ModelCloud · Qubitium · Jul 23, 2026 · Jul 21, 2026 · Jul 21, 2026 · Jul 21, 2026
diff --git a/README.md b/README.md
@@ -21,6 +21,7 @@
 
 ## Latest News
 
+* 07/23/2026 7.3.1 `main`: ✨ Added `inkling_mm_model` model support
 * 07/22/2026 7.3.1 `main`: ✨ Added Poolside `Laguna S 2.1` model support
 * 07/14/2026 7.3.0-dev `main`: ✨ Added `nemotron_h_puzzle` model support
 * 07/07/2026 7.3.0-dev `main`: ✨ Added `deepseek_vl` model support
@@ -258,24 +259,24 @@ Selected public references where teams or companies explicitly mention GPT-QMode
 
 ## Model Support  
 
-| Model                         |   |                                 |  |                  |  |                                 |  |                        |   |
-|-------------------------------|---|---------------------------------|--|------------------|--|---------------------------------|--|------------------------|---|
-| Apertus                       | ✅ | EXAONE 3/4                      | ✅ | Dots1            | ✅ | Mistral3 / Ministral3           | ✅ | Qwen 2/3/3.5 (Next/MoE) | ✅ |
-| Baichuan                      | ✅ | Falcon (H1 / Mamba)             | ✅ | InternLM 1/2/2.5 | ✅ | Mixtral                         | ✅ | Qwen 2/2.5/3 VL        | ✅ |
-| Bloom                         | ✅ | FastVLM                         | ✅ | Kimi K2          | ✅ | MobileLLM                       | ✅ | Qwen 2.5/3 Omni        | ✅ |
-| ChatGLM                       | ✅ | Gemma 1-4 / 3n                  | ✅ | Klear            | ✅ | MOSS                            | ✅ | RefinedWeb             | ✅ |
-| CodeGen                       | ✅ | GPTBigCode                      | ✅ | LING/RING        | ✅ | MPT                             | ✅ | StableLM               | ✅ |
-| Cohere 1-2 / 2 MoE            | ✅ | GPT-Neo / NeoX                  | ✅ | Llama 1-3.3      | ✅ | Nemotron H / H Puzzle / Omni    | ✅ | StarCoder2             | ✅ |
-| DBRX Converted                | ✅ | GPT-2                           | ✅ | Llama 3.2 VL     | ✅ | Nemotron Ultra / Labs-Diffusion | ✅ | TeleChat2              | ✅ |
-| Deci                          | ✅ | GPT-J                           | ✅ | Llama 4          | ✅ | OPT                             | ✅ | Trinity                | ✅ |
-| DeepSeek-V2/V3/V4/R1          | ✅ | GPT-OSS                         | ✅ | LongCat Flash    | ✅ | OLMo2 / LLaDA2                  | ✅ | Yi                     | ✅ |
-| DeepSeek-V2 Lite / VL / VL2 / OCR2 | ✅ | Granite / Granite MoE           | ✅ | LongLLaMA        | ✅ | Ovis 1.6/2/2.5/2.6 MoE/2.6 Next | ✅ | Seed-OSS               | ✅ |
-| Dream                         | ✅ | GRIN-MoE                        | ✅ | Instella         | ✅ | Phi 1-4                         | ✅ | Voxtral                | ✅ |
+| Model                         |   |                                 |  |                            |  |                                 |  |                        |   |
+|-------------------------------|---|---------------------------------|--|----------------------------|--|---------------------------------|--|------------------------|---|
+| Apertus                       | ✅ | EXAONE 3/4                      | ✅ | Dots1                      | ✅ | Mistral3 / Ministral3           | ✅ | Qwen 2/3/3.5 (Next/MoE) | ✅ |
+| Baichuan                      | ✅ | Falcon (H1 / Mamba)             | ✅ | InternLM 1/2/2.5           | ✅ | Mixtral                         | ✅ | Qwen 2/2.5/3 VL        | ✅ |
+| Bloom                         | ✅ | FastVLM                         | ✅ | Kimi K2                    | ✅ | MobileLLM                       | ✅ | Qwen 2.5/3 Omni        | ✅ |
+| ChatGLM                       | ✅ | Gemma 1-4 / 3n                  | ✅ | Klear                      | ✅ | MOSS                            | ✅ | RefinedWeb             | ✅ |
+| CodeGen                       | ✅ | GPTBigCode                      | ✅ | LING/RING                  | ✅ | MPT                             | ✅ | StableLM               | ✅ |
+| Cohere 1-2 / 2 MoE            | ✅ | GPT-Neo / NeoX                  | ✅ | Llama 1-3.3                | ✅ | Nemotron H / H Puzzle / Omni    | ✅ | StarCoder2             | ✅ |
+| DBRX Converted                | ✅ | GPT-2                           | ✅ | Llama 3.2 VL               | ✅ | Nemotron Ultra / Labs-Diffusion | ✅ | TeleChat2              | ✅ |
+| Deci                          | ✅ | GPT-J                           | ✅ | Llama 4                    | ✅ | OPT                             | ✅ | Trinity                | ✅ |
+| DeepSeek-V2/V3/V4/R1          | ✅ | GPT-OSS                         | ✅ | LongCat Flash              | ✅ | OLMo2 / LLaDA2                  | ✅ | Yi                     | ✅ |
+| DeepSeek-V2 Lite / VL / VL2 / OCR2 | ✅ | Granite / Granite MoE           | ✅ | LongLLaMA                  | ✅ | Ovis 1.6/2/2.5/2.6 MoE/2.6 Next | ✅ | Seed-OSS               | ✅ |
+| Dream                         | ✅ | GRIN-MoE                        | ✅ | Instella                   | ✅ | Phi 1-4                         | ✅ | Voxtral                | ✅ |
 | ERNIE 4.5 / MoE / VL MoE      | ✅ | GLM 4/4V/4.5V/4.6V/5/5.1/OCR/ASR | ✅ | GLM4 MoE / Lite / 4.5V MoE | ✅ | MiniCPM 3/O/V/V 4_6             | ✅ | PanGu-α                | ✅ |
-| XVERSE                        | ✅ | Brumby                          | ✅ | Hymba            | ✅ | Mistral                         | ✅ | Qwen 1/2/3/3.5         | ✅ |
-| MiniMax M2/M3                 | ✅ | AfMoE                           | ✅ | Bailing-MoE      | ✅ | LFM2 / LFM2-VL / LFM2-MoE       | ✅ | Marin                  | ✅ |
-| InternVL Chat                 | ✅ | Laguna                          | ✅ | Mimo / Mimo V2   | ✅ | Zamba / Zamba2                  | ✅ | Intern S1              | ✅ |
-| HunYuan V1 Dense / MoE        | ✅ | HY-V3                           | ✅ |    |  |                                 |  |                |   |
+| XVERSE                        | ✅ | Brumby                          | ✅ | Hymba                      | ✅ | Mistral                         | ✅ | Qwen 1/2/3/3.5         | ✅ |
+| MiniMax M2/M3                 | ✅ | AfMoE                           | ✅ | Bailing-MoE                | ✅ | LFM2 / LFM2-VL / LFM2-MoE       | ✅ | Marin                  | ✅ |
+| InternVL Chat                 | ✅ | Laguna                          | ✅ | Mimo / Mimo V2             | ✅ | Zamba / Zamba2                  | ✅ | Intern S1              | ✅ |
+| HunYuan V1 Dense / MoE        | ✅ | HY-V3                           | ✅ | Inkling           | ✅ |                                 |  |                |   |
 
