Instructions to use cimol/0ea943d2-149b-4d41-904a-afbf75ce1a20 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/0ea943d2-149b-4d41-904a-afbf75ce1a20 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-Coder-7B") model = PeftModel.from_pretrained(base_model, "cimol/0ea943d2-149b-4d41-904a-afbf75ce1a20") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/training_args.bin from cimol/0ea943d2-149b-4d41-904a-afbf75ce1a20: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/cimol/0ea943d2-149b-4d41-904a-afbf75ce1a20/resolve/main/last-checkpoint/training_args.bin
- Command line
-
hf download hf://cimol/0ea943d2-149b-4d41-904a-afbf75ce1a20/last-checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cimol/0ea943d2-149b-4d41-904a-afbf75ce1a20/resolve/main/last-checkpoint/training_args.bin
6.84 kB
- Xet hash:
- 5705774ce775cea57f861e72d0bc6bc9affd6857c18a6bfc9b705b4036d1aa7c
- Size of remote file:
- 6.84 kB
- SHA256:
- 6e6b09d721f522de793f2d95824a7d18e3a1b733a6ff8d429cee2d816c32a4f3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.