revert : "[Model] Qwen3.5 dense and MoE support (no vision) (#19435 )" (#19453 )

This reverts commit 39bf692af1.
ggml-virtgpu: add backend documentation (#19354 )
2026-02-12 14:03:20 +02:00 · 2026-02-09 14:57:51 +02:00 · 2026-02-09 20:15:42 +08:00 · 2026-02-09 07:12:02 +01:00
20 changed files with 981 additions and 1534 deletions
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -109,6 +109,7 @@ option(LLAMA_BUILD_TOOLS    "llama: build tools"          ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_EXAMPLES "llama: build examples"       ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_SERVER   "llama: build server example" ${LLAMA_STANDALONE})
 option(LLAMA_TOOLS_INSTALL  "llama: install tools"        ${LLAMA_TOOLS_INSTALL_DEFAULT})
+option(LLAMA_TESTS_INSTALL  "llama: install tests"        ON)

 # 3rd party libs
 option(LLAMA_HTTPLIB    "llama: httplib for downloading functionality" ON)
--- a/README.md
+++ b/README.md
@@ -288,6 +288,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 | [WebGPU [In Progress]](docs/build.md#webgpu) | All |
 | [RPC](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) | All |
 | [Hexagon [In Progress]](docs/backend/hexagon/README.md) | Snapdragon |
+| [VirtGPU](docs/backend/VirtGPU.md) | VirtGPU APIR |

 ## Obtaining and quantizing models

--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -4102,27 +4102,39 @@ class Qwen2MoeModel(TextModel):
        # process the experts separately
        name = name.replace("language_model.", "") # InternVL

-        # 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.
+        # 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
        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
-            # HF: [n_expert, n_embd, n_ff] → GGML: {n_ff, n_embd, n_expert} ✓
-            yield from super().modify_tensors(data_torch, mapped, bid)
+            # 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)
            return

