggml_mul do that broadcast.
+ ggml_tensor * gate = ggml_mul_mat(ctx0, layer.wqkv_gate, cur);
+ gate = ggml_sigmoid(ctx0, gate);
+ gate = ggml_reshape_3d(ctx0, gate, 1, n_head, n_tokens);
+ cb(gate, "attn_gate_headwise", il);
+
+ attn_out = ggml_reshape_3d(ctx0, attn_out, n_embd_head_v_mla, n_head, n_tokens);
+ attn_out = ggml_mul(ctx0, attn_out, gate);
+ attn_out = ggml_cont_2d(ctx0, attn_out, n_embd_head_v_mla * n_head, n_tokens);
+ cb(attn_out, "attn_gated", il);
+
+ cur = ggml_mul_mat(ctx0, layer.wo, attn_out);
+ cb(cur, "mla_out", il);
+ }
+
+ if (il == n_layer - 1 && inp_out_ids) {
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ cur = build_norm(ffn_inp, layer.ffn_norm, NULL, LLM_NORM_RMS, il);
+ cb(cur, "ffn_norm", il);
+
+ if ((uint32_t) il < hparams.n_layer_dense_lead) {
+ cur = build_ffn(cur,
+ layer.ffn_up, NULL, NULL,
+ layer.ffn_gate, NULL, NULL,
+ layer.ffn_down, NULL, NULL,
+ NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+ cb(cur, "ffn_out", il);
+ } else {
+ // noaux_tc grouped top-k + sigmoid + expert_bias: build_moe_ffn reads
+ // n_expert_groups / n_group_used straight from hparams.
+ ggml_tensor * moe_out = build_moe_ffn(cur,
+ layer.ffn_gate_inp,
+ layer.ffn_up_exps,
+ layer.ffn_gate_exps,
+ layer.ffn_down_exps,
+ layer.ffn_exp_probs_b,
+ hparams.n_expert,
+ hparams.n_expert_used,
+ LLM_FFN_SILU, hparams.expert_weights_norm,
+ hparams.expert_weights_scale,
+ (llama_expert_gating_func_type) hparams.expert_gating_func,
+ il);
+ cb(moe_out, "ffn_moe_out", il);
+
+ ggml_tensor * ffn_shexp = build_ffn(cur,
+ layer.ffn_up_shexp, NULL, NULL,
+ layer.ffn_gate_shexp, NULL, NULL,
+ layer.ffn_down_shexp, NULL, NULL,
+ NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+ cb(ffn_shexp, "ffn_shexp", il);
+
+ cur = ggml_add(ctx0, moe_out, ffn_shexp);
+ cb(cur, "ffn_out", il);
+ }
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+
+ cur = build_cvec(cur, il);
+ cb(cur, "l_out", il);
+
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+ cb(cur, "result_norm", -1);
+ res->t_embd = cur;
+
+ cur = ggml_mul_mat(ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+ res->t_logits = cur;
+
+ ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/models.h b/src/models/models.h
index ad3dadaf3..ba0496edc 100644
--- a/src/models/models.h
+++ b/src/models/models.h
@@ -2211,6 +2211,24 @@ struct llama_model_kimi_linear : public llama_model_base {
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
};
+// Ling 3.0 flash (inclusionAI/Ling-3.0-flash), model_type "bailing_hybrid".
+// Hybrid KDA + gated MLA MoE. Despite the family name this is NOT the
+// bailingmoe2 stack (Ling 2.0) -- only the MoE router carries over. See
+// src/models/bailing-hybrid.cpp for the deltas against kimi-linear.
+struct llama_model_bailing_hybrid : public llama_model_base {
+ llama_model_bailing_hybrid(const struct llama_model_params & params) : llama_model_base(params) {}
+ void load_arch_hparams(llama_model_loader & ml) override;
+ void load_arch_tensors(llama_model_loader & ml) override;
+
+ struct graph : public llm_build_delta_net_base {
+ graph(const llama_model & model, const llm_graph_params & params);
+
+ const llama_model & model;
+ };
+
+ std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
struct llama_model_step35 : public llama_model_base {
llama_model_step35(const struct llama_model_params & params) : llama_model_base(params) {}
|