Text Classification
PEFT
PyTorch
Safetensors
English
regression
story-point-estimation
software-engineering
Eval Results (legacy)
Instructions to use DEVCamiloSepulveda/333-Qwen3SP-appceleratorstudio-mule with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DEVCamiloSepulveda/333-Qwen3SP-appceleratorstudio-mule with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("Qwen/Qwen3-1.7B") model = PeftModel.from_pretrained(base_model, "DEVCamiloSepulveda/333-Qwen3SP-appceleratorstudio-mule") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from DEVCamiloSepulveda/333-Qwen3SP-appceleratorstudio-mule: direct link, hf CLI and curl.
- Browser
- Download file 1.99 GB
-
https://huggingface.co/DEVCamiloSepulveda/333-Qwen3SP-appceleratorstudio-mule/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://DEVCamiloSepulveda/333-Qwen3SP-appceleratorstudio-mule/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/DEVCamiloSepulveda/333-Qwen3SP-appceleratorstudio-mule/resolve/main/pytorch_model.bin
1.99 GB
- Xet hash:
- a599186a1b1012b1bbfc43e8786797d3f5f55e35869ca4d12de0782463d73c86
- Size of remote file:
- 1.99 GB
- SHA256:
- 1df9284fce598726dc93708f8a74d425136608cfe7523ce23f87d129fd39b369
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