        if name.endswith("mlp.experts.gate_up_proj") or name.endswith("mlp.experts.gate_up_proj.weight"):
-            # 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)
+            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)
            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"):
@@ -4332,40 +4344,6 @@ class Qwen3NextModel(Qwen2MoeModel):
            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
--- a/docs/backend/VirtGPU.md
+++ b/docs/backend/VirtGPU.md
@@ -0,0 +1,180 @@
+# GGML-VirtGPU Backend
+
+The GGML-VirtGPU backend enables GGML applications to run machine
+learning computations on host hardware while the application itself
+runs inside a virtual machine.  It uses host-guest shared memory to
+efficiently share data buffers between the two sides.
+
+This backend relies on the virtio-gpu, and VirglRenderer API Remoting
+(APIR) component. The backend is split into two libraries:
+- a GGML implementation (the "remoting frontend"), running in the
+  guest and interacting with the virtgpu device
+- a VirglRenderer APIR compatible library (the "remoting backend"),
+  running in the host and interacting with Virglrenderer and an actual
+  GGML device backend.
+
+## OS support
+
+| OS       | Status            | Backend     | CI testing  | Notes
+| -------- | ----------------- | ----------- | ----------- | -----
+| MacOS 14 | Supported         | ggml-metal  | X           | Working when compiled on MacOS 14
+| MacOS 15 | Supported         | ggml-metal  | X           | Working when compiled on MacOS 14 or MacOS 15
+| MacOS 26 | Not tested        |             |             |
+| Linux    | Under development | ggml-vulkan | not working | Working locally, CI running into deadlocks
+
+
+## Architecture Overview
+
+The GGML-VirtGPU backend consists of three main components:
+
+```mermaid
+graph TD
+    %% Nodes
+
+ subgraph GuestVM ["Guest VM - Frontend"]
+        App([GGML Application<br/>llama.cpp, etc.])
+
+        direction TB
+        Interface[GGML Backend Interface]
+        Comm["GGML-VirtGPU<br/>(hypercalls + shared mem)"]
+
+        App --> Interface
+        Interface --> Comm
+    end
+
+    API[virtio-gpu / virglrenderer API]
+
+    subgraph HostSystem [Host System - Backend]
+        direction TB
+        Dispatcher[GGML-VirtGPU-Backend]
+        BackendLib[GGML Backend library<br/>Metal / Vulkan / CPU / ...]
+
+        Dispatcher --> BackendLib
+    end
+
+    %% Connections
+    Comm --> API
+    API --> HostSystem
+```
+
+### Key Components
+
+1. **Guest-side Frontend** (`ggml-virtgpu/`): Implements the GGML backend interface and forwards operations to the host
+2. **Host-side Backend** (`ggml-virtgpu/backend/`): Receives forwarded operations and executes them on actual hardware backends
+3. **Communication Layer**: Uses virtio-gpu hypercalls and shared memory for efficient data transfer
+
+## Features
+
+- **Dynamic backend loading** on the host side (CPU, CUDA, Metal, etc.)
+- **Zero-copy data transfer** via host-guest shared memory pages
+
+## Communication Protocol
+
+### Hypercalls and Shared Memory
+
+The backend uses two primary communication mechanisms:
+
+1. **Hypercalls (`DRM_IOCTL_VIRTGPU_EXECBUFFER`)**: Trigger remote execution from guest to host
+2. **Shared Memory Pages**: Zero-copy data transfer for tensors and parameters
+
+#### Shared Memory Layout
+
+Each connection uses two shared memory buffers:
+
+- **Data Buffer** (24 MiB): For command/response data and tensor transfers
+- **Reply Buffer** (16 KiB): For command replies and status information
+- **Data Buffers**: Dynamically allocated host-guest shared buffers
+  served as GGML buffers.
+
+### APIR Protocol
+
+The Virglrender API Remoting protocol defines three command types:
+
+- `HANDSHAKE`: Protocol version negotiation and capability discovery
+- `LOADLIBRARY`: Dynamic loading of backend libraries on the host
+- `FORWARD`: API function call forwarding
+
+### Binary Serialization
+
+Commands and data are serialized using a custom binary protocol with:
+
+- Fixed-size encoding for basic types
+- Variable-length arrays with size prefixes
+- Buffer bounds checking
+- Error recovery mechanisms
+
+## Supported Operations
+
+### Device Operations
+- Device enumeration and capability queries
+- Memory information (total/free)
+- Backend type detection
+
+### Buffer Operations
+- Buffer allocation and deallocation
+- Tensor data transfer (host ↔ guest)
+- Memory copying and clearing
+
+### Computation Operations
+- Graph execution forwarding
+
+## Build Requirements
+
+### Guest-side Dependencies
+- `libdrm` for DRM/virtio-gpu communication
+- C++20 compatible compiler
+- CMake 3.14+
+
+### Host-side Dependencies
+- virglrenderer with APIR support (pending upstream review)
+- Target backend libraries (libggml-metal, libggml-vulkan, etc.)
+
+## Configuration
+
+### Environment Variables
+
+- `GGML_VIRTGPU_BACKEND_LIBRARY`: Path to the host-side backend library
+- `GGML_VIRTGPU_DEBUG`: Enable debug logging
+
+### Build Options
+
+- `GGML_VIRTGPU`: Enable the VirtGPU backend (`ON` or `OFF`, default: `OFF`)
+- `GGML_VIRTGPU_BACKEND`: Build the host-side backend component (`ON`, `OFF` or `ONLY`, default: `OFF`)
+
+### System Requirements
+
+- VM with virtio-gpu support
+- VirglRenderer with APIR patches
+- Compatible backend libraries on host
+
+## Limitations
+
+- **VM-specific**: Only works in virtual machines with virtio-gpu support
+- **Host dependency**: Requires properly configured host-side backend
+- **Latency**: Small overhead from VM escaping for each operation
+
+
+* This work is pending upstream changes in the VirglRenderer
+  project.
+  * The backend can be tested with Virglrenderer compiled from source
+  using this PR:
+  https://gitlab.freedesktop.org/virgl/virglrenderer/-/merge_requests/1590
+* This work is pending changes in the VMM/hypervisor running the
+  virtual machine, which need to know how to route the newly
+  introduced APIR capset.
+  * The environment variable `VIRGL_ROUTE_VENUS_TO_APIR=1` allows
+    using the Venus capset, until the relevant hypervisors have been
+    patched. However, setting this flag breaks the Vulkan/Venus normal
+    behavior.
+  * The environment variable `GGML_REMOTING_USE_APIR_CAPSET` tells the
+    `ggml-virtgpu` backend to use the APIR capset. This will become
+    the default when the relevant hypervisors have been patched.
+
+* This work focused on improving the performance of llama.cpp running
+  on MacOS containers, and is mainly tested on this platform. The
+  linux support (via `krun`) is in progress.
+
+## See Also
+
+- [Development and Testing](VirtGPU/development.md)
+- [Backend configuration](VirtGPU/configuration.md)
--- a/docs/backend/VirtGPU/configuration.md
+++ b/docs/backend/VirtGPU/configuration.md
@@ -0,0 +1,174 @@
+# GGML-VirtGPU Backend Configuration
+
+This document describes the environment variables used by the ggml-virtgpu backend system, covering both the frontend (guest-side) and backend (host-side) components.
+
+## Environment Variables Overview
+
+The ggml-virtgpu backend uses environment variables for configuration across three main components:
+- **Frontend (Guest)**: GGML applications running in VMs
+- **Hypervisor**: Virglrenderer/APIR system
+- **Backend (Host)**: Host-side GGML backend integration
+
+## Frontend (Guest-side) Configuration
+
+### GGML_REMOTING_USE_APIR_CAPSET
+- **Location**: `ggml/src/ggml-virtgpu/virtgpu.cpp`
+- **Type**: Boolean flag (presence-based)
+- **Purpose**: Controls which virtio-gpu capability set to use for communication
+- **Values**:
+  - Set (any value): Use the APIR capset (long-term setup)
+  - Unset: Use the Venus capset (easier for testing with an unmodified hypervisor)
+- **Default**: Unset (Venus capset)
+- **Usage**:
+  ```bash
+  export GGML_REMOTING_USE_APIR_CAPSET=1  # Use APIR capset
+  # or leave unset for Venus capset
+  ```
+
+## Hypervisor (Virglrenderer/APIR) Configuration
+
+These environment variables are used during the transition phase for
+running with an unmodified hypervisor (not supporting the
+VirglRenderer APIR component). They will be removed in the future, and
+the hypervisor will instead configure VirglRenderer with the APIR
+_Configuration Key_.
+
+### VIRGL_APIR_BACKEND_LIBRARY
+- **Location**: `virglrenderer/src/apir/apir-context.c`
+- **Configuration Key**: `apir.load_library.path`
+- **Type**: File path string
+- **Purpose**: Path to the APIR backend library that virglrenderer should dynamically load
+- **Required**: Yes
+- **Example**:
+  ```bash
+  export VIRGL_APIR_BACKEND_LIBRARY="/path/to/libggml-remotingbackend.so"
+  ```
+
+### VIRGL_ROUTE_VENUS_TO_APIR
+- **Location**: `virglrenderer/src/apir/apir-renderer.h`
+- **Type**: Boolean flag (presence-based)
+- **Purpose**: Temporary workaround to route Venus capset calls to APIR during hypervisor transition period
+- **Status**: will be removed once hypervisors support APIR natively
+- **Warning**: Breaks normal Vulkan/Venus functionality
+- **Usage**:
+  ```bash
+  export VIRGL_ROUTE_VENUS_TO_APIR=1  # For testing with an unmodified hypervisor
+  ```
+
+### VIRGL_APIR_LOG_TO_FILE
+- **Location**: `virglrenderer/src/apir/apir-renderer.c`
+- **Environment Variable**: `VIRGL_APIR_LOG_TO_FILE`
+- **Type**: File path string
+- **Purpose**: Enable debug logging from the VirglRenderer APIR component to specified file
+- **Required**: No (optional debugging)
+- **Default**: Logging to `stderr`
+- **Usage**:
+  ```bash
+  export VIRGL_APIR_LOG_TO_FILE="/tmp/apir-debug.log"
+  ```
+
+## Backend (Host-side) Configuration
+
+These environment variables are used during the transition phase for
+running with an unmodified hypervisor (not supporting the
+VirglRenderer APIR component). They will be removed in the future, and
+the hypervisor will instead configure VirglRenderer with the APIR
+_Configuration Key_.
+
+### APIR_LLAMA_CPP_GGML_LIBRARY_PATH
+- **Location**: `ggml/src/ggml-virtgpu/backend/backend.cpp`
+- **Environment Variable**: `APIR_LLAMA_CPP_GGML_LIBRARY_PATH`
+- **Configuration Key**: `ggml.library.path`
+- **Type**: File path string
+- **Purpose**: Path to the actual GGML backend library (Metal, CUDA, Vulkan, etc.)
+- **Required**: **Yes** - backend initialization fails without this
+- **Examples**:
+  ```bash
+  # macOS with Metal backend
+  export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="/opt/llama.cpp/lib/libggml-metal.dylib"
+
+  # Linux with CUDA backend
+  export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="/opt/llama.cpp/lib/libggml-cuda.so"
+
+  # macOS or Linux with Vulkan backend
+  export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="/opt/llama.cpp/lib/libggml-vulkan.so"
+  ```
+
+### APIR_LLAMA_CPP_GGML_LIBRARY_REG
+- **Location**: `ggml/src/ggml-virtgpu/backend/backend.cpp`
+- **Environment Variable**: `APIR_LLAMA_CPP_GGML_LIBRARY_REG`
+- **Configuration Key**: `ggml.library.reg`
+- **Type**: Function symbol name string
+- **Purpose**: Name of the backend registration function to call after loading the library
+- **Required**: No (defaults to `ggml_backend_init`)
+- **Default**: `ggml_backend_init`
+- **Examples**:
+  ```bash
+  # Metal backend
+  export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_metal_reg"
+
+  # CUDA backend
+  export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_cuda_reg"
+
+  # Vulkan backend
+  export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_vulkan_reg"
+
+  # Generic fallback (default)
+  # export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_init"
+  ```
+
+### APIR_LLAMA_CPP_LOG_TO_FILE
+- **Location**: `ggml/src/ggml-virtgpu/backend/backend.cpp:62`
+- **Environment Variable**: `APIR_LLAMA_CPP_LOG_TO_FILE`
+- **Type**: File path string
