Instructions to use s65b40/ChatGLM-Med with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use s65b40/ChatGLM-Med with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="s65b40/ChatGLM-Med", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("s65b40/ChatGLM-Med", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 119d21a0b11226274fb2fa423e0cc0a13d06078c0e1c3cf4e03afd1498be3863
- Size of remote file:
- 3.52 kB
- SHA256:
- 164c847d97799194aedc004b3e7b6a4da88a9408da16201d2799d8e761fd15e2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.