Instructions to use dewdev/Doge-60M-Instruct-ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dewdev/Doge-60M-Instruct-ONNX with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="dewdev/Doge-60M-Instruct-ONNX", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("dewdev/Doge-60M-Instruct-ONNX", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("dewdev/Doge-60M-Instruct-ONNX", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e052360e64191a9eee438eccfa501fb7f6bbdc515d9481dd3aadf0a72fa87c40
- Size of remote file:
- 218 MB
- SHA256:
- 2d30a2a446050f4e9c26bb833e260e5479937577b280c16d1e39f8ce4e66aba1
路
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