+- **Purpose**: Enable debug logging from the GGML backend to specified file
+- **Required**: No (optional debugging)
+- **Usage**:
+  ```bash
+  export APIR_LLAMA_CPP_LOG_TO_FILE="/tmp/ggml-backend-debug.log"
+  ```
+
+## Configuration Flow
+
+The configuration system works as follows:
+
+1. **Hypervisor Setup**: Virglrenderer loads the APIR backend library specified by `VIRGL_APIR_BACKEND_LIBRARY`
+
+2. **Context Creation**: When an APIR context is created, it populates a configuration table with environment variables:
+   - `apir.load_library.path` ← `VIRGL_APIR_BACKEND_LIBRARY`
+   - `ggml.library.path` ← `APIR_LLAMA_CPP_GGML_LIBRARY_PATH`
+   - `ggml.library.reg` ← `APIR_LLAMA_CPP_GGML_LIBRARY_REG`
+   - this step will eventually be performed by the hypervisor itself, with command-line arguments instead of environment variables.
+
+3. **Backend Initialization**: The backend queries the configuration via callbacks:
+   - `virgl_cbs->get_config(ctx_id, "ggml.library.path")` returns the library path
+   - `virgl_cbs->get_config(ctx_id, "ggml.library.reg")` returns the registration function
+
+4. **Library Loading**: The backend dynamically loads and initializes the specified GGML library
+
+## Error Messages
+
+Common error scenarios and their messages:
+
+- **Missing library path**: `"cannot open the GGML library: env var 'APIR_LLAMA_CPP_GGML_LIBRARY_PATH' not defined"`
+- **Missing registration function**: `"cannot register the GGML library: env var 'APIR_LLAMA_CPP_GGML_LIBRARY_REG' not defined"`
+
+## Example Complete Configuration
+
+Here's an example configuration for a macOS host with Metal backend:
+
+```bash
+# Hypervisor environment
+export VIRGL_APIR_BACKEND_LIBRARY="/opt/llama.cpp/lib/libggml-virtgpu-backend.dylib"
+
+# Backend configuration
+export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="/opt/llama.cpp/lib/libggml-metal.dylib"
+export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_metal_reg"
+
+# Optional logging
+export VIRGL_APIR_LOG_TO_FILE="/tmp/apir.log"
+export APIR_LLAMA_CPP_LOG_TO_FILE="/tmp/ggml.log"
+
+# Guest configuration
+export GGML_REMOTING_USE_APIR_CAPSET=1
+```
--- a/docs/backend/VirtGPU/development.md
+++ b/docs/backend/VirtGPU/development.md
@@ -0,0 +1,220 @@
+# Development and Testing
+
+## Development
+
+### Code Generation
+
+The backend uses code generation from YAML configuration:
+
+```bash
+# Regenerate protocol code
+cd ggml-virtgpu/
+python regenerate_remoting.py
+```
+
+### Adding New Operations
+
+1. Add function definition to `ggmlremoting_functions.yaml`
+2. Regenerate code with `regenerate_remoting.py`
+3. Implement guest-side forwarding in `virtgpu-forward-*.cpp`
+4. Implement host-side handling in `backend-dispatched-*.cpp`
+
+## Testing
+
+This document provides instructions for building and testing the GGML-VirtGPU backend on macOS with containers.
+
+### Prerequisites
+
+The testing setup requires:
+
+- macOS host system
+- Container runtime with `libkrun` provider (podman machine)
+- Access to development patchset for VirglRenderer
+
+### Required Patchsets
+
+The backend requires patches that are currently under review:
+
+- **Virglrenderer APIR upstream PR**: https://gitlab.freedesktop.org/virgl/virglrenderer/-/merge_requests/1590 (for reference)
+- **MacOS Virglrenderer (for krunkit)**: https://gitlab.freedesktop.org/kpouget/virglrenderer/-/tree/main-macos
+- **Linux Virglrenderer (for krun)**: https://gitlab.freedesktop.org/kpouget/virglrenderer/-/tree/main-linux
+
+### Build Instructions
+
+#### 1. Build ggml-virtgpu-backend (Host-side, macOS)
+
+```bash
+# Build the backend that runs natively on macOS
+mkdir llama.cpp
+cd llama.cpp
+git clone https://github.com/ggml-org/llama.cpp.git src
+cd src
+
+LLAMA_MAC_BUILD=$PWD/build/ggml-virtgpu-backend
+
+cmake -S . -B $LLAMA_MAC_BUILD \
+      -DGGML_NATIVE=OFF \
+      -DLLAMA_CURL=ON \
+      -DGGML_REMOTINGBACKEND=ONLY \
+      -DGGML_METAL=ON
+
+TARGETS="ggml-metal"
+cmake --build $LLAMA_MAC_BUILD --parallel 8 --target $TARGETS
+
+# Build additional tools for native benchmarking
+EXTRA_TARGETS="llama-run llama-bench"
+cmake --build $LLAMA_MAC_BUILD --parallel 8 --target $EXTRA_TARGETS
+```
+
+#### 2. Build virglrenderer (Host-side, macOS)
+
+```bash
+# Build virglrenderer with APIR support
+mkdir virglrenderer
+git clone https://gitlab.freedesktop.org/kpouget/virglrenderer -b main-macos src
+cd src
+
+VIRGL_BUILD_DIR=$PWD/build
+
+# -Dvenus=true and VIRGL_ROUTE_VENUS_TO_APIR=1 route the APIR requests via the Venus backend, for easier testing without a patched hypervisor
+
+meson setup $VIRGL_BUILD_DIR \
+      -Dvenus=true \
+      -Dapir=true
+
+ninja -C $VIRGL_BUILD_DIR
+```
+
+#### 3. Build ggml-virtgpu (Guest-side, Linux)
+
+Option A: Build from a script:
+
+```bash
+# Inside a Linux container
+mkdir llama.cpp
+git clone https://github.com/ggml-org/llama.cpp.git src
+cd src
+
+LLAMA_LINUX_BUILD=$PWD//build-virtgpu
+
+cmake -S . -B $LLAMA_LINUX_BUILD \
+      -DGGML_VIRTGPU=ON
+
+ninja -C $LLAMA_LINUX_BUILD
+```
+
+Option B: Build container image with frontend:
+
+```bash
+cat << EOF > remoting.containerfile
+FROM quay.io/fedora/fedora:43
+USER 0
+
+WORKDIR /app/remoting
+
+ARG LLAMA_CPP_REPO="https://github.com/ggml-org/llama.cpp.git"
+ARG LLAMA_CPP_VERSION="master"
+ARG LLAMA_CPP_CMAKE_FLAGS="-DGGML_VIRTGPU=ON"
+ARG LLAMA_CPP_CMAKE_BUILD_FLAGS="--parallel 4"
+
+RUN dnf install -y git cmake gcc gcc-c++ libcurl-devel libdrm-devel
+
+RUN git clone "\${LLAMA_CPP_REPO}" src \\
+ && git -C src fetch origin \${LLAMA_CPP_VERSION} \\
+ && git -C src reset --hard FETCH_HEAD
+
+RUN mkdir -p build \\
+ && cd src \\
+ && set -o pipefail \\
+ && cmake -S . -B ../build \${LLAMA_CPP_CMAKE_FLAGS} \\
+ && cmake --build ../build/ \${LLAMA_CPP_CMAKE_BUILD_FLAGS}
+
+ENTRYPOINT ["/app/remoting/src/build/bin/llama-server"]
+EOF
+
+mkdir -p empty_dir
+podman build -f remoting.containerfile ./empty_dir -t localhost/llama-cpp.virtgpu
+```
+
+### Environment Setup
+
+#### Set krunkit Environment Variables
+
+```bash
+# Define the base directories (adapt these paths to your system)
+VIRGL_BUILD_DIR=$HOME/remoting/virglrenderer/build
+LLAMA_MAC_BUILD=$HOME/remoting/llama.cpp/build-backend
+
+# For krunkit to load the custom virglrenderer library
+export DYLD_LIBRARY_PATH=$VIRGL_BUILD_DIR/src
+
+# For Virglrenderer to load the ggml-remotingbackend library
+export VIRGL_APIR_BACKEND_LIBRARY="$LLAMA_MAC_BUILD/bin/libggml-virtgpu-backend.dylib"
+
+# For llama.cpp remotingbackend to load the ggml-metal backend
+export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="$LLAMA_MAC_BUILD/bin/libggml-metal.dylib"
+export APIR_LLAMA_CPP_GGML_LIBRARY_REG=ggml_backend_metal_reg
+```
+
+#### Launch Container Environment
+
+```bash
+# Set container provider to libkrun
+export CONTAINERS_MACHINE_PROVIDER=libkrun
+podman machine start
+```
+
+#### Verify Environment
+
+Confirm that krunkit is using the correct virglrenderer library:
+
+```bash
+lsof -c krunkit | grep virglrenderer
+# Expected output:
+# krunkit 50574 user  txt  REG  1,14  2273912  10849442 ($VIRGL_BUILD_DIR/src)/libvirglrenderer.1.dylib
+```
+
+### Running Tests
+
+#### Launch Test Container
+
+```bash
+# Optional model caching
+mkdir -p models
+PODMAN_CACHE_ARGS="-v models:/models --user root:root --cgroupns host --security-opt label=disable -w /models"
+
+podman run $PODMAN_CACHE_ARGS -it --rm --device /dev/dri localhost/llama-cpp.virtgpu
+```
+
+#### Test llama.cpp in Container
+
+```bash
+
+# Run performance benchmark
+/app/remoting/build/bin/llama-bench -m ./llama3.2
+```
+
+Expected output (performance may vary):
+```
+| model                          |       size |     params | backend    | ngl |          test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | --: | ------------: | -------------------: |
+| llama 3B Q4_K - Medium         |   1.87 GiB |     3.21 B | ggml-virtgpu |  99 |         pp512 |        991.30 ± 0.66 |
+| llama 3B Q4_K - Medium         |   1.87 GiB |     3.21 B | ggml-virtgpu |  99 |         tg128 |         85.71 ± 0.11 |
+```
+
+### Troubleshooting
+
+#### SSH Environment Variable Issues
+
+⚠️ **Warning**: Setting `DYLD_LIBRARY_PATH` from SSH doesn't work on macOS. Here is a workaround:
+
+**Workaround 1: Replace system library**
+```bash
+VIRGL_BUILD_DIR=$HOME/remoting/virglrenderer/build  # ⚠️ adapt to your system
+BREW_VIRGL_DIR=/opt/homebrew/Cellar/virglrenderer/0.10.4d/lib
+VIRGL_LIB=libvirglrenderer.1.dylib
+
+cd $BREW_VIRGL_DIR
+mv $VIRGL_LIB ${VIRGL_LIB}.orig
+ln -s $VIRGL_BUILD_DIR/src/$VIRGL_LIB
+```
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -382,8 +382,6 @@ class MODEL_ARCH(IntEnum):
    QWEN3            = auto()
    QWEN3MOE         = auto()
    QWEN3NEXT        = auto()
-    QWEN3_5          = auto()
-    QWEN3_5_MOE      = auto()
    QWEN3VL          = auto()
    QWEN3VLMOE       = auto()
    PHI2             = auto()
@@ -814,8 +812,6 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
    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",
@@ -1788,61 +1784,6 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
        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,
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -228,7 +228,6 @@ 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
@@ -359,9 +358,8 @@ 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}.linear_attn.in_proj_z", # qwen3.5
+            "model.layers.{bid}.self_attn.gate_proj", # afmoe
+            "model.layers.{bid}.self_attn.g_proj",    # step3.5 head-wise attention gate
        ),

        # Feed-forward norm
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -57,7 +57,6 @@ 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
@@ -123,8 +122,6 @@ 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
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -35,8 +35,6 @@ 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"             },
@@ -987,63 +985,6 @@ 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:
@@ -2733,8 +2674,6 @@ 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:
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -39,8 +39,6 @@ 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,
--- a/src/llama-context.cpp
+++ b/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_QWEN3_5 || model.arch == LLM_ARCH_QWEN3_5_MOE || model.arch == LLM_ARCH_KIMI_LINEAR) {
+    if (model.arch == LLM_ARCH_QWEN3NEXT || 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());
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -2412,25 +2412,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                    default: type = LLM_TYPE_UNKNOWN;
                }
            } break;
-        case LLM_ARCH_QWEN3_5:
-        case LLM_ARCH_QWEN3_5_MOE:
-            {
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp, false);
-                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
-
-                // Load linear attention (gated delta net) parameters
-                ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
-                ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
-                ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
-                ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
-                ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
-
-                // Mark recurrent layers (linear attention layers)
-                for (uint32_t i = 0; i < hparams.n_layer; ++i) {
-                    hparams.recurrent_layer_arr[i] = ((i + 1) % 4 != 0);
-                }
-            } break;
        case LLM_ARCH_MISTRAL3:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -7113,129 +7094,6 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), { n_embd, n_ff_exp, n_expert }, 0);

