Instructions to use lew96123/deepmind_code_contests_adaptor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lew96123/deepmind_code_contests_adaptor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="lew96123/deepmind_code_contests_adaptor", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("lew96123/deepmind_code_contests_adaptor", trust_remote_code=True) model = AutoModel.from_pretrained("lew96123/deepmind_code_contests_adaptor", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download tokenizer.json from lew96123/deepmind_code_contests_adaptor: direct link, hf CLI and curl.
- Browser
- Download file 15.5 MB
-
https://huggingface.co/lew96123/deepmind_code_contests_adaptor/resolve/main/tokenizer.json
- Command line
-
hf download hf://lew96123/deepmind_code_contests_adaptor/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/lew96123/deepmind_code_contests_adaptor/resolve/main/tokenizer.json
15.5 MB
- Xet hash:
- fdc227ae825ce0d40a926036729f89230348d134f958f61d3e6b05591d49bf4b
- Size of remote file:
- 15.5 MB
- SHA256:
- d27bf952925b6e14ec92a0a31758494613e50dd289b4496b49db0c015a98dae7
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