Instructions to use gubartz/sparse_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gubartz/sparse_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="gubartz/sparse_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("gubartz/sparse_model") model = AutoModelForMaskedLM.from_pretrained("gubartz/sparse_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download generation_config.json from gubartz/sparse_model: direct link, hf CLI and curl.
- Browser
- Download file 90 Bytes
-
https://huggingface.co/gubartz/sparse_model/resolve/refs%2Fpr%2F1/generation_config.json
- Command line
-
hf download hf://gubartz/sparse_model@refs/pr/1/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/gubartz/sparse_model/resolve/refs%2Fpr%2F1/generation_config.json
90 Bytes
| { | |
| "_from_model_config": true, | |
| "pad_token_id": 0, | |
| "transformers_version": "4.30.2" | |
| } | |