-                        // Shared experts
-                        layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), { n_embd }, 0);
-                        layer.ffn_gate_shexp     = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP,     "weight", i), { n_embd, hparams.n_ff_shexp }, 0);
-                        layer.ffn_up_shexp       = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,       "weight", i), { n_embd, hparams.n_ff_shexp }, 0);
-                        layer.ffn_down_shexp     = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP,     "weight", i), { hparams.n_ff_shexp, n_embd }, 0);
-                    }
-                } break;
-            case LLM_ARCH_QWEN3_5:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
-
-                    // output
-                    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 }, TENSOR_NOT_REQUIRED);
-
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
-                    }
-
-                    // Calculate dimensions from hyperparameters
-                    const int64_t head_k_dim = hparams.ssm_d_state;
-                    const int64_t head_v_dim = hparams.ssm_d_state;
-                    const int64_t n_k_heads  = hparams.ssm_n_group;
-                    const int64_t n_v_heads  = hparams.ssm_dt_rank;
-                    const int64_t key_dim    = head_k_dim * n_k_heads;
-                    const int64_t value_dim  = head_v_dim * n_v_heads;
-                    const int64_t conv_dim   = key_dim * 2 + value_dim;
-
-                    const int64_t ba_dim = n_v_heads * 2;
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", i), { n_embd }, 0);
-                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0);
-
-                        if (!hparams.is_recurrent(i)) {
-                            // Full attention layers
-                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), { n_embd, n_embd_head_k * n_head * 2 }, 0);
-                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), { n_embd, n_embd_k_gqa }, 0);
-                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), { n_embd, n_embd_v_gqa }, 0);
-                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
-
-                            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
-                            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
-                        } else {
-                            // Linear attention (gated delta net) specific tensors
-                            layer.ssm_in         = create_tensor(tn(LLM_TENSOR_SSM_IN,         "weight", i), { n_embd, key_dim * 2 + value_dim * 2 }, TENSOR_NOT_REQUIRED);
-                            layer.wqkv           = create_tensor(tn(LLM_TENSOR_ATTN_QKV,       "weight", i), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED);
-                            layer.wqkv_gate      = create_tensor(tn(LLM_TENSOR_ATTN_GATE,      "weight", i), { n_embd, value_dim }, TENSOR_NOT_REQUIRED);
-                            layer.ssm_conv1d     = create_tensor(tn(LLM_TENSOR_SSM_CONV1D,     "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
-                            layer.ssm_dt         = create_tensor(tn(LLM_TENSOR_SSM_DT,         "bias",   i), { hparams.ssm_dt_rank }, 0);
-                            layer.ssm_a          = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN,             i), { hparams.ssm_dt_rank }, 0);
-                            layer.ssm_beta_alpha = create_tensor(tn(LLM_TENSOR_SSM_BETA_ALPHA, "weight", i), { n_embd, ba_dim }, 0);
-                            layer.ssm_norm       = create_tensor(tn(LLM_TENSOR_SSM_NORM,       "weight", i), { head_v_dim }, 0);
-                            layer.ssm_out        = create_tensor(tn(LLM_TENSOR_SSM_OUT,        "weight", i), { value_dim, n_embd }, 0);
-                        }
-
-                        // Dense FFN for all layers
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), { n_embd, n_ff }, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
-                    }
-                } break;
-            case LLM_ARCH_QWEN3_5_MOE:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
-
-                    // output
-                    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 }, TENSOR_NOT_REQUIRED);
-
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
-                    }
-
-                    const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
-
-                    // Calculate dimensions from hyperparameters
-                    const int64_t head_k_dim = hparams.ssm_d_state;
-                    const int64_t head_v_dim = hparams.ssm_d_state;
-                    const int64_t n_k_heads  = hparams.ssm_n_group;
-                    const int64_t n_v_heads  = hparams.ssm_dt_rank;
-                    const int64_t key_dim    = head_k_dim * n_k_heads;
-                    const int64_t value_dim  = head_v_dim * n_v_heads;
-                    const int64_t conv_dim   = key_dim * 2 + value_dim;
-
-                    const int64_t ba_dim = n_v_heads * 2;
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", i), { n_embd }, 0);
-                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0);
-
-                        if (!hparams.is_recurrent(i)) {
-                            // Full attention layers
-                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), { n_embd, n_embd_head_k * n_head * 2 }, 0);
-                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), { n_embd, n_embd_k_gqa }, 0);
-                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), { n_embd, n_embd_v_gqa }, 0);
-                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
-
-                            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
-                            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
-                        } else {
-                            // Linear attention (gated delta net) specific tensors
-                            layer.ssm_in         = create_tensor(tn(LLM_TENSOR_SSM_IN,         "weight", i), { n_embd, key_dim * 2 + value_dim * 2 }, TENSOR_NOT_REQUIRED);
-                            layer.wqkv           = create_tensor(tn(LLM_TENSOR_ATTN_QKV,       "weight", i), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED);
-                            layer.wqkv_gate      = create_tensor(tn(LLM_TENSOR_ATTN_GATE,      "weight", i), { n_embd, value_dim }, TENSOR_NOT_REQUIRED);
-                            layer.ssm_conv1d     = create_tensor(tn(LLM_TENSOR_SSM_CONV1D,     "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
-                            layer.ssm_dt         = create_tensor(tn(LLM_TENSOR_SSM_DT,         "bias",   i), { hparams.ssm_dt_rank }, 0);
-                            layer.ssm_a          = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN,             i), { hparams.ssm_dt_rank }, 0);
-                            layer.ssm_beta_alpha = create_tensor(tn(LLM_TENSOR_SSM_BETA_ALPHA, "weight", i), { n_embd, ba_dim }, 0);
-                            layer.ssm_norm       = create_tensor(tn(LLM_TENSOR_SSM_NORM,       "weight", i), { head_v_dim }, 0);
-                            layer.ssm_out        = create_tensor(tn(LLM_TENSOR_SSM_OUT,        "weight", i), { value_dim, n_embd }, 0);
-                        }
-
-                        // MoE FFN
-                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), { n_embd, n_expert }, 0);
-                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
-                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
-                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
-
                        // Shared experts
                        layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), { n_embd }, 0);
                        layer.ffn_gate_shexp     = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP,     "weight", i), { n_embd, hparams.n_ff_shexp }, 0);
@@ -7687,8 +7545,6 @@ void llama_model::print_info() const {
        arch == LLM_ARCH_PLAMO2 ||
        arch == LLM_ARCH_GRANITE_HYBRID ||
        arch == LLM_ARCH_QWEN3NEXT ||
-        arch == LLM_ARCH_QWEN3_5 ||
-        arch == LLM_ARCH_QWEN3_5_MOE ||
        arch == LLM_ARCH_NEMOTRON_H ||
        arch == LLM_ARCH_NEMOTRON_H_MOE) {
        LLAMA_LOG_INFO("%s: ssm_d_conv            = %u\n",     __func__, hparams.ssm_d_conv);
@@ -8487,14 +8343,6 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
            {
                llm = std::make_unique<llm_build_qwen3next>(*this, params);
            } break;
-        case LLM_ARCH_QWEN3_5:
-            {
-                llm = std::make_unique<llm_build_qwen3_5>(*this, params);
-            } break;
-        case LLM_ARCH_QWEN3_5_MOE:
-            {
-                llm = std::make_unique<llm_build_qwen3_5_moe>(*this, params);
-            } break;
        case LLM_ARCH_MISTRAL3:
            {
                llm = std::make_unique<llm_build_mistral3>(*this, params);
@@ -8755,8 +8603,6 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
        case LLM_ARCH_PANGU_EMBED:
        case LLM_ARCH_AFMOE:
        case LLM_ARCH_QWEN3NEXT:
-        case LLM_ARCH_QWEN3_5:
-        case LLM_ARCH_QWEN3_5_MOE:
        case LLM_ARCH_MIMO2:
        case LLM_ARCH_STEP35:
            return LLAMA_ROPE_TYPE_NEOX;
--- a/src/models/delta.cpp
+++ b/src/models/delta.cpp
@@ -1,618 +0,0 @@
-#include "models.h"
-#include "ggml.h"
-#include <cmath>
-#include <utility>
-#include <cassert>
-
-llm_graph_context_delta::llm_graph_context_delta(const llm_graph_params & params) : llm_graph_context_mamba(params) {}
-
-/**
- * Unified Delta Net implementation supporting both GDA and KDA modes.
- *
- * GDA (Gated Delta Attention): g has shape [H, T, B] in GGML (PyTorch: [B, T, H])
- *   - Per-head gating, broadcasts over K dimension
- *
- * KDA (Key-wise Delta Attention): g has shape [K, H, T, B] in GGML (PyTorch: [B, T, H, K])
- *   - Per-key gating
- *
- * The mode is auto-detected based on g's dimensionality.
- *
- * Tensor dimension convention:
- *   GGML: ne[0] is innermost (fastest varying), ne[3] is outermost
- *   PyTorch: dim 0 is outermost, dim -1 is innermost
- *   So GGML [A, B, C, D] corresponds to PyTorch [D, C, B, A]
- */
-
-// Helper to get a slice along dimension 2 (n_chunks dimension)
-static ggml_tensor * get_slice_2d(ggml_context * ctx, ggml_tensor * t, int64_t chunk) {
-    return ggml_view_4d(ctx, t,
-        t->ne[0], t->ne[1], 1, t->ne[3],
-        t->nb[1], t->nb[2], t->nb[3],
-        chunk * t->nb[2]);
-}
-
-/**
- * Unified chunked Delta Net implementation.
- *
- * Input tensor format matches qwen3next conventions:
- * @param q         Query tensor [S_k, H_k, n_tokens, n_seqs]
- * @param k         Key tensor [S_k, H_k, n_tokens, n_seqs]
- * @param v         Value tensor [S_v, H_v, n_tokens, n_seqs]
- * @param g         Gate tensor:
- *                    GDA: [H_v, n_tokens, n_seqs]
- *                    KDA: [S_k, H_v, n_tokens, n_seqs]
- * @param beta      Beta tensor [H_v, 1, n_tokens, n_seqs]
- * @param state     State tensor [S_v, S_v * H_v, 1, n_seqs]
- * @param causal_mask   Lower triangular mask [chunk_size, chunk_size]
- * @param identity      Identity matrix [chunk_size, chunk_size]
- * @param diag_mask     Diagonal mask [chunk_size, chunk_size]
- * @param il            Layer index (for debugging callbacks)
- * @param chunk_size    Chunk size for chunked processing
- * @param eps_norm      Epsilon for L2 normalization
- *
- * @return Pair of (output_tokens, new_state)
- */
-std::pair<ggml_tensor *, ggml_tensor *> llm_graph_context_delta::build_delta_net_unified_chunking(
-        ggml_context * ctx0,
-        ggml_tensor * q,
-        ggml_tensor * k,
-        ggml_tensor * v,
-        ggml_tensor * g,
-        ggml_tensor * beta,
-        ggml_tensor * state_reshaped,
-        ggml_tensor * causal_mask,
-        ggml_tensor * identity,
-        ggml_tensor * diag_mask,
-        int           il,
-        int64_t       chunk_size,
-        float         eps_norm) {
-
-    // Input format: [S, H, n_tokens, n_seqs] (matching qwen3next convention)
-    const int64_t S_k      = q->ne[0];
-    const int64_t H_k      = q->ne[1];
-    const int64_t n_tokens = q->ne[2];
-    const int64_t n_seqs   = q->ne[3];
-
-    const int64_t S_v = v->ne[0];
-    const int64_t H_v = v->ne[1];
-
-    // Detect KDA vs GDA based on g's shape
-    // GDA: g has shape [H_v, n_tokens, n_seqs]
-    // KDA: g has shape [S_k, H_v, n_tokens, n_seqs] (4D with ne[0]=S_k)
-    const bool is_kda = (g->ne[0] == S_k && g->ne[1] == H_v);
-
-    // Validate tensor shapes
-    GGML_ASSERT(v->ne[2] == n_tokens);
-    GGML_ASSERT(k->ne[2] == n_tokens);
-    GGML_ASSERT(state_reshaped->ne[0] == S_v && state_reshaped->ne[1] == S_v && state_reshaped->ne[2] == H_v && state_reshaped->ne[3] == n_seqs);
-    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
-    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
-    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
-    GGML_ASSERT(H_k == H_v);
-
-    if (is_kda) {
-        // KDA: g shape [S_k, H_v, n_tokens, n_seqs]
-        GGML_ASSERT(g->ne[0] == S_k && g->ne[1] == H_v && g->ne[2] == n_tokens && g->ne[3] == n_seqs);
-    } else {
-        // GDA: g shape [H_v, n_tokens, n_seqs]
-        GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
-    }
-
-    // L2 normalize q and k
-    q = ggml_l2_norm(ctx0, q, eps_norm);
-    k = ggml_l2_norm(ctx0, k, eps_norm);
-
-    const float scale = 1.0f / sqrtf((float)S_v);
-    q = ggml_scale(ctx0, q, scale);
-
-    beta = ggml_sigmoid(ctx0, beta);
-
-    cb(q, "q_in", il);
-    cb(k, "k_in", il);
-    cb(v, "v_in", il);
-    cb(beta, "beta_in", il);
-    cb(g, "g_in", il);
-
-    // Permute tensors to working format [S, n_tokens, H, n_seqs]
-    // Input: [S, H, n_tokens, n_seqs] -> permute(0, 2, 1, 3) -> [S, n_tokens, H, n_seqs]
-    q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_k, n_tokens, H_k, n_seqs);
-    k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_k, n_tokens, H_k, n_seqs);
-    v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
-    if (is_kda) {
-        g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 0, 2, 1, 3), S_k, n_tokens, H_k, n_seqs);
-    } else {
-        g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs);
-    }
-    beta = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
-
-    cb(q, "q_perm", il);
-    cb(k, "k_perm", il);
-    cb(v, "v_perm", il);
-    cb(beta, "beta_perm", il);
-    cb(g, "g_perm", il);
-    cb(state_reshaped, "state_in", il);
-
-    // Padding for chunk processing
-    const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size;
-    const int64_t n_chunks = (n_tokens + pad) / chunk_size;
-
-    q = ggml_pad(ctx0, q, 0, pad, 0, 0);
-    k = ggml_pad(ctx0, k, 0, pad, 0, 0);
-    v = ggml_pad(ctx0, v, 0, pad, 0, 0);
-    beta = ggml_pad(ctx0, beta, 0, pad, 0, 0);
-    g = ggml_pad(ctx0, g, pad, 0, 0, 0);
-
-
-    cb(q, "q_pad", il);
-    cb(k, "k_pad", il);
-    cb(v, "v_pad", il);
-    cb(beta, "beta_pad", il);
-    cb(g, "g_pad", il);
-
-    ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
-    ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
-
-    cb(v_beta, "v_beta", il);
-    cb(k_beta, "k_beta", il);
-
-    // Reshape to chunks
-    q      = ggml_reshape_4d(ctx0, q,      S_k, chunk_size, n_chunks, H_k * n_seqs);
-    k      = ggml_reshape_4d(ctx0, k,      S_k, chunk_size, n_chunks, H_k * n_seqs);
-    k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
-    v      = ggml_reshape_4d(ctx0, v,      S_v, chunk_size, n_chunks, H_v * n_seqs);
-    v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
-    beta   = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
-
-    // Reshape g for chunks
-    ggml_tensor * g_cumsum;
-    ggml_tensor * g_cumsum_t;
-    if (is_kda) {
-        // KDA: g [S_k, n_tokens+pad, H_k, n_seqs] -> [S_k, chunk_size, n_chunks, H_k * n_seqs]
-        g = ggml_reshape_4d(ctx0, g, S_k, chunk_size, n_chunks, H_k * n_seqs);
-        // Cumsum along chunk_size dimension (ne[1])
-        // GGML cumsum operates on ne[0], so we need to transpose, cumsum, transpose back
-        g = ggml_cont(ctx0, ggml_transpose(ctx0, g));  // [chunk_size, S_k, n_chunks, H_k * n_seqs]
-        g_cumsum_t = ggml_cumsum(ctx0, g);
-        g_cumsum = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum_t));  // [S_k, chunk_size, n_chunks, H_k * n_seqs]
-    } else {
-        // GDA: g [n_tokens+pad, 1, H_k, n_seqs] -> [chunk_size, 1, n_chunks, H_k * n_seqs]
-        g = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
-        g_cumsum = ggml_cumsum(ctx0, g);
-        g_cumsum_t = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_k * n_seqs);
-    }
-
-    cb(g_cumsum, "g_cumsum", il);
-
-    // Build attention matrix A for the WY representation solve
-    // For GDA: A[j,i] = sum_k(k[j,k] * exp(g[j] - g[i]) * k[i,k]) = (k @ k^T) * exp(g[j] - g[i])
-    // For KDA: A[j,i] = sum_k(k_beta[j,k] * exp(g[j,k] - g[i,k]) * k[i,k])
-    // KDA uses decay mask with S_k packed into batch to compute exp(g[j,k] - g[i,k]) per-key
-
-    ggml_tensor * k_decay;
-    ggml_tensor * decay_mask = nullptr;
-    ggml_tensor * g_exp_pos = nullptr;
-
-    if (is_kda) {
-        // KDA: Use decay mask with S_k in leading dimension for efficient mul_mat reduction
-        // A[j,i] = sum_k(k_beta[j,k] * exp(g[j,k] - g[i,k]) * k[i,k])
-        // By putting S_k in dim 0, mul_mat implicitly sums over it
-
-        const int64_t CHB = n_chunks * H_k * n_seqs;
-
-        // g_cumsum_t is [chunk_size, S_k, n_chunks, H_k * n_seqs]
-        // Reshape to [chunk_size, S_k, CHB] then build decay mask
-        ggml_tensor * gcs = ggml_reshape_3d(ctx0, g_cumsum_t, chunk_size, S_k, CHB);
-        ggml_tensor * gcs_i = ggml_reshape_4d(ctx0, gcs, chunk_size, 1, S_k, CHB);
-        ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, gcs, 1, chunk_size, S_k, CHB);
-
-        // Build decay mask: [chunk_size, chunk_size, S_k, CHB]
