Instructions to use q-future/q-instruct-mplug-owl2-1031 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use q-future/q-instruct-mplug-owl2-1031 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="q-future/q-instruct-mplug-owl2-1031", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("q-future/q-instruct-mplug-owl2-1031", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f6d4eb01c09ea19f95d14d1a6163a3443e299edd4b53070f81199e73efd39ed1
- Size of remote file:
- 5.88 kB
- SHA256:
- 80a0e6508b960234f9c1d5ae054c7613250465c0c1faa7544b3cb5b55c06ea0d
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