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)# 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) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use lew96123/deepmind_code_contests_adaptor with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for lew96123/deepmind_code_contests_adaptor to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for lew96123/deepmind_code_contests_adaptor to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for lew96123/deepmind_code_contests_adaptor to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="lew96123/deepmind_code_contests_adaptor", max_seq_length=2048, )
- Xet hash:
- fdc227ae825ce0d40a926036729f89230348d134f958f61d3e6b05591d49bf4b
- Size of remote file:
- 15.5 MB
- SHA256:
- d27bf952925b6e14ec92a0a31758494613e50dd289b4496b49db0c015a98dae7
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