-        ggml_tensor * gcs_j_bc = ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, S_k, CHB);
-        decay_mask = ggml_sub(ctx0, gcs_j_bc, gcs_i);
-
-        cb(decay_mask, "decay_mask_kda", il);
-
-        decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
-        decay_mask = ggml_exp(ctx0, decay_mask);
-        decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
-
-        // Permute to [S_k, chunk_size_j, chunk_size_i, CHB] for mul_mat reduction over S_k
-        decay_mask = ggml_cont_4d(ctx0, ggml_permute(ctx0, decay_mask, 2, 1, 0, 3), S_k, chunk_size, chunk_size, CHB);
-
-        // Reshape k and k_beta for broadcasting with decay_mask
-        // k_i: indexed at position i (dim 2 of decay_mask)
-        // k_beta_j: indexed at position j (dim 1 of decay_mask)
-        ggml_tensor * k_i = ggml_reshape_4d(ctx0, k, S_k, 1, chunk_size, CHB);
-        ggml_tensor * k_beta_j = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, 1, CHB);
-
-        // decay_k_beta_j[s,j,i,b] = decay[s,j,i,b] * k_beta[s,j,b]
-        ggml_tensor * decay_k_beta_j = ggml_mul(ctx0, decay_mask, k_beta_j);
-
-        // mul_mat sums over S_k: result[j,1,i,CHB] = sum_s decay_k_beta_j[s,j,i,b] * k_i[s,1,i,b]
-        k_decay = ggml_mul_mat(ctx0, decay_k_beta_j, k_i);
-        k_decay = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_4d(ctx0, k_decay, chunk_size, chunk_size, n_chunks, H_k * n_seqs)));
-
-        // g_exp_pos is still needed for later (kbeta_gexp, etc.)
-        g_exp_pos = ggml_exp(ctx0, g_cumsum);
-    } else {
-        // GDA: Use decay mask approach (g broadcasts over K dimension)
-        // g_cumsum [chunk_size, 1, n_chunks, H_v * n_seqs]
-        ggml_tensor * gcs_i = g_cumsum;
-        ggml_tensor * gcs_j = g_cumsum_t;
-        g_exp_pos = ggml_exp(ctx0, g_cumsum_t);
-        ggml_tensor * gcs_j_broadcast = ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
-        decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
-
-        cb(decay_mask, "decay_mask", il);
-
-        decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
-        decay_mask = ggml_exp(ctx0, decay_mask);
-        decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
-
-        ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
-        k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
-    }
-
-    ggml_tensor * attn = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
-
-    cb(attn, "attn_pre_solve", il);
-
-    // Solve triangular system: (I + L) @ X = I, where L is strictly lower triangular
-    ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
-    ggml_tensor * lhs = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
-    ggml_tensor * lin_solve = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
-    attn = ggml_mul(ctx0, lin_solve, causal_mask);
-    attn = ggml_add(ctx0, attn, identity);
-
-    cb(attn, "attn_solved", il);
-
-    // Compute u = A @ v and w = A @ (g.exp() * k)
-    v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
-
-    ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, g_exp_pos);
-    cb(kbeta_gexp, "kbeta_gexp", il);
-
-    ggml_tensor * k_cumdecay = ggml_cont(ctx0, ggml_transpose(ctx0,
-        ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
-    cb(k_cumdecay, "k_cumdecay", il);
-
-    // Attention scores q @ k^T with decay
-    // For GDA: attn_kq[j,i] = sum_k(q[j,k] * exp(g[j] - g[i]) * k[i,k])
-    // For KDA: attn_kq[j,i] = sum_k(q[j,k] * exp(g[j,k] - g[i,k]) * k[i,k])
-    ggml_tensor * attn_kq;
-    if (is_kda) {
-        // KDA: Same approach as k_decay - use decay_mask with S_k in leading dim
-        const int64_t CHB = n_chunks * H_k * n_seqs;
-
-        // Rebuild decay mask (same structure as k_decay)
-        ggml_tensor * gcs = ggml_reshape_3d(ctx0, g_cumsum_t, chunk_size, S_k, CHB);
-        ggml_tensor * gcs_i = ggml_reshape_4d(ctx0, gcs, chunk_size, 1, S_k, CHB);
-        ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, gcs, 1, chunk_size, S_k, CHB);
-        ggml_tensor * gcs_j_bc = ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, S_k, CHB);
-        ggml_tensor * decay_mask_kq = ggml_sub(ctx0, gcs_j_bc, gcs_i);
-
-        decay_mask_kq = ggml_mul(ctx0, decay_mask_kq, diag_mask);
-        decay_mask_kq = ggml_exp(ctx0, decay_mask_kq);
-        decay_mask_kq = ggml_mul(ctx0, decay_mask_kq, diag_mask);
-
-        // Permute to [S_k, chunk_size_j, chunk_size_i, CHB]
-        decay_mask_kq = ggml_cont_4d(ctx0, ggml_permute(ctx0, decay_mask_kq, 2, 1, 0, 3), S_k, chunk_size, chunk_size, CHB);
-
-        // q_j: indexed at position j, k_i: indexed at position i
-        ggml_tensor * q_j = ggml_reshape_4d(ctx0, q, S_k, chunk_size, 1, CHB);
-        ggml_tensor * k_i = ggml_reshape_4d(ctx0, k, S_k, 1, chunk_size, CHB);
-
-        // decay_q_j[s,j,i,b] = decay[s,j,i,b] * q[s,j,b]
-        ggml_tensor * decay_q_j = ggml_mul(ctx0, decay_mask_kq, q_j);
-
-        // mul_mat sums over S_k
-        attn_kq = ggml_mul_mat(ctx0, decay_q_j, k_i);
-        attn_kq = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_4d(ctx0, attn_kq, chunk_size, chunk_size, n_chunks, H_k * n_seqs)));
-    } else {
-        // GDA: Use decay mask
-        attn_kq = ggml_mul_mat(ctx0, k, q);
-        attn_kq = ggml_mul(ctx0, attn_kq, decay_mask);
-        attn_kq = ggml_mul(ctx0, attn_kq, diag_mask);
-    }
-    cb(attn_kq, "attn_kq", il);
-
-    // Compute g_last and g_diff for state updates
-    ggml_tensor * g_last;
-    ggml_tensor * g_diff_exp;
-    ggml_tensor * g_last_exp;
-
-    if (is_kda) {
-        // KDA: g_cumsum [S_k, chunk_size, n_chunks, H_k * n_seqs]
-        // Get last element along chunk_size dimension (ne[1])
-        g_last = ggml_view_4d(ctx0, g_cumsum,
-            g_cumsum->ne[0], 1, g_cumsum->ne[2], g_cumsum->ne[3],
-            g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
-            (g_cumsum->ne[1] - 1) * g_cumsum->nb[1]);
-        g_last = ggml_cont(ctx0, g_last);
-        g_last_exp = ggml_exp(ctx0, g_last);
-
-        // g_diff = g_last - g_cumsum
-        ggml_tensor * g_last_broadcast = ggml_repeat_4d(ctx0, g_last,
-            g_cumsum->ne[0], g_cumsum->ne[1], g_cumsum->ne[2], g_cumsum->ne[3]);
-        ggml_tensor * g_diff = ggml_sub(ctx0, g_last_broadcast, g_cumsum);
-        g_diff_exp = ggml_exp(ctx0, g_diff);
-    } else {
-        // GDA: g_cumsum [chunk_size, 1, n_chunks, H_k * n_seqs]
-        g_last = ggml_view_4d(ctx0, g_cumsum,
-            1, 1, g_cumsum->ne[2], g_cumsum->ne[3],
-            g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
-            (g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum));
-        g_last = ggml_cont(ctx0, g_last);
-        g_last_exp = ggml_exp(ctx0, g_last);
-
-        ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last));
-        g_diff_exp = ggml_exp(ctx0, g_diff);
-    }
-
-    cb(g_last, "g_last", il);
-    cb(g_last_exp, "g_last_exp", il);
-
-    ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp);
-    cb(key_gdiff, "key_gdiff", il);
-
-    // Process chunks
-    ggml_tensor * new_state = state_reshaped;
-    ggml_tensor * core_attn_out = nullptr;
-
-    for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
-        ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk);
-        ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk);
-        ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk);
-        ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk);
-        ggml_tensor * gexp_chunk = get_slice_2d(ctx0, g_exp_pos, chunk);
-
-        cb(attn_chunk, "attn_chunk", il);
-
-        ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3),
-            S_v, S_v, 1, H_v * n_seqs);
-
-        // v_prime = k_cumdecay @ state
-        ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
-        cb(v_prime, "v_prime_chunk", il);
-
-        // v_new = v - v_prime
-        ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime);
-        ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
-        cb(v_new, "v_new_chunk", il);
-
-        // attn_inter = (q * g.exp()) @ state
-        ggml_tensor * q_g_exp = ggml_mul(ctx0, q_chunk, gexp_chunk);
-        ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
-        cb(attn_inter, "attn_inter_chunk", il);
-
-        // output = attn_inter + attn @ v_new
-        ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk);
-        cb(v_attn, "v_attn_chunk", il);
-
-        ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
-        cb(core_attn_out_chunk, "core_attn_out_chunk", il);
-
-        core_attn_out = core_attn_out == nullptr
-            ? core_attn_out_chunk
-            : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2);
-
-        // State update: state = state * g_last_exp + key_gdiff^T @ v_new
-        ggml_tensor * k_gdiff = ggml_cont(ctx0, get_slice_2d(ctx0, key_gdiff, chunk));
-        ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, ggml_cont(ctx0, ggml_transpose(ctx0, k_gdiff)));
-
-        ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk));
-
-        if (is_kda) {
-            // KDA: g_last_exp [S_k, 1, n_chunks, H_k * n_seqs]
-            // State: [S_v, S_v, H_v, n_seqs]
-            // Need to reshape g_last_exp to broadcast correctly over V dimension only
-            gexp_last_chunk = ggml_reshape_4d(ctx0, gexp_last_chunk,
-                1, gexp_last_chunk->ne[0], H_v, n_seqs);  // [1, S_k, H_v, n_seqs]
-            // Transpose to [S_k, 1, H_v, n_seqs] then broadcast
-            gexp_last_chunk = ggml_cont(ctx0, ggml_permute(ctx0, gexp_last_chunk, 1, 0, 2, 3));
-        } else {
-            // GDA: g_last_exp [1, 1, n_chunks, H_k * n_seqs]
-            // Broadcasts over both K and V dimensions
-            gexp_last_chunk = ggml_reshape_4d(ctx0, gexp_last_chunk,
-                gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs);
-        }
-
-        new_state = ggml_add(ctx0,
-            ggml_mul(ctx0, new_state, gexp_last_chunk),
-            ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
-    }
-
-    // Truncate padding and permute back
-    ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out,
-        S_v, n_tokens, H_v, n_seqs,
-        ggml_row_size(core_attn_out->type, S_v),
-        ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks),
-        ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0);
-    output_tokens = ggml_cont(ctx0, output_tokens);
-
-    cb(output_tokens, "output_tokens", il);
-
-    output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3);
-    output_tokens = ggml_cont(ctx0, output_tokens);
-
-    return {output_tokens, new_state};
-}
-
-
-/**
- * Unified autoregressive Delta Net implementation (single token processing).
- *
- * This implementation uses matrix multiplication instead of elementwise operations + summation,
- * which is more efficient and mathematically equivalent. See inline comments for equivalences.
- *
- * Input tensor format matches qwen3next conventions:
- * @param q         Query tensor [S_k, H_k, 1, n_seqs]
- * @param k         Key tensor [S_k, H_k, 1, n_seqs]
- * @param v         Value tensor [S_v, H_v, 1, n_seqs]
- * @param g         Gate tensor:
- *                    GDA: [H_v, 1, n_seqs]
- *                    KDA: [S_k, H_v, 1, n_seqs]
- * @param beta      Beta tensor [H_v, 1, 1, n_seqs]
- * @param state     State tensor [S_v, S_v * H_v, 1, n_seqs]
- * @param il        Layer index (for debugging callbacks)
- * @param eps_norm  Epsilon for L2 normalization
- *
- * @return Pair of (output_tokens, new_state)
- */
-std::pair<ggml_tensor *, ggml_tensor *> llm_graph_context_delta::build_delta_net_unified_autoregressive(
-        ggml_context * ctx0,
-        ggml_tensor * q,
-        ggml_tensor * k,
-        ggml_tensor * v,
-        ggml_tensor * g,
-        ggml_tensor * beta,
-        ggml_tensor * state,
-        int           il,
-        float         eps_norm) {
-
-    // Input format: [S, H, n_tokens, n_seqs] (matching qwen3next convention)
-    const int64_t S_k      = q->ne[0];
-    const int64_t H_k      = q->ne[1];
-    const int64_t n_tokens = q->ne[2];
-    const int64_t n_seqs   = q->ne[3];
-
-    const int64_t S_v = v->ne[0];
-    const int64_t H_v = v->ne[1];
-
-    GGML_ASSERT(n_tokens == 1);  // Autoregressive mode is for single token
-
-    // Detect KDA vs GDA based on g's shape
-    // GDA: g has shape [H_v, 1, n_seqs] or [H_v, n_tokens, n_seqs]
-    // KDA: g has shape [S_k, H_v, 1, n_seqs] or [S_k, H_v, n_tokens, n_seqs]
-    const bool is_kda = (g->ne[0] == S_k && g->ne[1] == H_v);
-
-    // Validate shapes
-    GGML_ASSERT(v->ne[2] == n_tokens);
-    GGML_ASSERT(k->ne[2] == n_tokens);
-    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v && state->ne[2] == H_v && state->ne[3] == n_seqs);
-    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
-    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
-    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
-    GGML_ASSERT(H_k == H_v);
-
-    if (is_kda) {
-        GGML_ASSERT(g->ne[0] == S_k && g->ne[1] == H_v);
-    } else {
-        GGML_ASSERT(g->ne[0] == H_v);
-    }
-
-    // L2 normalize q and k
-    q = ggml_l2_norm(ctx0, q, eps_norm);
-    k = ggml_l2_norm(ctx0, k, eps_norm);
-
-    const float scale = 1.0f / sqrtf((float)S_v);
-    q = ggml_scale(ctx0, q, scale);
-    beta = ggml_sigmoid(ctx0, beta);
-
-    cb(q, "q_in", il);
-    cb(k, "k_in", il);
-    cb(v, "v_in", il);
-    cb(beta, "beta_in", il);
-    cb(g, "g_in", il);
-
-    // Reshape g and beta for broadcasting
-    ggml_tensor * g_t;
-    ggml_tensor * beta_t;
-
-    if (is_kda) {
-        // KDA: g [S_k, H_v, 1, n_seqs] -> [S_k, 1, H_k, n_seqs]
-        // For state multiplication, need [1, S_k, H_v, n_seqs] to broadcast over V only
-        g_t = ggml_reshape_4d(ctx0, g, S_k, 1, H_k, n_seqs);
-    } else {
-        // GDA: g [H_v, 1, n_seqs] -> [1, 1, H_k, n_seqs]
-        // For state multiplication, broadcasts over both K and V
-        g_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs);
-    }
-
-    beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
-
-    // Apply exponential to g_t
-    g_t = ggml_exp(ctx0, g_t);
-
-    // State decay: state = state * exp(g)
-    if (is_kda) {
-        // KDA: g_t [S_k, 1, H_k, n_seqs], state [S_v, S_v, H_v, n_seqs]
-        // Need to broadcast g_t over V dimension (ne[0] of state)
-        // Permute g_t to [1, S_k, H_k, n_seqs] for correct broadcasting
-        ggml_tensor * g_broadcast = ggml_cont(ctx0, ggml_permute(ctx0, g_t, 1, 0, 2, 3));
-        state = ggml_mul(ctx0, state, g_broadcast);
-    } else {
-        // GDA: g_t [1, 1, H_k, n_seqs] broadcasts over both dimensions
-        state = ggml_mul(ctx0, state, g_t);
-    }
-
-    // Equivalence to previous version:
-    // Previous: kv_mem = sum_k(state * k) using elementwise mult + sum_rows
-    // Current:  k_state = state_t @ k_t using matrix multiplication
-    // These are equivalent because: sum_k(A * B) = A @ B when dimensions align
-    ggml_tensor * state_t = ggml_cont(ctx0, ggml_transpose(ctx0, state));
-    ggml_tensor * k_t = ggml_reshape_4d(ctx0, k, S_k, 1, H_k, n_seqs);
-    ggml_tensor * k_state = ggml_mul_mat(ctx0, state_t, k_t);
-
-    // v_diff = v - k_state (equivalent to v - kv_mem in previous version)
-    ggml_tensor * v_t = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
-    ggml_tensor * v_diff = ggml_sub(ctx0, v_t, k_state);
-    ggml_tensor * k_beta = ggml_mul(ctx0, k_t, beta_t);
-
-    // Equivalence to previous version:
-    // Previous: state += k.unsqueeze(-1) * delta where delta = (v - kv_mem) * beta
-    // Current:  state += v_diff^T @ k_beta^T using matrix multiplication
-    // These are equivalent because: outer_product(k, v_diff * beta) = v_diff^T @ k^T
-    state = ggml_add(ctx0, state, ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_diff)), ggml_cont(ctx0, ggml_transpose(ctx0, k_beta))));
-
-    // Equivalence to previous version:
-    // Previous: core_attn_out = sum_k(state * q) using elementwise mult + sum_rows
-    // Current:  core_attn_out = state_t @ q using matrix multiplication
-    // These are equivalent because: sum_k(A * B) = A @ B when dimensions align
-    q = ggml_reshape_4d(ctx0, q, S_k, 1, H_k, n_seqs);
-    state_t = ggml_cont(ctx0, ggml_transpose(ctx0, state));
-    ggml_tensor * core_attn_out = ggml_mul_mat(ctx0, state_t, q);
-    // core_attn_out should be [S_v, 1, H_v, n_seqs] after this
-    cb(core_attn_out, "output_tokens", il);
-    cb(state, "new_state", il);
-
-    return {core_attn_out, state};
-}
-
-
-/**
- * Main entry point that dispatches to chunked or autoregressive based on n_tokens.
- *
- * Input tensor format matches qwen3next conventions:
- * @param q         Query tensor [S_k, H_k, n_tokens, n_seqs]
- * @param k         Key tensor [S_k, H_k, n_tokens, n_seqs]
- * @param v         Value tensor [S_v, H_v, n_tokens, n_seqs]
- * @param g         Gate tensor (GDA: [H_v, n_tokens, n_seqs], KDA: [S_k, H_v, n_tokens, n_seqs])
- * @param beta      Beta tensor [H_v, 1, n_tokens, n_seqs]
- * @param state     State tensor [S_v, S_v * H_v, 1, n_seqs]
- */
-std::pair<ggml_tensor *, ggml_tensor *> llm_graph_context_delta::build_delta_net_unified(
-        ggml_context * ctx0,
-        ggml_tensor * q,
-        ggml_tensor * k,
-        ggml_tensor * v,
-        ggml_tensor * g,
-        ggml_tensor * beta,
-        ggml_tensor * state,
-        ggml_tensor * causal_mask,
-        ggml_tensor * identity,
-        ggml_tensor * diag_mask,
-        int           il,
-        int64_t       chunk_size,
-        float         eps_norm) {
-
-    // Input format: [S, H, n_tokens, n_seqs] (matching qwen3next convention)
-    const int64_t n_tokens = q->ne[2];
-
-    if (n_tokens == 1) {
-        return build_delta_net_unified_autoregressive(
-            ctx0, q, k, v, g, beta, state, il, eps_norm);
-    }
-    return build_delta_net_unified_chunking(
-        ctx0, q, k, v, g, beta, state, causal_mask, identity, diag_mask,
-        il, chunk_size, eps_norm);
-}
--- a/src/models/kimi-linear.cpp
+++ b/src/models/kimi-linear.cpp
@@ -1,4 +1,5 @@
 #include "models.h"
+#include "ggml.h"

