Instructions to use hkondle/CapstoneMainModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hkondle/CapstoneMainModel with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" 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("image-to-text", model="hkondle/CapstoneMainModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("hkondle/CapstoneMainModel") model = AutoModelForMultimodalLM.from_pretrained("hkondle/CapstoneMainModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload in-memory good checkpoint
Browse files- tokenizer_config.json +1 -1
tokenizer_config.json
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@@ -3,7 +3,7 @@
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"is_local":
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"mask_token": "[MASK]",
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"model_input_names": [
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"input_ids",
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"is_local": true,
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"mask_token": "[MASK]",
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"model_input_names": [
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"input_ids",
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