Instructions to use mahbubhimel/BNLLaMA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mahbubhimel/BNLLaMA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mahbubhimel/BNLLaMA")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mahbubhimel/BNLLaMA") model = AutoModelForSequenceClassification.from_pretrained("mahbubhimel/BNLLaMA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b107f87ddcda6ba510c5e73bfe5fc88abdab99d8805c2f91402e2814e7db3871
- Size of remote file:
- 29.4 MB
- SHA256:
- 365a0c265893f9ab04f4094f07a13a399b024ab4fc75aadecd0133570d543a6f
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