 #define CHUNK_SIZE 64

--- a/src/models/models.h
+++ b/src/models/models.h
@@ -17,53 +17,6 @@ struct llm_graph_context_mamba : public llm_graph_context {

 };

-struct llm_graph_context_delta : public llm_graph_context_mamba {
-    llm_graph_context_delta(const llm_graph_params & params);
-
-    virtual ~llm_graph_context_delta() = default;
-
-    std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_unified_chunking(
-        ggml_context * ctx0,
-        ggml_tensor * q,
-        ggml_tensor * k,
-        ggml_tensor * v,
-        ggml_tensor * g,
-        ggml_tensor * beta,
-        ggml_tensor * state,
-        ggml_tensor * causal_mask,
-        ggml_tensor * identity,
-        ggml_tensor * diag_mask,
-        int           il,
-        int64_t       chunk_size,
-        float         eps_norm);
-
-    std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_unified_autoregressive(
-        ggml_context * ctx0,
-        ggml_tensor * q,
-        ggml_tensor * k,
-        ggml_tensor * v,
-        ggml_tensor * g,
-        ggml_tensor * beta,
-        ggml_tensor * state,
-        int           il,
-        float         eps_norm);
-
-    std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_unified(
-        ggml_context * ctx0,
-        ggml_tensor * q,
-        ggml_tensor * k,
-        ggml_tensor * v,
-        ggml_tensor * g,
-        ggml_tensor * beta,
-        ggml_tensor * state,
-        ggml_tensor * causal_mask,
-        ggml_tensor * identity,
-        ggml_tensor * diag_mask,
-        int           il,
-        int64_t       chunk_size,
-        float         eps_norm);
-};
-
 // Base class for RWKV-related models
 struct llm_build_rwkv6_base : public llm_graph_context {
    const llama_model & model;
@@ -523,7 +476,7 @@ struct llm_build_qwen3vl : public llm_graph_context {
 struct llm_build_qwen3vlmoe : public llm_graph_context {
    llm_build_qwen3vlmoe(const llama_model & model, const llm_graph_params & params);
 };
-struct llm_build_qwen3next : public llm_graph_context_delta {
+struct llm_build_qwen3next : public llm_graph_context_mamba {
    llm_build_qwen3next(const llama_model & model, const llm_graph_params & params);
 private:
    ggml_tensor * build_layer_attn(
@@ -581,59 +534,6 @@ private:
    const llama_model & model;
 };

