Instructions to use Hugging-Steven/chatglm2-6b-torchkeras with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hugging-Steven/chatglm2-6b-torchkeras with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Hugging-Steven/chatglm2-6b-torchkeras", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Hugging-Steven/chatglm2-6b-torchkeras", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from Hugging-Steven/chatglm2-6b-torchkeras: direct link, hf CLI and curl.
- Browser
- Download file 1.02 MB
-
https://huggingface.co/Hugging-Steven/chatglm2-6b-torchkeras/resolve/main/tokenizer.model
- Command line
-
hf download hf://Hugging-Steven/chatglm2-6b-torchkeras/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/Hugging-Steven/chatglm2-6b-torchkeras/resolve/main/tokenizer.model
1.02 MB
- Xet hash:
- 2d298e57b6acbc61ddd46b042c2a2c03b42becb9a074e65f7c12bf482ae12af9
- Size of remote file:
- 1.02 MB
- SHA256:
- e7dc4c393423b76e4373e5157ddc34803a0189ba96b21ddbb40269d31468a6f2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.