Text Classification
Transformers
PyTorch
Safetensors
Portuguese
llama
textual-entailment
text-embeddings-inference
Instructions to use nicholasKluge/TeenyTinyLlama-160m-FaQuAD-NLI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nicholasKluge/TeenyTinyLlama-160m-FaQuAD-NLI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nicholasKluge/TeenyTinyLlama-160m-FaQuAD-NLI")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nicholasKluge/TeenyTinyLlama-160m-FaQuAD-NLI") model = AutoModelForSequenceClassification.from_pretrained("nicholasKluge/TeenyTinyLlama-160m-FaQuAD-NLI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nicholasKluge/TeenyTinyLlama-160m-FaQuAD-NLI: direct link, hf CLI and curl.
- Browser
- Download file 551 MB
-
https://huggingface.co/nicholasKluge/TeenyTinyLlama-160m-FaQuAD-NLI/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nicholasKluge/TeenyTinyLlama-160m-FaQuAD-NLI/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nicholasKluge/TeenyTinyLlama-160m-FaQuAD-NLI/resolve/main/pytorch_model.bin
551 MB
- Xet hash:
- c03e69412489f57ed82fcf3558263e8ecb1f6a5c3920c88ba04975bcd3c0ef60
- Size of remote file:
- 551 MB
- SHA256:
- 14b2148f6e5b65a082d6fa6fd61cc43362754f7cec2ee570e122d716bf268962
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