Instructions to use SmallDoge/Doge-60M-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SmallDoge/Doge-60M-Instruct with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="SmallDoge/Doge-60M-Instruct", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SmallDoge/Doge-60M-Instruct", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("SmallDoge/Doge-60M-Instruct", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from SmallDoge/Doge-60M-Instruct: direct link, hf CLI and curl.
- Browser
- Download file 3.67 MB
-
https://huggingface.co/SmallDoge/Doge-60M-Instruct/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://SmallDoge/Doge-60M-Instruct@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/SmallDoge/Doge-60M-Instruct/resolve/refs%2Fpr%2F1/tokenizer.json
3.67 MB
File too large to display, you can check the raw version instead.