Instructions to use SmallDoge/Doge-20M-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SmallDoge/Doge-20M-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="SmallDoge/Doge-20M-Instruct", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SmallDoge/Doge-20M-Instruct", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("SmallDoge/Doge-20M-Instruct", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 70de076fa18896073beef6149fbfc8ac2a287bc510c1a6f422ca4b8538b7a952
- Size of remote file:
- 587 kB
- SHA256:
- 37f00374dea48658ee8f5d0f21895b9bc55cb0103939607c8185bfd1c6ca1f89
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.