Instructions to use daniel40/db57755b-c80b-4f73-82bc-02acf0262cbc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use daniel40/db57755b-c80b-4f73-82bc-02acf0262cbc with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2-2b-it") model = PeftModel.from_pretrained(base_model, "daniel40/db57755b-c80b-4f73-82bc-02acf0262cbc") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from daniel40/db57755b-c80b-4f73-82bc-02acf0262cbc: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/daniel40/db57755b-c80b-4f73-82bc-02acf0262cbc/resolve/main/training_args.bin
- Command line
-
hf download hf://daniel40/db57755b-c80b-4f73-82bc-02acf0262cbc/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/daniel40/db57755b-c80b-4f73-82bc-02acf0262cbc/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 8ec8a2fbc45fa834609774ffbf6a65cee8b3a4b0e7b82914560734c73d20dcf8
- Size of remote file:
- 6.78 kB
- SHA256:
- 5914a6379f240f404445f69ca771fec129b34374a2b09641458bfe1c49abfe80
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