Instructions to use m-a-p/Kun-PrimaryChatModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use m-a-p/Kun-PrimaryChatModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="m-a-p/Kun-PrimaryChatModel", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("m-a-p/Kun-PrimaryChatModel", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from m-a-p/Kun-PrimaryChatModel: direct link, hf CLI and curl.
- Browser
- Download file 6.46 kB
-
https://huggingface.co/m-a-p/Kun-PrimaryChatModel/resolve/main/training_args.bin
- Command line
-
hf download hf://m-a-p/Kun-PrimaryChatModel/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/m-a-p/Kun-PrimaryChatModel/resolve/main/training_args.bin
6.46 kB
- Xet hash:
- 99a4578ffd79bc4490ac4a8b32c67490684a775c3b9f823200e99976dac7a024
- Size of remote file:
- 6.46 kB
- SHA256:
- 31699e522a95b79fcf13114f20ef996c0155ece44693323431591e4cef088555
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