diff --git a/transformer_lens/loading_from_pretrained.py b/transformer_lens/loading_from_pretrained.py
index 8bfb6315d..e7c60358f 100644
--- a/transformer_lens/loading_from_pretrained.py
+++ b/transformer_lens/loading_from_pretrained.py
@@ -47,6 +47,7 @@
 )
 
 OFFICIAL_MODEL_NAMES = [
+    "openai/gpt-oss-20b",
     "gpt2",
     "gpt2-medium",
     "gpt2-large",
@@ -268,6 +269,7 @@
 
 # Model Aliases:
 MODEL_ALIASES = {
+    "openai/gpt-oss-20b": ["gpt-oss-20b", "gpt-oss"],
     "NeelNanda/SoLU_1L_v9_old": ["solu-1l-pile", "solu-1l-old"],
     "NeelNanda/SoLU_2L_v10_old": ["solu-2l-pile", "solu-2l-old"],
     "NeelNanda/SoLU_4L_v11_old": ["solu-4l-pile", "solu-4l-old"],
@@ -794,6 +796,12 @@ def convert_hf_model_config(model_name: str, **kwargs: Any):
         architecture = "Gemma2ForCausalLM"
     elif "gemma" in official_model_name.lower():
         architecture = "GemmaForCausalLM"
+    # elif architecture in ("GptOssForCausalLM", "GPTOssForCausalLM"):
+    #     from transformer_lens.factories.architecture_adapter_factory import (
+    #         ArchitectureAdapterFactory,
+    #     )
+    #     adapter = ArchitectureAdapterFactory().from_hf_config(hf_config)
+    #     cfg_dict = adapter.make_cfg_dict(hf_config)
     else:
         huggingface_token = os.environ.get("HF_TOKEN", "")
         hf_config = AutoConfig.from_pretrained(
@@ -802,6 +810,7 @@ def convert_hf_model_config(model_name: str, **kwargs: Any):
             **kwargs,
         )
         architecture = hf_config.architectures[0]
+    
 
     cfg_dict: dict[str, Any]
     if official_model_name.startswith(
@@ -824,6 +833,41 @@ def convert_hf_model_config(model_name: str, **kwargs: Any):
             "final_rms": True,
             "gated_mlp": True,
         }
+        
+    elif official_model_name.startswith(
+        ("gpt-oss-20b", "openai/gpt-oss-20b")
+    ):  # architecture for gpt-oss
+        cfg_dict = {
+            "d_model": 2880,
+            "d_head": 64,
+            "n_heads": 64,
+            "d_mlp": 2880,
+            "n_layers": 24,
+            "n_ctx": 131072,
+            "eps": 1e-5,
+            "d_vocab": 201088,
+            "act_fn": "silu",
+            "n_key_value_heads": 8,
+            "normalization_type": "RMS",
+            "positional_embedding_type": "rotary",
+            "rotary_adjacent_pairs": False,
+            "rotary_dim": 64,
+            "rotary_base": 150000, 
+            "num_experts": 32,  
+            "experts_per_token": 4, 
+
+            
+            "gated_mlp": True,              # SWiGLU
+            "use_local_attn": True,         # sliding window attention
+            "window_size": 128,             # sliding_window
+            "attn_types": [                 # 来自 layer_types（24 层交替）
+                "local","global","local","global","local","global",
+                "local","global","local","global","local","global",
+                "local","global","local","global","local","global",
+                "local","global","local","global","local","global",
+            ],
+        }
+    
     elif official_model_name.startswith("codellama"):  # same architecture CodeLlama and Llama-2
         cfg_dict = {
             "d_model": 4096,
@@ -838,9 +882,9 @@ def convert_hf_model_config(model_name: str, **kwargs: Any):
             "normalization_type": "RMS",
             "positional_embedding_type": "rotary",
             "rotary_dim": 4096 // 32,
-            "final_rms": True,
-            "gated_mlp": True,
-            "rotary_base": 1000000,
+            "n_key_value_heads": 8, 
+            "num_experts": 32, 
+            "experts_per_token": 4, 
         }
         if "python" in official_model_name.lower():
             # The vocab size of python version of CodeLlama-7b is 32000
@@ -1085,6 +1129,7 @@ def convert_hf_model_config(model_name: str, **kwargs: Any):
             "NTK_by_parts_factor": 8.0,
             "NTK_original_ctx_len": 8192,
         }
+    
     elif architecture == "GPTNeoForCausalLM":
         cfg_dict = {
             "d_model": hf_config.hidden_size,
@@ -1986,10 +2031,31 @@ def get_pretrained_state_dict(
             state_dict = convert_gemma_weights(hf_model, cfg)
         elif cfg.original_architecture == "Gemma2ForCausalLM":
             state_dict = convert_gemma_weights(hf_model, cfg)
-        else:
-            raise ValueError(
-                f"Loading weights from the architecture is not currently supported: {cfg.original_architecture}, generated from model name {cfg.model_name}. Feel free to open an issue on GitHub to request this feature."
+        # ---------- NEW: adapter fallback for new / custom HF architectures ----------
+        elif cfg.original_architecture in ("GptOssForCausalLM", "GPTOssForCausalLM"):
+            # 延迟导入以避免循环依赖
+            from transformer_lens.factories.architecture_adapter_factory import (
+                ArchitectureAdapterFactory,
             )
+            adapter = ArchitectureAdapterFactory().from_hf_config(hf_model.config)
+            state_dict = adapter.to_transformer_lens_state_dict(hf_model, cfg)
+        else:
+            # 兜底：尝试用 adapter 支持更多未来架构
+            try:
+                from transformer_lens.factories.architecture_adapter_factory import (
+                    ArchitectureAdapterFactory,
+                )
+                adapter = ArchitectureAdapterFactory().from_hf_config(hf_model.config)
+                state_dict = adapter.to_transformer_lens_state_dict(hf_model, cfg)
+            except Exception:
+                raise ValueError(
+                    f"Loading weights from the architecture is not currently supported: {cfg.original_architecture}, generated from model name {cfg.model_name}. Feel free to open an issue on GitHub to request this feature."
+                )
+        
+        # else:
+        #     raise ValueError(
+        #         f"Loading weights from the architecture is not currently supported: {cfg.original_architecture}, generated from model name {cfg.model_name}. Feel free to open an issue on GitHub to request this feature."
+        #     )
 
         return state_dict