diff --git a/unsloth/models/loader.py b/unsloth/models/loader.py
index 5edd88abfa..2944fcd071 100644
--- a/unsloth/models/loader.py
+++ b/unsloth/models/loader.py
@@ -485,7 +485,6 @@ class FastModel(FastBaseModel):
         auto_model                 = None,
         whisper_language           = None,
         whisper_task               = None,
-        is_multimodal              = None,
         *args, **kwargs,
     ):
         if token is None: token = get_token()
@@ -542,7 +541,7 @@ class FastModel(FastBaseModel):
             if transformers_version < Version("4.50.0.dev0"):
                 raise RuntimeError("Unsloth: Granite Vision only works on transformers >= 4.50.0." + NIGHTLY)
         elif "csm-1b" in model_name.lower():
-            os.environ["UNSLOTH_DISABLE_STATIC_GENERATION"] = "1"
+            os.environ["UNSLOTH_DISABLE_STATIC_GENERATION"] = "1" # Sesame fails
             os.environ["UNSLOTH_FORCE_CUSTOM_DTYPE"] = "torch.float16;if name.endswith(('_proj', 'fc1', 'fc2', 'codebook', 'head')): module.to(torch.float16)"
         elif "olmo-2" in model_name.lower() and transformers_version < Version("4.50.0.dev0"):
             raise RuntimeError("Unsloth: OLMo-2 only works on transformers >= 4.50.0." + NIGHTLY)
diff --git a/unsloth/models/vision.py b/unsloth/models/vision.py
index 97ed9945d7..2ba7c1391b 100644
--- a/unsloth/models/vision.py
+++ b/unsloth/models/vision.py
@@ -246,7 +246,6 @@ class FastBaseModel:
         supports_sdpa     = True,
         whisper_language  = None,
         whisper_task      = None,
-        is_multimodal     = False,
         **kwargs,
     ):
         if model_types is None:
@@ -402,7 +401,7 @@ class FastBaseModel:
         is_vlm = (auto_model is AutoModelForVision2Seq)
         is_whisper = (whisper_language is not None and whisper_task is not None)
         auto_processor = AutoProcessor if (is_vlm or is_whisper) else AutoTokenizer
-        if (whisper_language and whisper_task) or is_multimodal:
+        if (whisper_language and whisper_task) or auto_model.__name__.endswith("ForConditionalGeneration"):
            tokenizer = auto_processor.from_pretrained(
                 tokenizer_name,
                 padding_side = "right",