Text Classification
Transformers
TensorBoard
Safetensors
mistral
Generated from Trainer
text-embeddings-inference
Instructions to use xshubhamx/tiny-mistral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xshubhamx/tiny-mistral with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xshubhamx/tiny-mistral")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xshubhamx/tiny-mistral") model = AutoModelForSequenceClassification.from_pretrained("xshubhamx/tiny-mistral", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2f5c7da0aead3a36d51939d49e583864fdc0c2a896369932a6b447911b40088f
- Size of remote file:
- 1.58 GB
- SHA256:
- 2012d4f408d34f07b07b497adb0fdd6aad68929aa155a22759e6eab2d9c32050
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.