Instructions to use cwitcate/mymodel1001 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cwitcate/mymodel1001 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="cwitcate/mymodel1001")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("cwitcate/mymodel1001") model = AutoModelForMaskedLM.from_pretrained("cwitcate/mymodel1001", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from cwitcate/mymodel1001: direct link, hf CLI and curl.
- Browser
- Download file 59 Bytes
-
https://huggingface.co/cwitcate/mymodel1001/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://cwitcate/mymodel1001/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/cwitcate/mymodel1001/resolve/main/tokenizer_config.json
59 Bytes
| {"do_lower_case": false, "max_len": 512, "init_inputs": []} |