Instructions to use zai-org/WebGLM-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zai-org/WebGLM-2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="zai-org/WebGLM-2B", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("zai-org/WebGLM-2B", trust_remote_code=True) model = AutoModel.from_pretrained("zai-org/WebGLM-2B", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7d0e06f9ca8644130cd3bc07aebfc66baf2d7b74d86a770145c383c7cd2538b0
- Size of remote file:
- 3.84 GB
- SHA256:
- 8dc6d01e84acccd8a5769d5a62fab8c1dbcfc361317b26a1c35546a731d32c9a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.