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 tokenizer.json from DEVCamiloSepulveda/333-Qwen3SP-appceleratorstudio-mule: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/DEVCamiloSepulveda/333-Qwen3SP-appceleratorstudio-mule/resolve/main/tokenizer.json
- Command line
-
hf download hf://DEVCamiloSepulveda/333-Qwen3SP-appceleratorstudio-mule/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/DEVCamiloSepulveda/333-Qwen3SP-appceleratorstudio-mule/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 6aec39639a0a2d1ca966356b8c2b8426a484f80ff80731f44fa8482040713bdf
- Size of remote file:
- 11.4 MB
- SHA256:
- aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
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