Instructions to use dingodb/chatglm-tuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dingodb/chatglm-tuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dingodb/chatglm-tuning", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dingodb/chatglm-tuning", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model-00004-of-00007.bin from dingodb/chatglm-tuning: direct link, hf CLI and curl.
- Browser
- Download file 1.82 GB
-
https://huggingface.co/dingodb/chatglm-tuning/resolve/main/pytorch_model-00004-of-00007.bin
- Command line
-
hf download hf://dingodb/chatglm-tuning/pytorch_model-00004-of-00007.bin
-
curl -L -o pytorch_model-00004-of-00007.bin https://huggingface.co/dingodb/chatglm-tuning/resolve/main/pytorch_model-00004-of-00007.bin
1.82 GB
- Xet hash:
- 01783b34aaeb7c2076932c4ad5edd25eed1f6257c5661e03d700ab92339f070d
- Size of remote file:
- 1.82 GB
- SHA256:
- 2d0b5944f14b2609427ad673049600ea993b2a1675ae1b050c3cbe65900b6296
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