[Model] Qwen3.5 dense and MoE support (no vision)

convert_hf_to_gguf.py

@@ -4102,39 +4102,27 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
         # process the experts separately
         name = name.replace("language_model.", "") # InternVL
 
-        # handle aggregated expert tensors
-        # GGUF stores dimensions reversed from PyTorch, so:
-        # PyTorch (A,B,C) -> GGUF writes [C,B,A] -> GGML reads ne={C,B,A}
-        # Input shapes from HF: (n_expert, n_ff_exp, n_embd) or (n_expert, n_embd, n_ff_exp)
-        # Expected GGML ne: {n_embd, n_ff_exp, n_expert} for gate/up, {n_ff_exp, n_embd, n_expert} for down
+        # handle pre-packed expert tensors (e.g. Qwen3.5 MoE, Qwen3Next)
+        # HF stores these using nn.Linear convention: [n_expert, out_features, in_features]
+        # This matches the individual expert stacking path below (which stacks
+        # per-expert [out, in] weights into [n_expert, out, in]), so no permute is needed.
         if name.endswith("mlp.experts.down_proj") or name.endswith("mlp.experts.down_proj.weight"):
             mapped = f"{name}.weight" if not name.endswith(".weight") else name
-            # Input: (n_expert=128, n_ff_exp=768, n_embd=2048)
-            # Want GGML ne: {n_ff_exp, n_embd, n_expert} = {768, 2048, 128}
-            # Need PyTorch: (128, 2048, 768) [reversed of GGML]
-            # So: permute(0, 2, 1): (128, 768, 2048) -> (128, 2048, 768)
-            permuted = data_torch.permute(0, 2, 1).contiguous()
-            yield from super().modify_tensors(permuted, mapped, bid)
+            # HF: [n_expert, n_embd, n_ff] → GGML: {n_ff, n_embd, n_expert} ✓
+            yield from super().modify_tensors(data_torch, mapped, bid)
             return
 
         if name.endswith("mlp.experts.gate_up_proj") or name.endswith("mlp.experts.gate_up_proj.weight"):
-            if data_torch.ndim < 3 or data_torch.shape[-1] % 2 != 0:
-                raise ValueError(f"Unexpected gate_up_proj shape for {name}: {tuple(data_torch.shape)}")
-            split_dim = data_torch.shape[-1] // 2
-            gate = data_torch[..., :split_dim].contiguous()
-            up = data_torch[..., split_dim:].contiguous()
-            # Input gate/up: (n_expert=128, n_embd=2048, n_ff_exp=768)
-            # Want GGML ne: {n_embd, n_ff_exp, n_expert} = {2048, 768, 128}
-            # Need PyTorch: (128, 768, 2048) [reversed of GGML]
-            # So: permute(0, 2, 1): (128, 2048, 768) -> (128, 768, 2048)
-            base_name = name.removesuffix(".weight")
-            base = base_name.rsplit('.', 1)[0]
-            mapped_gate = f"{base}.gate_proj.weight"
-            mapped_up = f"{base}.up_proj.weight"
-            perm_gate = gate.permute(0, 2, 1).contiguous()
-            perm_up = up.permute(0, 2, 1).contiguous()
-            yield from super().modify_tensors(perm_gate, mapped_gate, bid)
-            yield from super().modify_tensors(perm_up, mapped_up, bid)
+            # HF: [n_expert, 2*n_ff, n_embd] → split on dim=1
+            n_ff = data_torch.shape[1] // 2
+            gate = data_torch[:, :n_ff, :].contiguous()
+            up = data_torch[:, n_ff:, :].contiguous()
+            # gate/up: [n_expert, n_ff, n_embd] → GGML: {n_embd, n_ff, n_expert} ✓
+            base_name = name.removesuffix(".weight").removesuffix(".gate_up_proj")
+            mapped_gate = f"{base_name}.gate_proj.weight"
+            mapped_up = f"{base_name}.up_proj.weight"
+            yield from super().modify_tensors(gate, mapped_gate, bid)
+            yield from super().modify_tensors(up, mapped_up, bid)
             return
 
         if name.startswith("mlp") or name.startswith("vision_model") or name.startswith("model.vision_tower") or name.startswith("model.multi_modal_projector") or name.startswith("model.visual"):
@@ -4344,6 +4332,40 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
             yield from super().modify_tensors(data_torch, name, bid)
 
