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
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Download README.md from lew96123/deepmind_code_contests_adaptor: direct link, hf CLI and curl.
- Browser
- Download file 611 Bytes
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https://huggingface.co/lew96123/deepmind_code_contests_adaptor/resolve/main/README.md
- Command line
-
hf download hf://lew96123/deepmind_code_contests_adaptor/README.md
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curl -L -o README.md https://huggingface.co/lew96123/deepmind_code_contests_adaptor/resolve/main/README.md
611 Bytes
metadata
base_model: unsloth/Phi-4-mini-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- phi3
license: apache-2.0
language:
- en
Uploaded finetuned model
- Developed by: lew96123
- License: apache-2.0
- Finetuned from model : unsloth/Phi-4-mini-instruct-unsloth-bnb-4bit
This phi3 model was trained 2x faster with Unsloth and Huggingface's TRL library.