-struct llm_build_qwen3_5 : public llm_graph_context_delta {
-    llm_build_qwen3_5(const llama_model & model, const llm_graph_params & params);
-
-protected:
-    // Tag type for subclass constructors that need to call build_graph() themselves
-    // (to ensure virtual dispatch works correctly)
-    struct defer_graph_build_t {};
-
-    llm_build_qwen3_5(const llama_model & model, const llm_graph_params & params, defer_graph_build_t);
-
-    void build_graph();
-
-    virtual ggml_tensor * build_layer_ffn(
-                ggml_tensor * cur,
-                        int   il);
-
-    const llama_model & model;
-
-private:
-    ggml_tensor * build_layer_attn(
-    llm_graph_input_attn_kv * inp_attn,
-                ggml_tensor * cur,
-                ggml_tensor * inp_pos,
-                        int   il);
-
-    ggml_tensor * build_layer_attn_linear(
-         llm_graph_input_rs * inp,
-                ggml_tensor * cur,
-                ggml_tensor * causal_mask,
-                ggml_tensor * identity,
-                ggml_tensor * diag_mask,
-                        int   il);
-
-    ggml_tensor * build_norm_gated(
-                ggml_tensor * input,
-                ggml_tensor * weights,
-                ggml_tensor * gate,
-                        int   layer);
-
-    std::pair<ggml_tensor *, ggml_tensor *> build_qkvz(
-                ggml_tensor * input,
-                        int   il);
-};
-
-struct llm_build_qwen3_5_moe : public llm_build_qwen3_5 {
-    llm_build_qwen3_5_moe(const llama_model & model, const llm_graph_params & params);
-
-protected:
-    ggml_tensor * build_layer_ffn(
-                ggml_tensor * cur,
-                        int   il) override;
-};
-
 struct llm_build_qwen : public llm_graph_context {
    llm_build_qwen(const llama_model & model, const llm_graph_params & params);
 };
--- a/src/models/qwen3-5.cpp
+++ b/src/models/qwen3-5.cpp
@@ -1,421 +0,0 @@
-#include "models.h"
-
-#define CHUNK_SIZE 64
-
-llm_build_qwen3_5::llm_build_qwen3_5(const llama_model & model, const llm_graph_params & params) :
-    llm_graph_context_delta(params), model(model) {
-    build_graph();
-}
-
-// virtual call in constructor fix
-llm_build_qwen3_5::llm_build_qwen3_5(const llama_model & model, const llm_graph_params & params, defer_graph_build_t /*tag*/) :
-    llm_graph_context_delta(params), model(model) {
-}
-
-void llm_build_qwen3_5::build_graph() {
-    ggml_tensor * cur;
-    ggml_tensor * inpL;
-
-    inpL = build_inp_embd(model.tok_embd);
-    cb(inpL, "model.embed_tokens", -1);
-
-    auto * inp = build_inp_mem_hybrid();
-
-    ggml_tensor * inp_pos     = build_inp_pos();
-    ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-    ggml_tensor * causal_mask =
-        ggml_tri(ctx0, ggml_fill(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, CHUNK_SIZE, CHUNK_SIZE), 1.0f),
-                    GGML_TRI_TYPE_LOWER);
-
-    ggml_tensor * identity = ggml_diag(ctx0, ggml_fill(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, CHUNK_SIZE), 1.0f));
-    ggml_tensor * diag_mask = ggml_add(ctx0, causal_mask, identity);
-
-    ggml_build_forward_expand(gf, causal_mask);
-    ggml_build_forward_expand(gf, identity);
-    ggml_build_forward_expand(gf, diag_mask);
-
-    for (int il = 0; il < n_layer; ++il) {
-        ggml_tensor * inpSA = inpL;
-
-        cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
-        cb(cur, "attn_norm", il);
-
-        if (hparams.is_recurrent(il)) {
-            cur = build_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, diag_mask, il);
-        } else {
-            cur = build_layer_attn(inp->get_attn(), cur, inp_pos, 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);
-        }
-
-        cur = ggml_add(ctx0, cur, inpSA);
-        cb(cur, "attn_residual", il);
-
-        ggml_tensor * ffn_residual = cur;
-
-        ggml_tensor * attn_post_norm = build_norm(cur, model.layers[il].attn_post_norm, nullptr, LLM_NORM_RMS, il);
-        cb(attn_post_norm, "attn_post_norm", il);
-
-        cur = build_layer_ffn(attn_post_norm, il);
-        cb(cur, "ffn_out", il);
-
-        cur = ggml_add(ctx0, cur, ffn_residual);
-        cb(cur, "post_ffn", il);
-
-        inpL = cur;
-    }
-    cur = inpL;
-
-    cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1);
-
-    cb(cur, "result_norm", -1);
-    res->t_embd = cur;
-
-    cur = build_lora_mm(model.output, cur);
-
-    cb(cur, "result_output", -1);
-    res->t_logits = cur;
-
-    ggml_build_forward_expand(gf, cur);
-}
-
-ggml_tensor * llm_build_qwen3_5::build_norm_gated(
-        ggml_tensor * input,
-        ggml_tensor * weights,
-        ggml_tensor * gate,
-        int           layer) {
-    ggml_tensor * normalized = build_norm(input, weights, nullptr, LLM_NORM_RMS, layer);
-    ggml_tensor * gated_silu = ggml_silu(ctx0, gate);
-
-    return ggml_mul(ctx0, normalized, gated_silu);
-}
-
-ggml_tensor * llm_build_qwen3_5::build_layer_attn(
-        llm_graph_input_attn_kv * inp,
-        ggml_tensor *             cur,
-        ggml_tensor *             inp_pos,
-        int                       il) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-    ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur); // [ (n_embd_head * 2) * n_head, n_tokens ]
-    cb(Qcur_full, "Qcur_full", il);
-
-    ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,
-        ggml_element_size(Qcur_full) * n_embd_head * 2,
-        ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head, 0);
-    cb(Qcur, "Qcur_reshaped", il);
-
-    Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
-    cb(Qcur, "Qcur_normed", il);
-
-    ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-    cb(Kcur, "Kcur", il);
-
-    ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-    cb(Vcur, "Vcur", il);
-
-    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
-    Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
-    cb(Kcur, "Kcur_normed", il);
-
-    ggml_tensor * gate = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,
-        ggml_element_size(Qcur_full) * n_embd_head * 2,
-        ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head,
-        ggml_element_size(Qcur_full) * n_embd_head);
-    gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
-    cb(gate, "gate_reshaped", il);
-
-    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-    Qcur = ggml_rope_ext(
-            ctx0, Qcur, inp_pos, nullptr,
-            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-            ext_factor, attn_factor, beta_fast, beta_slow);
-
-    Kcur = ggml_rope_ext(
-            ctx0, Kcur, inp_pos, nullptr,
-            n_rot, rope_type, n_ctx_orig, freq_base,
-            freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
-
-    cb(Qcur, "Qcur", il);
-    cb(Kcur, "Kcur", il);
-    cb(Vcur, "Vcur", il);
-
-    const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
-
-    cur = build_attn(inp,
-                nullptr, nullptr,
-                Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
-    cb(cur, "attn_pregate", il);
-
-    ggml_tensor * gate_sigmoid = ggml_sigmoid(ctx0, gate);
-    cb(gate_sigmoid, "gate_sigmoid", il);
-
-    cur = ggml_mul(ctx0, cur, gate_sigmoid);
-    cb(cur, "attn_gated", il);
-
-    cur = build_lora_mm(model.layers[il].wo, cur);
-    cb(cur, "attn_output", il);
-
-    return cur;
-}
-
-std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3_5::build_qkvz(
-                ggml_tensor * input,
-                        int   il) {
-    const int64_t d_inner      = hparams.ssm_d_inner;
-    const int64_t n_seqs       = ubatch.n_seqs;
-    const int64_t head_k_dim   = hparams.ssm_d_state;
-    const int64_t num_k_heads  = hparams.ssm_n_group;
-    const int64_t num_v_heads  = hparams.ssm_dt_rank;
-    const int64_t head_v_dim   = d_inner / num_v_heads;
-    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
-
-    if (model.layers[il].wqkv) {
-        ggml_tensor * qkv_mixed = build_lora_mm(model.layers[il].wqkv, input);
-        qkv_mixed = ggml_reshape_3d(ctx0, qkv_mixed, qkv_mixed->ne[0], n_seq_tokens, n_seqs);
-        cb(qkv_mixed, "linear_attn_qkv_mixed", il);
-
-        ggml_tensor * z = build_lora_mm(model.layers[il].wqkv_gate, input);
-        cb(z, "z", il);
-
-        return { qkv_mixed, z };
-
-    }
-    // legacy path for combined in_proj_qkvz
-    ggml_tensor * mixed_qkvz = build_lora_mm(model.layers[il].ssm_in, input);
-    cb(mixed_qkvz, "linear_attn_mixed_qkvz", il);
-
-    int64_t       qkvz_new_dim        = 2 * head_k_dim + 2 * head_v_dim * (num_v_heads / num_k_heads);
-    ggml_tensor * mixed_qkvz_reshaped = ggml_reshape_4d(ctx0, mixed_qkvz, qkvz_new_dim, num_k_heads, n_seq_tokens, n_seqs);
-
-    int64_t split_sizes_qkvz[4] = {
-        head_k_dim,
-        head_k_dim,
-        head_v_dim * num_v_heads / num_k_heads,
-        head_v_dim * num_v_heads / num_k_heads
-    };
-
-    ggml_tensor * query =
-        ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[0], num_k_heads, n_seq_tokens, n_seqs,
-                    mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3], 0);
-    cb(query, "q", il);
-
-    ggml_tensor * key = ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[1], num_k_heads, n_seq_tokens, n_seqs,
-                                    mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
-                                    split_sizes_qkvz[0] * ggml_element_size(mixed_qkvz_reshaped));
-    cb(key, "k", il);
-
-    ggml_tensor * value =
-        ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[2], num_k_heads, n_seq_tokens, n_seqs,
-                    mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
-                    (split_sizes_qkvz[0] + split_sizes_qkvz[1]) * ggml_element_size(mixed_qkvz_reshaped));
-    cb(value, "v", il);
-
-    ggml_tensor * z = ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[3], num_k_heads, n_seq_tokens, n_seqs,
-                                mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
-                                (split_sizes_qkvz[0] + split_sizes_qkvz[1] + split_sizes_qkvz[2]) * ggml_element_size(mixed_qkvz_reshaped));
-    z = ggml_cont(ctx0, z);
-    cb(z, "z", il);
-
-    ggml_tensor * query_flat = ggml_reshape_3d(ctx0, query, head_k_dim * num_k_heads, n_seq_tokens, n_seqs);
-    cb(query_flat, "query_flat", il);
-
-    ggml_tensor * key_flat = ggml_reshape_3d(ctx0, key, head_k_dim * num_k_heads, n_seq_tokens, n_seqs);
-    cb(key_flat, "key_flat", il);
-
-    ggml_tensor * value_flat = ggml_reshape_3d(ctx0, value, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
-    cb(value_flat, "value_flat", il);
-
-    ggml_tensor * qkv_mixed = ggml_concat(ctx0, query_flat, key_flat, 0);
-    qkv_mixed               = ggml_concat(ctx0, qkv_mixed, value_flat, 0);
-    cb(qkv_mixed, "qkv_mixed", il);
-
-    return { qkv_mixed, z };
-}
-
-ggml_tensor * llm_build_qwen3_5::build_layer_attn_linear(
-        llm_graph_input_rs * inp,
-        ggml_tensor *        cur,
-        ggml_tensor *        causal_mask,
-        ggml_tensor *        identity,
-        ggml_tensor *        diag_mask,
-        int                  il) {
-    const auto * mctx_cur = inp->mctx;
-
-    const int64_t d_inner      = hparams.ssm_d_inner;
-    const int64_t n_seqs       = ubatch.n_seqs;
-    const int64_t head_k_dim   = hparams.ssm_d_state;
-    const int64_t num_k_heads  = hparams.ssm_n_group;
-    const int64_t num_v_heads  = hparams.ssm_dt_rank;
-    const int64_t head_v_dim   = d_inner / num_v_heads;
-    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
-
-    const auto kv_head = mctx_cur->get_head();
-
-    GGML_ASSERT(n_seqs != 0);
-    GGML_ASSERT(ubatch.equal_seqs());
-    GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
-
-    auto qkvz = build_qkvz(cur, il);
-    ggml_tensor * qkv_mixed = qkvz.first;
-    ggml_tensor * z         = qkvz.second;
-
-    ggml_tensor * mixed_ba = build_lora_mm(model.layers[il].ssm_beta_alpha, cur);
-    cb(mixed_ba, "linear_attn_mixed_ba", il);
-
-    int64_t       ba_new_dim        = 2 * num_v_heads / num_k_heads;
-    ggml_tensor * mixed_ba_reshaped = ggml_reshape_4d(ctx0, mixed_ba, ba_new_dim, num_k_heads, n_seq_tokens, n_seqs);
-
-    int64_t split_sizes_ba[2] = {
-        num_v_heads / num_k_heads,
-        num_v_heads / num_k_heads
-    };
-
-    ggml_tensor * b = ggml_view_4d(ctx0, mixed_ba_reshaped, split_sizes_ba[0], num_k_heads, n_seq_tokens, n_seqs,
-                                   mixed_ba_reshaped->nb[1], mixed_ba_reshaped->nb[2], mixed_ba_reshaped->nb[3], 0);
-    cb(b, "b", il);
-
-    ggml_tensor * a = ggml_view_4d(ctx0, mixed_ba_reshaped, split_sizes_ba[1], num_k_heads, n_seq_tokens, n_seqs,
-                                   mixed_ba_reshaped->nb[1], mixed_ba_reshaped->nb[2], mixed_ba_reshaped->nb[3],
-                                   split_sizes_ba[0] * ggml_element_size(mixed_ba_reshaped));
-    cb(a, "a", il);
-
-    ggml_tensor * beta  = ggml_cont_4d(ctx0, b, num_v_heads, 1, n_seq_tokens, n_seqs);
-
-    ggml_tensor * alpha = ggml_cont_3d(ctx0, a, num_v_heads, n_seq_tokens, n_seqs);
-
-    ggml_tensor * alpha_biased   = ggml_add(ctx0, alpha, model.layers[il].ssm_dt);
-    ggml_tensor * alpha_softplus = ggml_softplus(ctx0, alpha_biased);
-    cb(alpha_softplus, "a_softplus", il);
-    ggml_tensor * gate = ggml_mul(ctx0, alpha_softplus, model.layers[il].ssm_a);
-    cb(gate, "gate", il);
-
-    ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
-    ggml_tensor * ssm_states_all  = mctx_cur->get_s_l(il);
-
-    ggml_tensor * conv_states = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
-    cb(conv_states, "conv_states", il);
-
-    ggml_tensor * conv_kernel      = model.layers[il].ssm_conv1d;
-    const int64_t conv_kernel_size = conv_kernel->ne[0];
-    const int64_t conv_channels    = d_inner + 2 * hparams.ssm_n_group * hparams.ssm_d_state;
-    conv_states                    = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs);
-    cb(conv_states, "conv_states_reshaped", il);
-
-    qkv_mixed = ggml_permute(ctx0, qkv_mixed, 1, 0, 2, 3);
-    cb(qkv_mixed, "qkv_mixed_permuted", il);
-
-    ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0);
-    cb(conv_input, "conv_input", il);
-
-    ggml_tensor * last_conv_states =
-        ggml_view_3d(ctx0, conv_input, conv_kernel_size - 1, conv_channels, n_seqs, conv_input->nb[1],
-                     conv_input->nb[2], (conv_input->ne[0] - conv_states->ne[0]) * ggml_element_size(conv_input));
-    cb(last_conv_states, "last_conv_states", il);
-
-    ggml_tensor * state_update_target =
-        ggml_view_1d(ctx0, conv_states_all, (conv_kernel_size - 1) * conv_channels * n_seqs,
-                     kv_head * (conv_kernel_size - 1) * conv_channels * ggml_element_size(conv_states_all));
-    cb(state_update_target, "state_update_target", il);
-
-    ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target));
-    cb(conv_states_all, "conv_states_updated", il);
-
-    ggml_tensor * conv_output_proper = ggml_ssm_conv(ctx0, conv_input, conv_kernel);
-    cb(conv_output_proper, "conv_output_raw", il);
-
-    ggml_tensor * conv_output_silu = ggml_silu(ctx0, conv_output_proper);
-    cb(conv_output_silu, "conv_output_silu", il);
-
-    ggml_tensor * conv_qkv_mix = conv_output_silu;
-
-    int64_t qkv_dim = head_k_dim * num_k_heads * 2 + head_v_dim * num_v_heads;
-    int64_t nb1_qkv = ggml_row_size(conv_qkv_mix->type, qkv_dim);
-
-    ggml_tensor * q_conv =
-        ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv, 0);
-    cb(q_conv, "q_conv", il);
-    ggml_tensor * k_conv =
-        ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv,
-                     head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
-    cb(k_conv, "k_conv", il);
-    ggml_tensor * v_conv =
-        ggml_view_2d(ctx0, conv_qkv_mix, head_v_dim * num_v_heads, n_seq_tokens * n_seqs, nb1_qkv,
-                     2 * head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
-    cb(v_conv, "v_conv", il);
-
-    q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
-    k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
-    v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
-
-    ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
-    state               = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim,  num_v_heads, n_seqs);
-    cb(state, "state_predelta", il);
-
-    if (num_k_heads != num_v_heads) {
-        GGML_ASSERT(num_v_heads % num_k_heads == 0);
-        int64_t repeat_factor = num_v_heads / num_k_heads;
-
-        ggml_tensor * q_reshaped = ggml_reshape_3d(ctx0, q_conv, head_k_dim, 1, num_k_heads * n_seq_tokens * n_seqs);
-        ggml_tensor * k_reshaped = ggml_reshape_3d(ctx0, k_conv, head_k_dim, 1, num_k_heads * n_seq_tokens * n_seqs);
-
-        ggml_tensor * q_repeated =
-            ggml_repeat_4d(ctx0, q_reshaped, head_k_dim, repeat_factor, num_k_heads * n_seq_tokens * n_seqs, 1);
-        ggml_tensor * k_repeated =
-            ggml_repeat_4d(ctx0, k_reshaped, head_k_dim, repeat_factor, num_k_heads * n_seq_tokens * n_seqs, 1);
-
-        q_conv = ggml_reshape_4d(ctx0, q_repeated, head_k_dim, num_k_heads * repeat_factor, n_seq_tokens, n_seqs);
-        k_conv = ggml_reshape_4d(ctx0, k_repeated, head_k_dim, num_k_heads * repeat_factor, n_seq_tokens, n_seqs);
-    }
-
-    cb(q_conv, "q_conv_predelta", il);
-    cb(k_conv, "k_conv_predelta", il);
-    cb(v_conv, "v_conv_predelta", il);
-
-    std::pair<ggml_tensor *, ggml_tensor *> attn_out = build_delta_net_unified(ctx0, q_conv, k_conv, v_conv,
-            gate, beta, state, causal_mask, identity, diag_mask,
-            il, CHUNK_SIZE, hparams.f_norm_rms_eps);
-
-    ggml_tensor * output    = attn_out.first;
-    ggml_tensor * new_state = attn_out.second;
-    cb(output, "attn_output", il);
-    cb(new_state, "new_state", il);
-
-    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))));
-
-    ggml_tensor * attn_out_2d_final = ggml_reshape_2d(ctx0, output, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
-
-    ggml_tensor * z_2d = ggml_reshape_2d(ctx0, z, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
-
-    ggml_tensor * attn_out_norm = build_norm_gated(attn_out_2d_final, model.layers[il].ssm_norm, z_2d, il);
-
-    ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
-    cb(final_output, "final_output", il);
-
-    cur = build_lora_mm(model.layers[il].ssm_out, final_output);
-    cb(cur, "linear_attn_out", il);
-
-    cur = ggml_cont_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);
-    return cur;
-}
-
-ggml_tensor * llm_build_qwen3_5::build_layer_ffn(ggml_tensor * cur, const int il) {
-    // Qwen3.5 Dense always uses dense FFN
-    cur = build_ffn(cur,
-        model.layers[il].ffn_up, NULL, NULL,
-        model.layers[il].ffn_gate, NULL, NULL,
-        model.layers[il].ffn_down, NULL, NULL,
-        NULL,
-        LLM_FFN_SILU, LLM_FFN_PAR, il);
-    cb(cur, "ffn_out", il);
-    return cur;
-}
--- a/src/models/qwen3-5moe.cpp
+++ b/src/models/qwen3-5moe.cpp
@@ -1,52 +0,0 @@
-#include "models.h"
-
-llm_build_qwen3_5_moe::llm_build_qwen3_5_moe(const llama_model & model, const llm_graph_params & params) :
-    llm_build_qwen3_5(model, params, defer_graph_build_t{}) {
-    build_graph();
-}
-
-ggml_tensor * llm_build_qwen3_5_moe::build_layer_ffn(ggml_tensor * cur, const int il) {
-    // Check if this is an MoE layer
-    if (model.layers[il].ffn_gate_inp != nullptr) {
-        // MoE branch
-        ggml_tensor * moe_out =
-            build_moe_ffn(cur,
-                model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps,
-                model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps,
-                nullptr,
-                n_expert, n_expert_used, LLM_FFN_SILU,
-                true, false, 0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il);
-        cb(moe_out, "ffn_moe_out", il);
-
-        // Add shared experts if present
-        if (model.layers[il].ffn_up_shexp != nullptr) {
-            ggml_tensor * ffn_shexp =
-                build_ffn(cur,
-                    model.layers[il].ffn_up_shexp, NULL, NULL,
-                    model.layers[il].ffn_gate_shexp, NULL, NULL,
-                    model.layers[il].ffn_down_shexp, NULL, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(ffn_shexp, "ffn_shexp", il);
-
-            // Apply shared expert gating (sigmoid)
-            ggml_tensor * shared_gate = build_lora_mm(model.layers[il].ffn_gate_inp_shexp, cur);
-            cb(shared_gate, "shared_expert_gate", il);
-
-            shared_gate = ggml_sigmoid(ctx0, shared_gate);
-            cb(shared_gate, "shared_expert_gate_sigmoid", il);
-
-            ffn_shexp = ggml_mul(ctx0, ffn_shexp, shared_gate);
-            cb(ffn_shexp, "ffn_shexp_gated", il);
-
-            cur = ggml_add(ctx0, moe_out, ffn_shexp);
-            cb(cur, "ffn_out", il);
-        } else {
-            cur = moe_out;
-        }
-    } else {
-        // Dense FFN branch (fallback)
-        cur = llm_build_qwen3_5::build_layer_ffn(cur, il);
-    }
-    return cur;
-}
--- a/src/models/qwen3next.cpp
+++ b/src/models/qwen3next.cpp
@@ -1,9 +1,10 @@
+#include "ggml.h"
 #include "models.h"