 Prism Bonsai GGUF checkpoints are supported for inference only through GPT-QModel's native GGUF path and internal GGUF runtime. Bonsai checkpoints load through the normal model path or repo argument and do not require the external `gguf` package. Prism model quantization is not included.
 

diff --git a/gptqmodel/models/auto.py b/gptqmodel/models/auto.py
@@ -125,6 +125,7 @@
 from .definitions.hy_v3 import HYV3QModel  # noqa: E402
 from .definitions.hymba import HymbaQModel  # noqa: E402
 from .definitions.instella import InstellaQModel  # noqa: E402
+from .definitions.inkling import InklingMMQModel  # noqa: E402
 from .definitions.internlm import InternLMQModel  # noqa: E402
 from .definitions.internlm2 import InternLM2QModel  # noqa: E402
 from .definitions.interns1 import InternS1QModel  # noqa: E402
@@ -324,6 +325,7 @@
     "ovis2_6_next": Ovis2_6_NextQModel,
     "telechat": TeleChat2QModel,
     "instella": InstellaQModel,
+    "inkling_mm_model": InklingMMQModel,
     "mimo": MimoQModel,
     "mimo_v2": MimoV2QModel,
     "falcon_h1": FalconH1QModel,

diff --git a/gptqmodel/models/definitions/__init__.py b/gptqmodel/models/definitions/__init__.py
@@ -51,6 +51,7 @@
 from .hy_v3 import HYV3QModel
 from .hymba import HymbaQModel
 from .instella import InstellaQModel
+from .inkling import InklingMMQModel
 from .internlm import InternLMQModel
 from .internlm2 import InternLM2QModel
 from .interns1 import InternS1QModel

