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 last-checkpoint/optimizer.pt from daniel40/db57755b-c80b-4f73-82bc-02acf0262cbc: direct link, hf CLI and curl.
- Browser
- Download file 21.5 MB
-
https://huggingface.co/daniel40/db57755b-c80b-4f73-82bc-02acf0262cbc/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://daniel40/db57755b-c80b-4f73-82bc-02acf0262cbc/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/daniel40/db57755b-c80b-4f73-82bc-02acf0262cbc/resolve/main/last-checkpoint/optimizer.pt
21.5 MB
- Xet hash:
- b0c5db27b75fc8eb376b345b659e8fa4dc80252f119464ee8a6bc1941bc99eac
- Size of remote file:
- 21.5 MB
- SHA256:
- e782f2abb5dcde5fa524ba06e00abc3eeb366b3ac81f4aca450e39c486ff5140
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