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
- Xet hash:
- 32b3828ee5bd176a56fb05ad692ece4f06f2212dc5faf2eb1a273afd632eae45
- Size of remote file:
- 2.62 MB
- SHA256:
- 5a493598071550244b2ee7f26118f3edec2150b9dfa967929a99052ac83fe716
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