diff --git a/gptqmodel/models/definitions/inkling.py b/gptqmodel/models/definitions/inkling.py
@@ -0,0 +1,194 @@
+# SPDX-FileCopyrightText: 2026 ModelCloud.ai
+# SPDX-FileCopyrightText: 2026 qubitium@modelcloud.ai
+# SPDX-License-Identifier: Apache-2.0
+# Contact: qubitium@modelcloud.ai, x.com/qubitium
+
+from typing import Any, Dict
+
+import torch
+from transformers import AutoModelForMultimodalLM, AutoProcessor, ProcessorMixin
+from transformers.masking_utils import create_causal_mask, create_sliding_window_causal_mask
+
+from ...utils.calibration import batched
+from ...utils.model import MODALITY, move_to
+from ...utils.offload import offload_to_disk
+from .._const import CPU
+from ..base import BaseQModel
+from ..moe_lifecycle import GateUpDownMoELifecycleHooks
+
+
+class InklingMMQModel(BaseQModel):
+    loader = AutoModelForMultimodalLM
+
+    require_load_processor = True
+    require_trust_remote_code = False
+    layer_modules_strict = False
+
+    modality = [MODALITY.TEXT, MODALITY.IMAGE_TO_TEXT]
+
+    dynamic_expert_index = "n_routed_experts"
+    defuser_auto_detect_moe = True
+    moe_lifecycle_hooks = GateUpDownMoELifecycleHooks()
+
+    pre_lm_head_norm_module = "model.language_model.norm"
+
+    # GQA output shapes differ across the attention projections. AWQ should
+    # optimize the output projection against its actual attention input.
+    awq_scale_optimize_shape_dependent_modules = ["self_attn.o_proj"]
+
+    module_tree = [
+        "model",
+        "language_model",
+        "layers",
+        "#",
+        {
+            "input_layernorm": ("input_layernorm:!",),
+            "self_attn": (
+                "q_proj:0",
+                "k_proj:0",
+                "v_proj:0",
+                "r_proj:0",
+                "q_norm:!",
+                "k_norm:!",
+                "o_proj:1",
+            ),
+            "post_attention_layernorm": ("post_attention_layernorm:!",),
+            "mlp:moe": {
+                # Inkling's first decoder blocks use a dense MLP.
+                "": ("gate_proj:0", "up_proj:0", "down_proj:1"),
+                "gate": ("gate:!",),
+                "experts": {
+                    "#": ("gate_proj:0", "up_proj:0", "down_proj:1"),
+                },
+                # Shared experts are native 3D parameters rather than Linear
+                # modules. Keep these two accuracy-sensitive experts dense;
+                # routed experts are expanded and quantized individually.
+            },
+        },
+    ]
+
+    def pre_quantize_generate_hook_start(self):
+        core_model = self.model.model
+        language_model = core_model.language_model
+        self.shell_module_materialize(language_model.embed_tokens, self.quantize_config.device)
+        self.shell_module_materialize(language_model.embed_norm, self.quantize_config.device)
+        self.shell_module_materialize(core_model.vision_tower, self.quantize_config.device)
+        self.shell_module_materialize(core_model.audio_tower, self.quantize_config.device)
+
+    def pre_quantize_generate_hook_end(self):
+        core_model = self.model.model
+        language_model = core_model.language_model
+        if self.quantize_config.offload_to_disk:
+            offload_to_disk(
+                model=language_model,
+                module=language_model.embed_tokens,
+                disk_path=self.quantize_config.offload_to_disk_path,
+            )
+            offload_to_disk(
+                model=language_model,
+                module=language_model.embed_norm,
+                disk_path=self.quantize_config.offload_to_disk_path,
+            )
+            offload_to_disk(
+                model=core_model,
+                module=core_model.vision_tower,
+                disk_path=self.quantize_config.offload_to_disk_path,
+            )
+            offload_to_disk(
+                model=core_model,
+                module=core_model.audio_tower,