 
+@ModelBase.register("Qwen3_5ForCausalLM", "Qwen3_5TextForCausalLM")
+class Qwen3_5Model(Qwen3NextModel):
+    model_arch = gguf.MODEL_ARCH.QWEN3_5
+
+    # Stores whichever of in_proj_a/in_proj_b is seen first, keyed by layer
+    _pending_ba: dict[int | None, tuple[str, Tensor]] = {}
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # Handle split in_proj_b + in_proj_a → concatenated SSM_BETA_ALPHA
+        # safetensors sorts alphabetically so in_proj_a arrives before in_proj_b
+        if "in_proj_a.weight" in name or "in_proj_b.weight" in name:
+            which = "a" if "in_proj_a" in name else "b"
+            if bid not in self._pending_ba:
+                self._pending_ba[bid] = (which, data_torch)
+                return
+            prev_which, prev_tensor = self._pending_ba.pop(bid)
+            assert prev_which != which, f"duplicate in_proj_{which} for layer {bid}"
+            b_tensor = prev_tensor if prev_which == "b" else data_torch
+            a_tensor = prev_tensor if prev_which == "a" else data_torch
+            ba_combined = torch.cat([b_tensor, a_tensor], dim=0)
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.SSM_BETA_ALPHA, bid, ".weight"), ba_combined)
+            return
+        else:
+            # Qwen3Next uses .qkvz tensor, so we use the super to get the other functionalities
+            # (norm correction, A_log to A etc.) for free
+            # Qwen2Moe already does the gate_up conversion properly, just use that
+            yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Qwen3_5MoeForCausalLM", "Qwen3_5MoeTextForCausalLM")
+class Qwen3_5MoeModel(Qwen3_5Model):
+    model_arch = gguf.MODEL_ARCH.QWEN3_5_MOE
+
+
 @ModelBase.register("RND1")
 class RND1Model(Qwen2MoeModel):
     model_arch = gguf.MODEL_ARCH.RND1

gguf-py/gguf/constants.py

@@ -382,6 +382,8 @@ class MODEL_ARCH(IntEnum):
     QWEN3            = auto()
     QWEN3MOE         = auto()
     QWEN3NEXT        = auto()
+    QWEN3_5          = auto()
+    QWEN3_5_MOE      = auto()
     QWEN3VL          = auto()
     QWEN3VLMOE       = auto()
     PHI2             = auto()
@@ -812,6 +814,8 @@ class MODEL_TENSOR(IntEnum):
     MODEL_ARCH.QWEN3:            "qwen3",
     MODEL_ARCH.QWEN3MOE:         "qwen3moe",
     MODEL_ARCH.QWEN3NEXT:        "qwen3next",
+    MODEL_ARCH.QWEN3_5:          "qwen3_5",
+    MODEL_ARCH.QWEN3_5_MOE:      "qwen3_5moe",
     MODEL_ARCH.QWEN3VL:          "qwen3vl",
     MODEL_ARCH.QWEN3VLMOE:       "qwen3vlmoe",
     MODEL_ARCH.PHI2:             "phi2",
@@ -1784,6 +1788,61 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.SSM_BETA_ALPHA,
         MODEL_TENSOR.SSM_OUT
     ],
+    MODEL_ARCH.QWEN3_5: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_POST_NORM,
+        MODEL_TENSOR.ATTN_GATE,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.SSM_A,
+        MODEL_TENSOR.SSM_CONV1D,
+        MODEL_TENSOR.SSM_DT,
+        MODEL_TENSOR.SSM_NORM,
+        MODEL_TENSOR.SSM_IN,
+        MODEL_TENSOR.SSM_BETA_ALPHA,
+        MODEL_TENSOR.SSM_OUT,
+    ],
+    MODEL_ARCH.QWEN3_5_MOE: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_POST_NORM,
+        MODEL_TENSOR.ATTN_GATE,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_INP_SHEXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.SSM_A,
+        MODEL_TENSOR.SSM_CONV1D,
+        MODEL_TENSOR.SSM_DT,
+        MODEL_TENSOR.SSM_NORM,
+        MODEL_TENSOR.SSM_IN,
+        MODEL_TENSOR.SSM_BETA_ALPHA,
+        MODEL_TENSOR.SSM_OUT,
+    ],
     MODEL_ARCH.QWEN3VL: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,

gguf-py/gguf/tensor_mapping.py

@@ -228,6 +228,7 @@ class TensorNameMap:
             "transformer_encoder.{bid}.qkv",                                       # neobert
             "layers.{bid}.attn.Wqkv",                                              # modern-bert
             "model.layers.{bid}.self_attn.language_expert_query_key_value",        # cogvlm
+            "model.layers.{bid}.linear_attn.in_proj_qkv",                          # qwen3.5
         ),
 
         # Attention query
@@ -358,8 +359,9 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.ATTN_GATE: (
-            "model.layers.{bid}.self_attn.gate_proj", # afmoe
-            "model.layers.{bid}.self_attn.g_proj",    # step3.5 head-wise attention gate
+            "model.layers.{bid}.self_attn.gate_proj",   # afmoe
+            "model.layers.{bid}.self_attn.g_proj",      # step3.5 head-wise attention gate
+            "model.layers.{bid}.linear_attn.in_proj_z", # qwen3.5
         ),
 
