Instructions to use cimol/a799937b-9418-4d0f-927f-7ec8073d086a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/a799937b-9418-4d0f-927f-7ec8073d086a with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-3.2-1B") model = PeftModel.from_pretrained(base_model, "cimol/a799937b-9418-4d0f-927f-7ec8073d086a") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from cimol/a799937b-9418-4d0f-927f-7ec8073d086a: direct link, hf CLI and curl.
- Browser
- Download file 7.03 kB
-
https://huggingface.co/cimol/a799937b-9418-4d0f-927f-7ec8073d086a/resolve/main/training_args.bin
- Command line
-
hf download hf://cimol/a799937b-9418-4d0f-927f-7ec8073d086a/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cimol/a799937b-9418-4d0f-927f-7ec8073d086a/resolve/main/training_args.bin
7.03 kB
- Xet hash:
- f48ef5f9f00ed981dbd3ed6e2356a70cda1e4d7ec3e59a78174765d7ac2335ea
- Size of remote file:
- 7.03 kB
- SHA256:
- bcd97e504418567c0fc41b41c0f5adb759c2295d0a84870db544d8d7b6a86be8
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