Instructions to use cimol/9239fed7-15ba-4497-bf0f-d387d14ca95f with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/9239fed7-15ba-4497-bf0f-d387d14ca95f with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Meta-Llama-3.1-8B") model = PeftModel.from_pretrained(base_model, "cimol/9239fed7-15ba-4497-bf0f-d387d14ca95f") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from cimol/9239fed7-15ba-4497-bf0f-d387d14ca95f: direct link, hf CLI and curl.
- Browser
- Download file 341 MB
-
https://huggingface.co/cimol/9239fed7-15ba-4497-bf0f-d387d14ca95f/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://cimol/9239fed7-15ba-4497-bf0f-d387d14ca95f/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/cimol/9239fed7-15ba-4497-bf0f-d387d14ca95f/resolve/main/last-checkpoint/optimizer.pt
341 MB
- Xet hash:
- 6ee771a7517fe952ea073ce405efcd3a64cd2dd7c7ee4b02ff8c95538159bee0
- Size of remote file:
- 341 MB
- SHA256:
- b87542dade113d86db272037532c3c2bfd05037e4ab2fc033369020d533b2339
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