+                disk_path=self.quantize_config.offload_to_disk_path,
+            )
+            return
+
+        language_model.embed_tokens = move_to(language_model.embed_tokens, device=CPU)
+        language_model.embed_norm = move_to(language_model.embed_norm, device=CPU)
+        core_model.vision_tower = move_to(core_model.vision_tower, device=CPU)
+        core_model.audio_tower = move_to(core_model.audio_tower, device=CPU)
+
+    def load_processor(self) -> ProcessorMixin:
+        return AutoProcessor.from_pretrained(self.model_local_path, trust_remote_code=False)
+
+    @classmethod
+    def prepare_inputs_for_conversations(
+        cls,
+        processor: ProcessorMixin,
+        conversations: list[dict] | list[list[dict]],
+    ):
+        return processor.apply_chat_template(
+            conversations,
+            tokenize=True,
+            add_generation_prompt=True,
+            reasoning_effort="medium",
+            return_dict=True,
+            return_tensors="pt",
+        )
+
+    def prepare_dataset(self, calibration_dataset, batch_size: int = 1, **kwargs):
+        del kwargs
+        processor = self.load_processor()
+        calibration_data = []
+        for batch in batched(calibration_dataset, batch_size):
+            calibration_data.append(self.prepare_inputs_for_conversations(processor, batch))
+        del processor
+        return calibration_data
+
+    def move_input_capture_example(
+        self,
+        example: Dict[str, Any],
+        data_device: torch.device,
+    ) -> Dict[str, Any]:
+        example = super().move_input_capture_example(example, data_device)
+        pixel_values = example.get("pixel_values")
+        if not torch.is_tensor(pixel_values) or not pixel_values.is_floating_point():
+            return example
+
+        first_parameter = next(self.model.model.vision_tower.parameters(), None)
+        if first_parameter is not None:
+            example["pixel_values"] = pixel_values.to(
+                device=first_parameter.device,
+                dtype=first_parameter.dtype,
+            )
+        return example
+
+    def prepare_layer_replay_kwargs(self, layer, layer_input, additional_inputs, target_device):
+        additional_inputs = super().prepare_layer_replay_kwargs(
+            layer,
+            layer_input,
+            additional_inputs,
+            target_device,
+        )
+        if not layer_input or not torch.is_tensor(layer_input[0]):
+            return additional_inputs
+
+        self_attn = getattr(layer, "self_attn", None)
+        layer_config = getattr(self_attn, "config", None)
+        if layer_config is None:
+            return additional_inputs
+
+        # The first-layer hook captures the sliding layer's prepared 4D mask.
+        # Rebuild it for every replayed layer so full-attention blocks do not
+        # accidentally inherit the sliding window. ``conv_mask`` retains the
+        # original 2D padding signal when the calibration batch is padded.
+        padding_mask = additional_inputs.get("conv_mask")
+        if not torch.is_tensor(padding_mask) or padding_mask.ndim != 2:
+            captured_mask = additional_inputs.get("attention_mask")
+            padding_mask = captured_mask if torch.is_tensor(captured_mask) and captured_mask.ndim == 2 else None
+
+        mask_factory = (
+            create_sliding_window_causal_mask
+            if getattr(layer, "layer_type", None) == "hybrid_sliding"
+            else create_causal_mask
+        )
+        additional_inputs["attention_mask"] = mask_factory(
+            config=layer_config,
+            inputs_embeds=layer_input[0],
+            attention_mask=padding_mask,
+            past_key_values=additional_inputs.get("past_key_values"),
+            position_ids=additional_inputs.get("position_ids"),
+            layer_idx=getattr(self_attn, "layer_idx", None),
+        )
+        return additional_inputs
+
+
+__all__ = ["InklingMMQModel"]