 #define CHUNK_SIZE 64

 llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_graph_params & params) :
-    llm_graph_context_delta(params), model(model) {
+    llm_graph_context_mamba(params), model(model) {
    ggml_tensor * cur;
    ggml_tensor * inpL;

@@ -85,6 +86,362 @@ llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_gr
    ggml_build_forward_expand(gf, cur);
 }

+// utility to get one slice from the third dimension
+// input dim:  [x, y, c, b]
+// output dim: [x, y, 1, b]
+static ggml_tensor * get_slice_2d(ggml_context * ctx0, ggml_tensor * t, int64_t c) {
+    return ggml_view_4d(ctx0, t, t->ne[0], t->ne[1], 1, t->ne[3],
+        t->nb[1], t->nb[2], t->nb[3], t->nb[2] * c);
+}
+
+std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3next::build_delta_net_chunking(
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * g,
+        ggml_tensor * beta,
+        ggml_tensor * state,
+        ggml_tensor * causal_mask,
+        ggml_tensor * identity,
+        ggml_tensor * diag_mask,
+        int           il) {
+    const int64_t S_k      = q->ne[0];
+    const int64_t H_k      = q->ne[1];
+    const int64_t n_tokens = q->ne[2];
+    const int64_t n_seqs   = q->ne[3];
+
+    const int64_t S_v = v->ne[0];
+    const int64_t H_v = v->ne[1];
+
+    GGML_ASSERT(v->ne[2] == n_tokens);
+    GGML_ASSERT(k->ne[2] == n_tokens);
+    GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
+    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
+    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
+
+    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+
+    GGML_ASSERT(H_k == H_v);  // we did a repeat to make sure this is the case
+
+    const float eps_norm = hparams.f_norm_rms_eps;
+
+    q = ggml_l2_norm(ctx0, q, eps_norm);
+    k = ggml_l2_norm(ctx0, k, eps_norm);
+
+    const float scale = 1.0f / sqrtf(S_v);
+
+    q = ggml_scale(ctx0, q, scale);
+
+    beta = ggml_sigmoid(ctx0, beta);
+
+    cb(q, "q_in", il);
+    cb(k, "k_in", il);
+    cb(v, "v_in", il);
+    cb(beta, "beta_in", il);
+    cb(g, "g_in", il);
+
+    q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+    k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+    v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+    g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs);
+
+    beta  = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
+    state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
+
+    cb(q, "q_perm", il);
+    cb(k, "k_perm", il);
+    cb(v, "v_perm", il);
+    cb(beta, "beta_perm", il);
+    cb(g, "g_perm", il);
+    cb(state, "state_in", il);
+
+    GGML_ASSERT(q->ne[1] == n_tokens && q->ne[0] == S_k && q->ne[2] == H_k && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[1] == n_tokens && k->ne[0] == S_k && k->ne[2] == H_k && k->ne[3] == n_seqs);
+    GGML_ASSERT(v->ne[1] == n_tokens && v->ne[0] == S_v && v->ne[2] == H_k && v->ne[3] == n_seqs);
+    GGML_ASSERT(beta->ne[1] == n_tokens && beta->ne[2] == H_k && beta->ne[0] == 1 && beta->ne[3] == n_seqs);
+
+    // Do padding
+    const int64_t chunk_size = CHUNK_SIZE;
+
+    const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size;
+    const int64_t n_chunks = (n_tokens + pad) / chunk_size;
+
+    q = ggml_pad(ctx0, q, 0, pad, 0, 0);
+    k = ggml_pad(ctx0, k, 0, pad, 0, 0);
+    v = ggml_pad(ctx0, v, 0, pad, 0, 0);
+    g = ggml_pad(ctx0, g, pad, 0, 0, 0);
+    beta = ggml_pad(ctx0, beta, 0, pad, 0, 0);
+
+    cb(q, "q_pad", il);
+    cb(k, "k_pad", il);
+    cb(v, "v_pad", il);
+    cb(beta, "beta_pad", il);
+    cb(g, "g_pad", il);
+
+    ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
+    ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
+
+    cb(v_beta, "v_beta", il);
+    cb(k_beta, "k_beta", il);
+
+    q      = ggml_reshape_4d(ctx0, q,      S_k, chunk_size, n_chunks, H_k * n_seqs);
+    k      = ggml_reshape_4d(ctx0, k,      S_k, chunk_size, n_chunks, H_k * n_seqs);
+    k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
+    v      = ggml_reshape_4d(ctx0, v,      S_v, chunk_size, n_chunks, H_v * n_seqs);
+    v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
+
+    g    = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
+    beta = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
+
+    ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);
+    cb(g_cumsum, "g_cumsum", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * gcs_i = g_cumsum; // ggml_reshape_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs);
+    ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs);
+
+    ggml_tensor * gcs_j_broadcast =
+        ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
+
+    ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
+    cb(decay_mask, "decay_mask", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+    decay_mask = ggml_exp(ctx0, decay_mask);
+    decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+
+    ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
+
+    ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
+    ggml_tensor * attn    = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
+    cb(attn, "attn_pre_solve", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
+    ggml_tensor * lhs        = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
+
+    ggml_tensor * lin_solve  = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
+    attn                     = ggml_mul(ctx0, lin_solve, causal_mask);
+    attn                     = ggml_add(ctx0, attn, identity);
+    cb(attn, "attn_solved", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
+
+    ggml_tensor * g_cumsum_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum));
+    ggml_tensor * gexp       = ggml_exp(ctx0, g_cumsum_t);
+
+    ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp);
+    cb(kbeta_gexp, "kbeta_gexp", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * k_cumdecay =
+        ggml_cont(ctx0, ggml_transpose(ctx0, ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
+    cb(k_cumdecay, "k_cumdecay", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * attn_kq = ggml_mul_mat(ctx0, k, q);
+    attn_kq = ggml_mul(ctx0, attn_kq, decay_mask);
+    attn_kq = ggml_mul(ctx0, attn_kq, diag_mask);
+    cb(attn_kq, "attn_kq", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+
+    // vectorized calculation of key_gdiff
+    // improved from the chunked version:
+    //   g_last = torch.clamp(g_cum[:, :, -1], max=50.0).exp().unsqueeze(-1).unsqueeze(-1)
+    //   g_diff = torch.clamp(g_cum[:, :, -1:] - g_cum, max=50.0).exp()
+    //   key_gdiff = key * g_diff.unsqueeze(-1)
+    //   kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
+    //   last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
+
+    // get last element in g_cumsum along chunk_size dimension (ne0)
+    // example: [[x, y, z, ..., last], ...] -> [[last], ...]
+    ggml_tensor * g_last = ggml_view_4d(ctx0, g_cumsum, 1, 1, g_cumsum->ne[2], g_cumsum->ne[3],
+                                        g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
+                                        (g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum));
+    g_last = ggml_cont(ctx0, g_last);
+    cb(g_last, "g_last", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * g_last_exp = ggml_exp(ctx0, g_last);
+    cb(g_last_exp, "g_last_exp", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last));
+    cb(g_diff, "g_diff", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
+    ggml_tensor * g_diff_exp_t = ggml_reshape_4d(ctx0, g_diff_exp,
+                                                 1, chunk_size, n_chunks, g_diff_exp->ne[3]);
+
+    ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp_t);
+    cb(key_gdiff, "key_gdiff", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * key_gdiff_t = ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff));
+    cb(key_gdiff_t, "key_gdiff_t", il); // shape: (chunk_size, S_k, n_chunks, H_v * n_seqs)
+
+
+    // state to be updated per chunk
+    ggml_tensor * new_state = state; // ggml_dup(ctx0, state);
+    cb(new_state, "new_state", il); // shape: (S_v, S_v, H_v, n_seqs)
+
+    // shape after loop of chunks: (S_v, chunk_size, n_chunks, H_v * n_seqs)
+    ggml_tensor * core_attn_out = nullptr;
+
+    for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
+        // shape: (S_k, chunk_size, 1, H_k * n_seqs)
+        ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk); // (no cont), next op: ggml_mul
+
+        // shape: (S_v, chunk_size, 1, H_v * n_seqs)
+        ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk); // (no cont), next op: ggml_repeat
+
+        // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+        ggml_tensor * gexp_chunk = get_slice_2d(ctx0, gexp, chunk); // (no cont), next op: ggml_mul
+
+        // shape: (chunk_size, 1, H_v * n_seqs)
+        ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk); // (no cont), next op: ggml_mul_mat
+
+        // attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
+        // replaced by precomputed attn_kq
+        ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk);
+        cb(attn_chunk, "attn_chunk", il);
+
+        ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3), S_v, S_v, 1, H_v * n_seqs);
+
+        // v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
+        ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
+        cb(v_prime, "v_prime_chunk", il); // shape: (S_v, 1, H_v * n_seqs)
+
+        // v_new = v_i - v_prime
+        ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime);
+        ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
+        cb(v_new, "v_new_chunk", il);
+
+        // attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
+        ggml_tensor * q_g_exp    = ggml_mul(ctx0, q_chunk, gexp_chunk);
+        ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
+        cb(attn_inter, "attn_inter_chunk", il);
+
+        // core_attn_out[:, :, i] = attn_inter + attn @ v_new
+        ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk);
+        cb(v_attn, "v_attn_chunk", il);
+
+        ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
+        cb(core_attn_out_chunk, "core_attn_out_chunk", il); // shape: (S_v, chunk_size, 1, H_v * n_seqs)
+
+        core_attn_out = core_attn_out == nullptr
+            ? core_attn_out_chunk
+            : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2);
+
+        // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
+        ggml_tensor * k_gdiff_t = get_slice_2d(ctx0, key_gdiff_t, chunk);
+        //ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, k_gdiff, v_new); // this is slower on metal, why?
+        ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, k_gdiff_t);
+
+        // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
+        ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk));
+        new_state = ggml_add(ctx0,
+            ggml_mul(ctx0, new_state, ggml_reshape_4d(ctx0, gexp_last_chunk, gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs)),
+            ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
+    }
+
+    // truncate padded tokens
+    ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out,
+            S_v, n_tokens, H_v, n_seqs,
+            ggml_row_size(core_attn_out->type, S_v),
+            ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks),
+            ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0);
+    output_tokens = ggml_cont(ctx0, output_tokens);
+    cb(output_tokens, "output_tokens", il);
+
+    // permute back to (S_v, H_v, n_tokens, n_seqs)
+    output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3);
+    output_tokens = ggml_cont(ctx0, output_tokens);
+
+    return {output_tokens, new_state};
+}
+
+std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3next::build_delta_net_autoregressive(
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * g,
+        ggml_tensor * beta,
+        ggml_tensor * state,
+        int           il) {
+    const int64_t S_k      = q->ne[0];
+    const int64_t H_k      = q->ne[1];
+    const int64_t n_tokens = q->ne[2];
+    const int64_t n_seqs   = q->ne[3];
+
+    const int64_t S_v = v->ne[0];
+    const int64_t H_v = v->ne[1];
+
+    GGML_ASSERT(n_tokens == 1);  // This function is optimized for single token processing
+    GGML_ASSERT(v->ne[2] == n_tokens);
+    GGML_ASSERT(k->ne[2] == n_tokens);
+    GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
+    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
+    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
+
+    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+
+    GGML_ASSERT(H_k == H_v);  // we did a repeat to make sure this is the case
+
+    const float eps_norm = hparams.f_norm_rms_eps;
+
+    q = ggml_l2_norm(ctx0, q, eps_norm);
+    k = ggml_l2_norm(ctx0, k, eps_norm);
+
+    const float scale = 1.0f / sqrtf(S_v);
+
+    q    = ggml_scale(ctx0, q, scale);
+    beta = ggml_sigmoid(ctx0, beta);
+
+    cb(q, "q_in", il);
+    cb(k, "k_in", il);
+    cb(v, "v_in", il);
+    cb(beta, "beta_in", il);
+    cb(g, "g_in", il);
+
+    state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
+
+    ggml_tensor * g_t    = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs);
+    ggml_tensor * beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
+
+    // Apply exponential to g_t
+    g_t = ggml_exp(ctx0, g_t);
+
+    // Apply the gated delta rule for the single timestep
+    // last_recurrent_state = last_recurrent_state * g_t
+    state = ggml_mul(ctx0, state, g_t);
+
+    // kv_mem = (last_recurrent_state * k_t.unsqueeze(-1)).sum(dim=-2)
+    ggml_tensor * k_t_unsqueezed = ggml_reshape_4d(ctx0, k, 1, S_v, H_v, n_seqs);
+    ggml_tensor * kv_mem         = ggml_mul(ctx0, state, k_t_unsqueezed);
+    // we need to sum over dim=-2, so we transpose, sum, then transpose again
+    kv_mem = ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, kv_mem))));
+
+    // v_t = v.unsqueeze(2) (we insert the singleton dimension after n_seqs and H_v)
+    ggml_tensor * v_t    = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
+    // delta = (v_t - kv_mem) * beta_t
+    ggml_tensor * v_diff = ggml_sub(ctx0, v_t, kv_mem);  // both should be [S_v, 1, H_v, n_seqs]
+    ggml_tensor * delta  = ggml_mul(ctx0, v_diff, beta_t);
+
+    // last_recurrent_state = last_recurrent_state + k_t.unsqueeze(-1) * delta
+    ggml_tensor * k_t_delta = ggml_mul(ctx0, ggml_repeat_4d(ctx0, k_t_unsqueezed, S_v, S_v, H_v, n_seqs), delta);
+    state                   = ggml_add(ctx0, state, k_t_delta);
+
+    // Compute the attention output
+    // core_attn_out = (last_recurrent_state * q_t.unsqueeze(-1)).sum(dim=-2)
+    ggml_tensor * q_t_unsqueezed = ggml_reshape_4d(ctx0, q, 1, S_v, H_v, n_seqs);  // unsqueeze q_t
+    ggml_tensor * state_q        = ggml_mul(ctx0, state, q_t_unsqueezed);
+    // again, since it's over dim = -2, transpose, sum, transpose back
+    ggml_tensor * core_attn_out =
+        ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, state_q))));
+
+    // core_attn_out should be [S_v, 1, H_v, n_seqs] after this
+    cb(core_attn_out, "output_tokens", il);
+    cb(state, "new_state", il);
+
+    return {core_attn_out, state};
+}
+
 ggml_tensor * llm_build_qwen3next::build_norm_gated(
        ggml_tensor * input,
        ggml_tensor * weights,
@@ -395,7 +752,7 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
    v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);

    ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
-    state               = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim,  num_v_heads, n_seqs);
+    state               = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim * num_v_heads, 1, n_seqs);
    cb(state, "state_predelta", il);

    // if head keys and value keys are different, repeat to force tensors into matching shapes
@@ -424,10 +781,13 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
    cb(k_conv, "k_conv_predelta", il);
    cb(v_conv, "v_conv_predelta", il);

-    std::pair<ggml_tensor *, ggml_tensor *> attn_out = build_delta_net_unified(ctx0, q_conv, k_conv, v_conv,
-            gate, beta, state, causal_mask, identity, diag_mask,
-            il, CHUNK_SIZE, hparams.f_norm_rms_eps);
-
+    // Choose between build_delta_net_chunking, build_delta_net_recurrent, and build_delta_net_autoregressive based on n_tokens
+    std::pair<ggml_tensor *, ggml_tensor *> attn_out; // pair of (output, new_state)
+    if (n_seq_tokens == 1) {
+        attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il);
+    } else {
+        attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, diag_mask, il);
+    }
    ggml_tensor * output    = attn_out.first;
    ggml_tensor * new_state = attn_out.second;
    cb(output, "attn_output", il);
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -11,7 +11,9 @@ function(llama_build source)

    add_executable(${TEST_TARGET} ${TEST_SOURCES})
    target_link_libraries(${TEST_TARGET} PRIVATE common)
-    install(TARGETS ${TEST_TARGET} RUNTIME)
+    if (LLAMA_TESTS_INSTALL)
+        install(TARGETS ${TEST_TARGET} RUNTIME)
+    endif()
 endfunction()

 function(llama_test target)
@@ -100,7 +102,9 @@ function(llama_build_and_test source)
    endif()

    add_executable(${TEST_TARGET} ${TEST_SOURCES})
-    install(TARGETS ${TEST_TARGET} RUNTIME)
+    if (LLAMA_TESTS_INSTALL)
+        install(TARGETS ${TEST_TARGET} RUNTIME)
+    endif()
    target_link_libraries(${TEST_TARGET} PRIVATE common)

    add_test(