Instructions to use zai-org/glm-large-chinese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zai-org/glm-large-chinese with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="zai-org/glm-large-chinese", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("zai-org/glm-large-chinese", trust_remote_code=True) model = AutoModel.from_pretrained("zai-org/glm-large-chinese", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Inquiries regarding implementation details
#1
by LemonNoel - opened
Thanks for your excellent work! I attempted to execute the fine-tuning code in GLM, but I noticed a slight difference in the implementation of the gelu function. Specifically, GLM uses the "approximate" strategy, whereas HuggingFace uses the default mode. I'm unsure which one I should use when fine-tuning the model.