         # Feed-forward norm

src/CMakeLists.txt

@@ -57,6 +57,7 @@ add_library(llama
             models/deci.cpp
             models/deepseek.cpp
             models/deepseek2.cpp
+            models/delta.cpp
             models/dots1.cpp
             models/dream.cpp
             models/ernie4-5-moe.cpp
@@ -122,6 +123,8 @@ add_library(llama
             models/qwen3vl-moe.cpp
             models/qwen3moe.cpp
             models/qwen3next.cpp
+            models/qwen3-5.cpp
+            models/qwen3-5moe.cpp
             models/refact.cpp
             models/rnd1.cpp
             models/rwkv6-base.cpp

src/llama-arch.cpp

@@ -35,6 +35,8 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_QWEN3,            "qwen3"            },
     { LLM_ARCH_QWEN3MOE,         "qwen3moe"         },
     { LLM_ARCH_QWEN3NEXT,        "qwen3next"        },
+    { LLM_ARCH_QWEN3_5,          "qwen3_5"          },
+    { LLM_ARCH_QWEN3_5_MOE,      "qwen3_5moe"       },
     { LLM_ARCH_QWEN3VL,          "qwen3vl"          },
     { LLM_ARCH_QWEN3VLMOE,       "qwen3vlmoe"       },
     { LLM_ARCH_PHI2,             "phi2"             },
@@ -985,6 +987,63 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                 LLM_TENSOR_SSM_NORM,
                 LLM_TENSOR_SSM_OUT,
             };
+        case LLM_ARCH_QWEN3_5:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_POST_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_GATE,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_SSM_A_NOSCAN,
+                LLM_TENSOR_SSM_CONV1D,
+                LLM_TENSOR_SSM_DT,
+                LLM_TENSOR_SSM_BETA_ALPHA,
+                LLM_TENSOR_SSM_IN,
+                LLM_TENSOR_SSM_NORM,
+                LLM_TENSOR_SSM_OUT,
+            };
+        case LLM_ARCH_QWEN3_5_MOE:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_POST_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_GATE,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_GATE_INP_SHEXP,
+                LLM_TENSOR_FFN_GATE_SHEXP,
+                LLM_TENSOR_FFN_DOWN_SHEXP,
+                LLM_TENSOR_FFN_UP_SHEXP,
+                LLM_TENSOR_SSM_A_NOSCAN,
+                LLM_TENSOR_SSM_CONV1D,
+                LLM_TENSOR_SSM_DT,
+                LLM_TENSOR_SSM_BETA_ALPHA,
+                LLM_TENSOR_SSM_IN,
+                LLM_TENSOR_SSM_NORM,
+                LLM_TENSOR_SSM_OUT,
+            };
         case LLM_ARCH_QWEN3VL:
         case LLM_ARCH_CHAMELEON:
         case LLM_ARCH_HUNYUAN_DENSE:
@@ -2674,6 +2733,8 @@ bool llm_arch_is_hybrid(const llm_arch & arch) {
         case LLM_ARCH_NEMOTRON_H:
         case LLM_ARCH_NEMOTRON_H_MOE:
         case LLM_ARCH_QWEN3NEXT:
+        case LLM_ARCH_QWEN3_5:
+        case LLM_ARCH_QWEN3_5_MOE:
         case LLM_ARCH_KIMI_LINEAR:
             return true;
         default:

src/llama-arch.h

@@ -39,6 +39,8 @@ enum llm_arch {
     LLM_ARCH_QWEN3,
     LLM_ARCH_QWEN3MOE,
     LLM_ARCH_QWEN3NEXT,
+    LLM_ARCH_QWEN3_5,
+    LLM_ARCH_QWEN3_5_MOE,
     LLM_ARCH_QWEN3VL,
     LLM_ARCH_QWEN3VLMOE,
     LLM_ARCH_PHI2,

src/llama-context.cpp

@@ -2013,7 +2013,7 @@ void llama_context::output_reorder() {
 //
 
 uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const {
-    if (model.arch == LLM_ARCH_QWEN3NEXT || model.arch == LLM_ARCH_KIMI_LINEAR) {
+    if (model.arch == LLM_ARCH_QWEN3NEXT || model.arch == LLM_ARCH_QWEN3_5 || model.arch == LLM_ARCH_QWEN3_5_MOE || model.arch == LLM_ARCH_KIMI_LINEAR) {
         return std::max<uint32_t>(n_tokens * 40, 32u * model.n_tensors());
     }
     uint32_t res = std::max<uint32_t>(1024u, 8u*model.n_tensors());