Instructions to use kumarme072/mytoken_model_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kumarme072/mytoken_model_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="kumarme072/mytoken_model_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("kumarme072/mytoken_model_1") model = AutoModelForMaskedLM.from_pretrained("kumarme072/mytoken_model_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from kumarme072/mytoken_model_1: direct link, hf CLI and curl.
- Browser
- Download file 13.6 MB
-
https://huggingface.co/kumarme072/mytoken_model_1/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://kumarme072/mytoken_model_1/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/kumarme072/mytoken_model_1/resolve/main/tokenizer_config.json
13.6 MB
- Xet hash:
- fc14b5d2023c8f170b4a09055f36cdd780e6c443f2f93ece3df03bc31a2edc99
- Size of remote file:
- 13.6 MB
- SHA256:
- 51ddf6ff33e2625dedfaff45b8aaf028d75b37d0c7c0ca8d83260462d2a44681
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