Text Classification
PEFT
PyTorch
Safetensors
English
regression
story-point-estimation
software-engineering
Eval Results (legacy)
Instructions to use DEVCamiloSepulveda/1-DeepSeekR1SP-appceleratorstudio with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DEVCamiloSepulveda/1-DeepSeekR1SP-appceleratorstudio with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B") model = PeftModel.from_pretrained(base_model, "DEVCamiloSepulveda/1-DeepSeekR1SP-appceleratorstudio") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from DEVCamiloSepulveda/1-DeepSeekR1SP-appceleratorstudio: direct link, hf CLI and curl.
- Browser
- Download file 1.62 GB
-
https://huggingface.co/DEVCamiloSepulveda/1-DeepSeekR1SP-appceleratorstudio/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://DEVCamiloSepulveda/1-DeepSeekR1SP-appceleratorstudio/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/DEVCamiloSepulveda/1-DeepSeekR1SP-appceleratorstudio/resolve/main/pytorch_model.bin
1.62 GB
- Xet hash:
- fa51609c53b66086df58e5c869c5e8609b43b263bf3dc031e36e36a1e5373ac8
- Size of remote file:
- 1.62 GB
- SHA256:
- 7ad5982b63e837b96ee21e9399355bb632b99c59b213590eccc96d73252ce6f3
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