Document Question Answering
Transformers
Safetensors
chatglm
feature-extraction
text-generation-inference
custom_code
4-bit precision
bitsandbytes
Instructions to use nikravan/glm-4vq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nikravan/glm-4vq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="nikravan/glm-4vq", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nikravan/glm-4vq", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
got an unexpected keyword argument 'standardize_cache_format'
#4
by CCChen - opened
.cache\huggingface\modules\transformers_modules\glm-4vq\modeling_chatglm.py", line 948, in _update_model_kwargs_for_generation
model_kwargs["past_key_values"] = self._extract_past_from_model_output(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: GenerationMixin._extract_past_from_model_output() got an unexpected keyword argument 'standardize_cache_format'
Name: transformers
Version: 4.44.0