feat(models): register gemma4_unified_text text-only GPTQ definition

gptqmodel/models/auto.py

@@ -97,7 +97,7 @@
 from .definitions.gemma3 import Gemma3ForConditionalGenerationGPTQ, Gemma3QModel  # noqa: E402
 from .definitions.gemma3n import Gemma3nForConditionalGenerationGPTQ, Gemma3nTextQModel  # noqa: E402
 from .definitions.gemma4 import Gemma4ForConditionalGenerationGPTQ, Gemma4TextQModel  # noqa: E402
-from .definitions.gemma4_unified import Gemma4UnifiedForConditionalGenerationGPTQ  # noqa: E402
+from .definitions.gemma4_unified import Gemma4UnifiedForConditionalGenerationGPTQ, Gemma4UnifiedTextQModel  # noqa: E402
 from .definitions.glm import GlmQModel  # noqa: E402
 from .definitions.glm4_moe import GLM4MoEGPTQ  # noqa: E402
 from .definitions.glm4_moe_lite import Glm4MoeLiteQModel  # noqa: E402
@@ -256,6 +256,7 @@
     "gemma3n": Gemma3nForConditionalGenerationGPTQ,
     "gemma4_text": Gemma4TextQModel,
     "gemma4": Gemma4ForConditionalGenerationGPTQ,
+    "gemma4_unified_text": Gemma4UnifiedTextQModel,
     "gemma4_unified": Gemma4UnifiedForConditionalGenerationGPTQ,
     "phi": PhiQModel,
     "phi3": Phi3QModel,

gptqmodel/models/definitions/__init__.py

@@ -30,7 +30,7 @@
 from .gemma3 import Gemma3QModel
 from .gemma3n import Gemma3nForConditionalGenerationGPTQ, Gemma3nTextQModel
 from .gemma4 import Gemma4ForConditionalGenerationGPTQ, Gemma4TextQModel
-from .gemma4_unified import Gemma4UnifiedForConditionalGenerationGPTQ
+from .gemma4_unified import Gemma4UnifiedForConditionalGenerationGPTQ, Gemma4UnifiedTextQModel
 from .glm import GlmQModel
 from .glmasr import GlmASRGPTQ
 from .glm_ocr import GlmOCRGPTQ

gptqmodel/models/definitions/gemma4_unified.py

@@ -55,6 +55,28 @@ def _prepare_gemma4_unified_replay_kwargs(model_def, layer, layer_input, additio
     return additional_inputs
 
 
+# Shared decoder layout for both the composite (``gemma4_unified``) and text-only
+# (``gemma4_unified_text``) variants: the standard Gemma sandwich-norm decoder with
+# per-projection q/k/v norms. Unlike the per-layer-input Gemma 4 variants there is no
+# per-layer input gate/projection, so those module-tree entries are intentionally absent.
+_GEMMA4_UNIFIED_DECODER = {
+    "input_layernorm": ("input_layernorm:!",),
+    "self_attn": (
+        "q_norm:!",
+        "q_proj:0",
+        "k_norm:!",
+        "k_proj:0",
+        "v_norm:!",
+        "v_proj:0",
+        "o_proj:1",
+    ),
+    "post_attention_layernorm": ("post_attention_layernorm:!",),
+    "pre_feedforward_layernorm": ("pre_feedforward_layernorm:!",),
+    "mlp": ("gate_proj:0", "up_proj:0", "down_proj:1"),
+    "post_feedforward_layernorm": ("post_feedforward_layernorm:!",),
+}
+
+
 class Gemma4UnifiedForConditionalGenerationGPTQ(BaseQModel):
     """Quantization definition for Gemma 4 unified (multimodal) checkpoints.
 
@@ -75,22 +97,34 @@ class Gemma4UnifiedForConditionalGenerationGPTQ(BaseQModel):
         "language_model",
         "layers",
         "#",
-        {
-            "input_layernorm": ("input_layernorm:!",),
-            "self_attn": (
-                "q_norm:!",
-                "q_proj:0",
-                "k_norm:!",
-                "k_proj:0",
-                "v_norm:!",
-                "v_proj:0",
-                "o_proj:1",
-            ),
-            "post_attention_layernorm": ("post_attention_layernorm:!",),
-            "pre_feedforward_layernorm": ("pre_feedforward_layernorm:!",),
-            "mlp": ("gate_proj:0", "up_proj:0", "down_proj:1"),
-            "post_feedforward_layernorm": ("post_feedforward_layernorm:!",),
-        },
+        _GEMMA4_UNIFIED_DECODER,
+    ]
+
+    def prepare_layer_replay_kwargs(self, layer, layer_input, additional_inputs, target_device):
+        """Refresh Gemma 4 unified rope kwargs during cached layer replay."""
+
+        return _prepare_gemma4_unified_replay_kwargs(self, layer, layer_input, additional_inputs, target_device)
+
+
+class Gemma4UnifiedTextQModel(BaseQModel):
+    """Quantization definition for text-only Gemma 4 unified checkpoints (``gemma4_unified_text``).
+
+    ``Gemma4UnifiedTextModel`` is the standalone language stack of the unified family, so its
+    decoder layers live at ``model.layers`` (no ``language_model`` nesting) with the norm and
+    rotary embedding at ``model.norm`` / ``model.rotary_emb``. The quantizable layout is identical
+    to the composite variant, mirroring how ``gemma4_text`` parallels ``gemma4``.
+    """
+
+    layer_modules_strict = False
+    support_batch_quantize = False
+    pre_lm_head_norm_module = "model.norm"
+    rotary_embedding = "model.rotary_emb"
+
+    module_tree = [
+        "model",
+        "layers",
+        "#",
+        _GEMMA4_UNIFIED_DECODER,
     ]
 
     def prepare_layer_replay_kwargs(self, layer, layer_input, additional_inputs, target_device):