Text Classification
PEFT
PyTorch
Safetensors
English
regression
story-point-estimation
software-engineering
Eval Results (legacy)
Instructions to use DEVCamiloSepulveda/222-LLAMA3SP-talenddataquality-appceleratorstudio with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DEVCamiloSepulveda/222-LLAMA3SP-talenddataquality-appceleratorstudio with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("meta-llama/Llama-3.2-1B") model = PeftModel.from_pretrained(base_model, "DEVCamiloSepulveda/222-LLAMA3SP-talenddataquality-appceleratorstudio") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from DEVCamiloSepulveda/222-LLAMA3SP-talenddataquality-appceleratorstudio: direct link, hf CLI and curl.
- Browser
- Download file 614 kB
-
https://huggingface.co/DEVCamiloSepulveda/222-LLAMA3SP-talenddataquality-appceleratorstudio/resolve/main/tokenizer.json
- Command line
-
hf download hf://DEVCamiloSepulveda/222-LLAMA3SP-talenddataquality-appceleratorstudio/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/DEVCamiloSepulveda/222-LLAMA3SP-talenddataquality-appceleratorstudio/resolve/main/tokenizer.json
614 kB
File too large to display, you can check the raw version instead.