[Model] Gemma3: Fix GGUF loading and quantization (#26189)

Signed-off-by: Luciano Martins <lucianommartins@users.noreply.github.com>
Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>
Co-authored-by: Luciano Martins <lucianommartins@users.noreply.github.com>
Co-authored-by: Isotr0py <mozf@mail2.sysu.edu.cn>

Author: lucianommartins

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

@@ -372,6 +372,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.embed_tokens = VocabParallelEmbedding(
             config.vocab_size,
             config.hidden_size,
+            quant_config=quant_config,
             prefix=f"{prefix}.embed_tokens",
         )
         self.start_layer, self.end_layer, self.layers = make_layers(
@@ -442,6 +443,15 @@ 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:
+            # Revert +1 during llama.cpp conversion
+            # see: https://github.com/ggml-org/llama.cpp/blob/be7c3034108473beda214fd1d7c98fd6a7a3bdf5/convert_hf_to_gguf.py#L3397-L3400
+            if (
+                self.quant_config
+                and self.quant_config.get_name() == "gguf"
+                and name.endswith("norm.weight")
+            ):
+                loaded_weight -= 1
+
             if self.quant_config is not None and (
                 scale_name := self.quant_config.get_cache_scale(name)
             ):