Instructions to use bpHigh/Cross-Encoder-LLamaIndex-Demo-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bpHigh/Cross-Encoder-LLamaIndex-Demo-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bpHigh/Cross-Encoder-LLamaIndex-Demo-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bpHigh/Cross-Encoder-LLamaIndex-Demo-v2") model = AutoModelForSequenceClassification.from_pretrained("bpHigh/Cross-Encoder-LLamaIndex-Demo-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from bpHigh/Cross-Encoder-LLamaIndex-Demo-v2: direct link, hf CLI and curl.
- Browser
- Download file 712 kB
-
https://huggingface.co/bpHigh/Cross-Encoder-LLamaIndex-Demo-v2/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
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hf download hf://bpHigh/Cross-Encoder-LLamaIndex-Demo-v2@refs/pr/1/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/bpHigh/Cross-Encoder-LLamaIndex-Demo-v2/resolve/refs%2Fpr%2F1/tokenizer.json
712 kB
File too large to display, you can check the raw version instead.