diff --git a/AGENTS.md b/AGENTS.md
index 5236a00cf..a8bacbd5e 100644
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -171,6 +171,9 @@
 - Reuse existing model classes, blocks, ops, and helper modules when appropriate.
   Before implementing a new version of a model component, search the existing
   model code for a class or helper that already provides the behavior.
+- Model detection code that inspects linear weight shapes should only use the
+  first dimension. The second dimension may be half the original size for
+  NVFP4 or other 4-bit quantized models.
 - Avoid adding `einops` usage in core inference code. Use native torch tensor
   ops such as `reshape`, `view`, `permute`, `transpose`, `flatten`, `unflatten`,
   `unsqueeze`, and `squeeze` instead.
diff --git a/CLAUDE.md b/CLAUDE.md
new file mode 120000
index 000000000..47dc3e3d8
--- /dev/null
+++ b/CLAUDE.md
@@ -0,0 +1 @@
+AGENTS.md
\ No newline at end of file
diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py
index bb11ed425..044fd513c 100644
--- a/comfy/sd1_clip.py
+++ b/comfy/sd1_clip.py
@@ -551,18 +551,24 @@ class SDTokenizer:
     def _try_get_embedding(self, embedding_name:str):
         '''
         Takes a potential embedding name and tries to retrieve it.
-        Returns a Tuple consisting of the embedding and any leftover string, embedding can be None.
+        Returns a Tuple consisting of the embedding, the cleaned embedding name, and any leftover string, embedding can be None.
         '''
         split_embed = embedding_name.split()
         embedding_name = split_embed[0]
         leftover = ' '.join(split_embed[1:])
+
+        match = re.search(r'[<\[]', embedding_name)
+        if match is not None:
+            leftover = embedding_name[match.start():] + (" " + leftover if leftover else "")
+            embedding_name = embedding_name[:match.start()]
+
         embed = load_embed(embedding_name, self.embedding_directory, self.embedding_size, self.embedding_key)
         if embed is None:
             stripped = embedding_name.strip(',')
             if len(stripped) < len(embedding_name):
                 embed = load_embed(stripped, self.embedding_directory, self.embedding_size, self.embedding_key)
-                return (embed, "{} {}".format(embedding_name[len(stripped):], leftover))
-        return (embed, leftover)
+                return (embed, embedding_name, "{} {}".format(embedding_name[len(stripped):], leftover))
+        return (embed, embedding_name, leftover)
 
     def pad_tokens(self, tokens, amount):
         if self.pad_left:
@@ -611,7 +617,7 @@ class SDTokenizer:
 
                 if matched_id is not None and self.embedding_directory is not None:
                     embedding_name = word[len(matched_id):].strip('\n')
-                    embed, leftover = self._try_get_embedding(embedding_name)
+                    embed, embedding_name, leftover = self._try_get_embedding(embedding_name)
                     if embed is None:
                         logging.warning(f"warning, embedding:{embedding_name} does not exist, ignoring")
                     else:
diff --git a/comfy_api_nodes/nodes_bytedance.py b/comfy_api_nodes/nodes_bytedance.py
index a67883555..58307290d 100644
--- a/comfy_api_nodes/nodes_bytedance.py
+++ b/comfy_api_nodes/nodes_bytedance.py
@@ -2611,7 +2611,7 @@ class ByteDanceSeedAudioNode(IO.ComfyNode):
         return IO.Schema(
             node_id="ByteDanceSeedAudio",
             display_name="ByteDance Seed Audio 1.0",
-            category="api node/audio/ByteDance",
+            category="partner/audio/ByteDance",
             description=(
                 "Generate speech, music, sound effects and multi-speaker dialogue from a single prompt "
                 "with ByteDance Seed Audio 1.0. Describe the voice(s), emotion, ambience, background music "