Instructions to use maziyaramini/llama-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maziyaramini/llama-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="maziyaramini/llama-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("maziyaramini/llama-classification") model = AutoModelForSequenceClassification.from_pretrained("maziyaramini/llama-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from maziyaramini/llama-classification: direct link, hf CLI and curl.
- Browser
- Download file 17.2 MB
-
https://huggingface.co/maziyaramini/llama-classification/resolve/main/tokenizer.json
- Command line
-
hf download hf://maziyaramini/llama-classification/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/maziyaramini/llama-classification/resolve/main/tokenizer.json
17.2 MB
- Xet hash:
- 0fe0317133d131441e446cd553bfecfda7baeff9330c9b6d795d1a4d696fe1f2
- Size of remote file:
- 17.2 MB
- SHA256:
- a9d4fd2d4afa82d8a7dadae3490fdc20b26f06e32cec78a8dc96521b4dc79038
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