Text Classification
Transformers
Safetensors
llama
Generated from Trainer
trl
reward-trainer
text-embeddings-inference
4-bit precision
bitsandbytes
Instructions to use shirwu/debug with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shirwu/debug with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="shirwu/debug")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shirwu/debug") model = AutoModelForSequenceClassification.from_pretrained("shirwu/debug", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 14bc40ce771b3776f214ec2415ece3ed6bf31a2f34360ade219c665cada8bb3a
- Size of remote file:
- 1.07 GB
- SHA256:
- 63fbbabf5f92c3cd484494e08b7fd95b0d32ba3d7b6ba5e46f53a50b51eea440
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.