Instructions to use cimol/b16d9612-9267-485c-a7c6-526ffb20af02 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/b16d9612-9267-485c-a7c6-526ffb20af02 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct") model = PeftModel.from_pretrained(base_model, "cimol/b16d9612-9267-485c-a7c6-526ffb20af02") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from cimol/b16d9612-9267-485c-a7c6-526ffb20af02: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/cimol/b16d9612-9267-485c-a7c6-526ffb20af02/resolve/main/training_args.bin
- Command line
-
hf download hf://cimol/b16d9612-9267-485c-a7c6-526ffb20af02/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cimol/b16d9612-9267-485c-a7c6-526ffb20af02/resolve/main/training_args.bin
6.84 kB
- Xet hash:
- 3a70c095028256605386d2514fd587aa4c8362e6ce8c3597b716e90f88af1808
- Size of remote file:
- 6.84 kB
- SHA256:
- 3cdc446d5c54d88751fe989d8c983c0fe39fa49112edf0817e87522a77908271
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