diff --git a/comfy/sd.py b/comfy/sd.py
index b8e2d0951..7de7dd9c6 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -1272,7 +1272,7 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             clip_target.clip = comfy.text_encoders.kandinsky5.te(**llama_detect(clip_data))
             clip_target.tokenizer = comfy.text_encoders.kandinsky5.Kandinsky5TokenizerImage
         elif clip_type == CLIPType.NEWBIE:
-            clip_target.clip = comfy.text_encoders.newbie.NewBieClipModel
+            clip_target.clip = comfy.text_encoders.newbie.te(**llama_detect(clip_data))
             clip_target.tokenizer = comfy.text_encoders.newbie.NewBieTokenizer
             if "model.layers.0.self_attn.q_norm.weight" in clip_data[0]:
                 clip_data_gemma = clip_data[0]
diff --git a/comfy/text_encoders/newbie.py b/comfy/text_encoders/newbie.py
index 5cc1b5926..31904462b 100644
--- a/comfy/text_encoders/newbie.py
+++ b/comfy/text_encoders/newbie.py
@@ -21,12 +21,13 @@ class NewBieTokenizer:
     def state_dict(self):
         return {}
 
-class NewBieClipModel(torch.nn.Module):
-    def __init__(self, device="cpu", dtype=None, model_options={}):
+class NewBieTEModel(torch.nn.Module):
+    def __init__(self, dtype_gemma=None, device="cpu", dtype=None, model_options={}):
         super().__init__()
-        self.gemma = comfy.text_encoders.lumina2.Gemma3_4BModel(device=device, dtype=dtype, model_options=model_options)
+        dtype_gemma = comfy.model_management.pick_weight_dtype(dtype_gemma, dtype, device)
+        self.gemma = comfy.text_encoders.lumina2.Gemma3_4BModel(device=device, dtype=dtype_gemma, model_options=model_options)
         self.jina = comfy.text_encoders.jina_clip_2.JinaClip2TextModel(device=device, dtype=dtype, model_options=model_options)
-        self.dtypes = {dtype}
+        self.dtypes = {dtype, dtype_gemma}
 
     def set_clip_options(self, options):
         self.gemma.set_clip_options(options)
@@ -50,3 +51,12 @@ class NewBieClipModel(torch.nn.Module):
             return self.gemma.load_sd(sd)
         else:
             return self.jina.load_sd(sd)
+
+def te(dtype_llama=None, llama_quantization_metadata=None):
+    class NewBieTEModel_(NewBieTEModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if llama_quantization_metadata is not None:
+                model_options = model_options.copy()
+                model_options["quantization_metadata"] = llama_quantization_metadata
+            super().__init__(dtype_gemma=dtype_llama, device=device, dtype=dtype, model_options=model_options)
+    return NewBieTEModel_