Instructions to use rangell/Qwen2-0.5B-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rangell/Qwen2-0.5B-SFT with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rangell/Qwen2-0.5B-SFT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rangell/Qwen2-0.5B-SFT: direct link, hf CLI and curl.
- Browser
- Download file 5.59 kB
-
https://huggingface.co/rangell/Qwen2-0.5B-SFT/resolve/main/training_args.bin
- Command line
-
hf download hf://rangell/Qwen2-0.5B-SFT/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rangell/Qwen2-0.5B-SFT/resolve/main/training_args.bin
5.59 kB
- Xet hash:
- f0d3a026d920f765d7497af702cfb1108bcd3ca51661852f78c929e4264fbd13
- Size of remote file:
- 5.59 kB
- SHA256:
- 1c9abbe8d4757dcd637833a35c6266e00f599265dd6fed7d8084fc243371bc42
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.