Instructions to use SmallDoge/Doge-20M-MoE-Instruct-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SmallDoge/Doge-20M-MoE-Instruct-SFT 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-20M-MoE-Instruct-SFT", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SmallDoge/Doge-20M-MoE-Instruct-SFT", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("SmallDoge/Doge-20M-MoE-Instruct-SFT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from SmallDoge/Doge-20M-MoE-Instruct-SFT: direct link, hf CLI and curl.
- Browser
- Download file 2.36 MB
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https://huggingface.co/SmallDoge/Doge-20M-MoE-Instruct-SFT/resolve/main/tokenizer.json
- Command line
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hf download hf://SmallDoge/Doge-20M-MoE-Instruct-SFT/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/SmallDoge/Doge-20M-MoE-Instruct-SFT/resolve/main/tokenizer.json
2.36 MB
File too large to display, you can check the raw version instead.