Add vLLM support for Gemma-3-1b-it

models/tt_transformers/tt/generator_vllm.py

@@ -373,3 +373,37 @@ def decode_forward(self, *args, **kwargs):
 
     def allocate_kv_cache(self, *args, **kwargs):
         return allocate_vllm_kv_cache(*args, **kwargs, dp_model=self.model, tt_cache_path=self.cache_path)
+
+
+class Gemma3ForCausalLM(Generator):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    @classmethod
+    def initialize_vllm_model(
+        cls, hf_config, mesh_device, max_batch_size, max_seq_len=32768, n_layers=None, tt_data_parallel=1
+    ):
+        tt_model, model_args = initialize_vllm_text_transformer(
+            hf_config,
+            tt_data_parallel,
+            mesh_device,
+            max_batch_size,
+            max_seq_len=max_seq_len,
+            n_layers=n_layers,
+            dtype=ttnn.bfloat16,
+            optimizations=DecodersPrecision.accuracy,
+        )
+        return cls(tt_model, model_args, mesh_device)
+
+    @property
+    def cache_path(self):
+        return self.model_args[0].model_cache_path
+
+    def prefill_forward(self, *args, **kwargs):
+        return super().prefill_forward_text(*args, **kwargs)
+
+    def decode_forward(self, *args, **kwargs):
+        return super().decode_forward_text(*args, **kwargs)
+
+    def allocate_kv_cache(self, *args, **kwargs):
+        return allocate_vllm_kv_cache(*args, **kwargs, dp_model=self.model, tt_cache_path=self.cache_path)