B200 VLLM:glm53-flash
#2
by corgito - opened
It's complaining that there isn't a processor_config.json. I can clearly see one Just noting from someone trying to deploy via runpod.
(APIServer pid=58) File "/usr/local/lib/python3.12/dist-packages/vllm/transformers_utils/processors/glm5next.py", line 853, in from_pretrained
(APIServer pid=58) with open(os.path.join(model_path, "processor_config.json")) as f:
(APIServer pid=58) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
same here on GB10
I think the issue is caused by accessing the configuration file directory using the model name itself.
If you have mounted the Hugging Face cache, you can work around the problem with the following patch.
--- a/transformers_utils/processors/glm5next.py 2026-08-27 10:44:53.961431596 +0900
+++ b/transformers_utils/processors/glm5next.py 2026-08-27 10:46:56.517110367 +0900
@@ -49,7 +49,7 @@
VideosKwargs,
)
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
-from transformers.utils import TensorType, logging
+from transformers.utils import TensorType, logging, cached_file
from transformers.video_processing_utils import BaseVideoProcessor
from transformers.video_utils import (
VideoInput,
@@ -850,7 +850,8 @@
**{k: v for k, v in ip_cfg.items() if k != “image_processor_type”}
)
- with open(os.path.join(model_path, “processor_config.json”)) as f:
+ config_path = cached_file(model_path, “processor_config.json”)
+ with open(config_path) as f:
vp_cfg = _cap_cfg(dict(json.load(f)[“video_processor”]), is_video=True)
video_processor = Glm5NextVideoProcessor(
**{k: v for k, v in vp_cfg.items() if k != “video_processor_type”}