Instructions to use radna/mini_intern_chat_triton with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use radna/mini_intern_chat_triton with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="radna/mini_intern_chat_triton", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("radna/mini_intern_chat_triton", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3c196ddbb2296d64ea367e00ff7c7737de67377a23750affdfe94492fec9a4e7
- Size of remote file:
- 3.34 GB
- SHA256:
- 0191d413ddd6d67b2f2a5c626c5cf957ce6760d4a7a4848b06609072a600a20e
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