Instructions to use osmzrl/fine_tuned_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use osmzrl/fine_tuned_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="osmzrl/fine_tuned_model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("osmzrl/fine_tuned_model") model = AutoModel.from_pretrained("osmzrl/fine_tuned_model") - Notebooks
- Google Colab
- Kaggle
Upload model
Browse files- config.json +1 -1
- model.safetensors +2 -2
config.json
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"_name_or_path": "./fine_tuned_model",
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"apply_residual_connection_post_layernorm": false,
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"architectures": [
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"
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"attention_dropout": 0.0,
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"attention_softmax_in_fp32": true,
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"_name_or_path": "./fine_tuned_model",
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"apply_residual_connection_post_layernorm": false,
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"architectures": [
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"BloomModel"
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],
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"attention_dropout": 0.0,
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"attention_softmax_in_fp32": true,
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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oid sha256:c6a11403913f5869c0d49a9168a8110d9c7ef83bc123c833309d54e05f4c8a4f
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size 2236888792
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