[Model] Gemma3: Fix GGUF loading and quantization

vllm/model_executor/model_loader/gguf_loader.py

@@ -72,6 +72,10 @@ def _get_gguf_weights_map(self, model_config: ModelConfig):
         # hack: ggufs have a different name than transformers
         if model_type == "cohere":
             model_type = "command-r"
+        if model_type == "gemma3_text":
+            # Gemma3 models use "gemma3_text" in HuggingFace but
+            # "gemma3" in GGUF architecture naming
+            model_type = "gemma3"
         if model_type in ("deepseek_v3", "deepseek_v2"):
             model_type = "deepseek2"
             # GGUF layer map assumes that we will have a merged expert weights

vllm/model_executor/models/gemma3.py

@@ -44,6 +44,7 @@
     default_weight_loader,
     maybe_remap_kv_scale_name,
 )
+from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
 from ...attention.layers.encoder_only_attention import EncoderOnlyAttention
@@ -442,6 +443,20 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         params_dict = dict(self.named_parameters())
         loaded_params: set[str] = set()
         for name, loaded_weight in weights:
+            # Apply GGUF-specific RMSNorm weight correction for Gemma3
+            # This must happen BEFORE any transformations (transpose, etc.)
+            # GemmaRMSNorm computes: output = x * (1 + weight)
+            # GGUF stores full weight values (for standard x * weight)
+            # but vLLM's GemmaRMSNorm expects (weight - 1) since it adds 1
+            # during the forward pass.
+            if (
+                self.quant_config is not None
+                and self.quant_config.get_name() == "gguf"
+                and "norm" in name
+                and len(loaded_weight.shape) == 1
+            ):
+                loaded_weight = loaded_weight - 1.0
+
             if self.quant_config is not None and (
                 scale_name := self.quant_config.get_cache_scale(name)
             ):
@@ -485,6 +500,21 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                 # Skip loading extra bias for GPTQ models.
                 if name.endswith(".bias") and name not in params_dict:
                     continue
+                # Skip GGUF qweight_type metadata for layers that don't have it
+                # (e.g., embedding layers). These are handled by GGUF
+                # quantization layers.
+                if name.endswith(".qweight_type") and name not in params_dict:
+                    continue
+
+                # Handle GGUF qweight for embedding and other non-merged layers
+                # GGUF uses .qweight for quantized weights, but some layers
+                # (like VocabParallelEmbedding) expect .weight
+                if name.endswith(".qweight") and name not in params_dict:
+                    # Try to load as regular weight instead
+                    name = name.replace(".qweight", ".weight")
+                    if name not in params_dict:
+                        continue
+
                 # Remapping the name of FP8 kv-scale.
                 name = maybe_remap_kv_scale_name(name, params_dict)
                 if name is None:
@@ -519,6 +549,8 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         del lora_config  # Unused.
         super().__init__()
         self.config = config
+        # Store model config for quantization access
+        self.model_config = vllm_config.model_config
         # currently all existing Gemma models have `tie_word_embeddings` enabled
         assert config.tie_word_embeddings
         self.quant_config = quant_config
@@ -551,8 +583,11 @@ def forward(
     def compute_logits(
         self,
         hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
     ) -> Optional[torch.Tensor]:
-        logits = self.logits_processor(self.model.embed_tokens, hidden_states)
+        logits = self.logits_processor(
+            self.model.embed_tokens, hidden_states, sampling_metadata
+        )
         return logits
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: