diff --git a/laya/agent.py b/laya/agent.py
index b0fb78a..0d88c86 100644
--- a/laya/agent.py
+++ b/laya/agent.py
@@ -151,19 +151,20 @@ def _load_tokenizer(tok_dir: str, cfg: Dict) -> Any:
     """
     from transformers import AutoTokenizer

+    remote = bool(cfg.get("trust_remote_code", False))
     if not os.path.isdir(tok_dir):
-        return AutoTokenizer.from_pretrained(cfg.get("encoder"))
+        return AutoTokenizer.from_pretrained(cfg.get("encoder"), trust_remote_code=remote)

     try:
         stamp = (os.path.abspath(tok_dir), os.path.getmtime(os.path.join(tok_dir, "tokenizer_config.json")))
     except OSError:
-        return AutoTokenizer.from_pretrained(tok_dir)
+        return AutoTokenizer.from_pretrained(tok_dir, trust_remote_code=remote)

     with _TOKENIZERS_LOCK:
         cached = _TOKENIZERS.get(stamp)
         if cached is not None:
             return cached
-        tokenizer = AutoTokenizer.from_pretrained(tok_dir)
+        tokenizer = AutoTokenizer.from_pretrained(tok_dir, trust_remote_code=remote)
         _TOKENIZERS[stamp] = tokenizer
         return tokenizer

diff --git a/laya/common.py b/laya/common.py
index 6c4c426..2be1aec 100644
--- a/laya/common.py
+++ b/laya/common.py
@@ -201,12 +201,26 @@ def _apply_rope_config(ecfg) -> None:
 def build_model(cfg: Dict, encoder_dir: Optional[str] = None, pretrained: bool = True) -> DecisionModel:
     from transformers import AutoConfig, AutoModel

+    # Encoders whose modeling code lives on the Hub (e.g. NeoBERT) must opt in via the config.
+    remote = bool(cfg.get("trust_remote_code", False))
     if not pretrained or (encoder_dir and os.path.exists(encoder_dir)):
-        ecfg = AutoConfig.from_pretrained(encoder_dir or cfg["encoder"])
+        ecfg = AutoConfig.from_pretrained(encoder_dir or cfg["encoder"], trust_remote_code=remote)
         _apply_rope_config(ecfg)
-        enc = AutoModel.from_config(ecfg, attn_implementation="sdpa")
+        enc = AutoModel.from_config(ecfg, attn_implementation="sdpa", trust_remote_code=remote)
     else:
-        enc = AutoModel.from_pretrained(cfg["encoder"], attn_implementation="sdpa")
+        enc = AutoModel.from_pretrained(cfg["encoder"], attn_implementation="sdpa", trust_remote_code=remote)
+    # NeoBERT computes its RoPE tables as non-persistent buffers in __init__; transformers 5 builds models on
+    # the meta device, so they come back as uninitialised memory and every forward pass returns NaN.
+    if hasattr(enc, "freqs_cos"):
+        import sys
+
+        precompute = sys.modules[type(enc).__module__].precompute_freqs
+        c = enc.config
+        enc.freqs_cos, enc.freqs_sin = precompute(c.hidden_size // c.num_attention_heads, c.max_length)
+    # Some encoder configs pin a half-precision dtype (NeoBERT ships bfloat16), which transformers 5 honours. Keep
+    # master weights in float32: bf16 weights round most optimizer updates to zero, and CPU inference in bf16 is ~8x slower.
+    # Mixed precision is applied by autocast at train/inference time instead.
+    enc = enc.float()
     return DecisionModel(enc, cfg.get("head_layers", 2), len(cfg.get("act_costs", {})) + 1)