Instructions to use codesagar/prompt-guard-classification-v7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codesagar/prompt-guard-classification-v7 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("codesagar/prompt-guard-classification-v7", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
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Download README.md from codesagar/prompt-guard-classification-v7: direct link, hf CLI and curl.
- Browser
- Download file 580 Bytes
-
https://huggingface.co/codesagar/prompt-guard-classification-v7/resolve/main/README.md
- Command line
-
hf download hf://codesagar/prompt-guard-classification-v7/README.md
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curl -L -o README.md https://huggingface.co/codesagar/prompt-guard-classification-v7/resolve/main/README.md
580 Bytes
metadata
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: unsloth/mistral-7b-bnb-4bit
Uploaded model
- Developed by: codesagar
- License: apache-2.0
- Finetuned from model : unsloth/mistral-7b-bnb-4bit
This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.
