diff --git a/README.md b/README.md
index 611be5c..04d0011 100644
--- a/README.md
+++ b/README.md
@@ -173,3 +173,4 @@ curl http://localhost:80/v1/chat/completions \
 | v0.0.2 | 2026-02-04 | **Qwen3 模型支持**：实现 QK Normalization 架构适配，修复 rope_scaling 和 tokenizer 兼容性问题，解决张量连续性导致的 view 操作失败 |
 | v0.0.3 | 2026-02-06 | **Transformers 通用后端**：支持通过 `auto_map` 加载任意自定义 HuggingFace 模型，新增 registry 回退逻辑、Linear 返回值处理、RMSNorm 维度恢复等 |
 | v0.0.3.1 | 2026-02-06 | **CNNL Tensor 溢出修复**：解决极小模型在大显存设备上部署时 KV cache 元素数超过 int32 限制的问题，在 mlu_worker 和 cache_engine 中添加双重防护 |
+| v0.0.4 | 2026-02-10 | **Gemma3 模型支持**：新增 Gemma3ForCausalLM 模型实现（含 QK Normalization、per-layer rope 配置、layer_types 滑动窗口），修复 `patch_rope_scaling_dict` 在 rope_scaling 缺少 `rope_type` 键时崩溃的问题，更新模型注册表及 config.py 中 interleaved attention 和 dtype 自动处理逻辑 |
diff --git a/vllm-v0.6.2/vllm/model_executor/models/gemma3.py b/vllm-v0.6.2/vllm/model_executor/models/gemma3.py
index 45a03f5..81aa9a7 100644
--- a/vllm-v0.6.2/vllm/model_executor/models/gemma3.py
+++ b/vllm-v0.6.2/vllm/model_executor/models/gemma3.py
@@ -140,11 +140,21 @@ class Gemma3Attention(nn.Module):
             self.is_sliding = (layer_idx % 2 == 1
                                and config.sliding_window is not None)
 
+        # Extract rope config, compatible with both old-style (rope_theta,
+        # rope_scaling) and new-style (rope_parameters dict) transformers.
+        rope_params = getattr(config, "rope_parameters", None)
+
         # Set up rope based on layer type
         if self.is_sliding:
             # Local/sliding attention uses rope_local_base_freq
-            local_base = getattr(config, "rope_local_base_freq",
-                                 self.rope_theta)
+            if hasattr(config, "rope_local_base_freq"):
+                local_base = config.rope_local_base_freq
+            elif (isinstance(rope_params, dict)
+                  and "sliding_attention" in rope_params):
+                local_base = rope_params["sliding_attention"].get(
+                    "rope_theta", self.rope_theta)
+            else:
+                local_base = self.rope_theta
             self.rotary_emb = get_rope(
                 self.head_dim,
                 rotary_dim=self.head_dim,
@@ -155,11 +165,27 @@ class Gemma3Attention(nn.Module):
         else:
             # Global attention uses rope_scaling from config
             rope_scaling = getattr(config, "rope_scaling", None)
+            rope_base = self.rope_theta
+            if rope_scaling is None and isinstance(rope_params, dict):
+                # Try to extract from rope_parameters (newer transformers)
+                if "full_attention" in rope_params:
+                    rp = rope_params["full_attention"]
+                else:
+                    rp = rope_params
+                rope_base = rp.get("rope_theta", self.rope_theta)
+                rtype = rp.get("rope_type", None)
+                if rtype and rtype != "default":
+                    rope_scaling = {
+                        k: v for k, v in rp.items()
+                        if k not in ("rope_theta",)
+                    }
+                    rope_scaling["type"] = rope_scaling.pop("rope_type",
+                                                            rtype)
             self.rotary_emb = get_rope(
                 self.head_dim,
                 rotary_dim=self.head_dim,
                 max_position=max_position_embeddings,
-                base=self.rope_theta,
+                base=rope_base,
                 is_neox_style=True,
                 rope_scaling=rope_scaling,
             )
@@ -210,6 +236,16 @@ class Gemma3DecoderLayer(nn.Module):
     ) -> None:
         super().__init__()
         self.hidden_size = config.hidden_size
+        # Extract rope_theta: try direct attribute first, then
+        # rope_parameters dict (newer transformers), fallback to 10000.0
+        rope_params = getattr(config, "rope_parameters", None)
+        if hasattr(config, "rope_theta"):
+            rope_theta = config.rope_theta
+        elif isinstance(rope_params, dict):
+            rope_theta = rope_params.get("rope_theta", 10000.0)
+        else:
+            rope_theta = 10000.0
+
         self.self_attn = Gemma3Attention(
             layer_idx=layer_idx,
             config=config,
@@ -218,7 +254,7 @@ class Gemma3DecoderLayer(nn.Module):
             num_kv_heads=config.num_key_value_heads,
             head_dim=config.head_dim,
             max_position_embeddings=config.max_position_embeddings,
-            rope_theta=config.rope_theta,
+            rope_theta=rope_theta,
             cache_config=cache_config,
             quant_config=quant_config,
             # Gemma3 does not use attn logit softcapping