Instructions to use MiBo/RepML with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MiBo/RepML with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MiBo/RepML")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MiBo/RepML") model = AutoModelForSequenceClassification.from_pretrained("MiBo/RepML", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from MiBo/RepML: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/MiBo/RepML/resolve/main/model.safetensors
- Command line
-
hf download hf://MiBo/RepML/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/MiBo/RepML/resolve/main/model.safetensors
443 MB
- Xet hash:
- af670cb8d4067af2bd9a961eb0f74851f5b5a06a6967e6deda187248841490e9
- Size of remote file:
- 443 MB
- SHA256:
- 69b07bec0c649862f0dbba57cfdee78f1c3d5a2d02d79f5aafb6f242d49fc0f4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.