Instructions to use hf-internal-testing/tiny-random-GPTBigCodeForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-GPTBigCodeForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-GPTBigCodeForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPTBigCodeForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-GPTBigCodeForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
Update tiny models for GPTBigCodeForSequenceClassification
#12
by hf-transformers-bot - opened
- config.json +1 -1
- pytorch_model.bin +1 -1
config.json
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@@ -25,7 +25,7 @@
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"scale_attn_by_inverse_layer_idx": false,
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"scale_attn_weights": true,
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"torch_dtype": "float32",
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"transformers_version": "4.
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"type_vocab_size": 16,
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"use_cache": true,
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"vocab_size": 1024
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"scale_attn_by_inverse_layer_idx": false,
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"scale_attn_weights": true,
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"torch_dtype": "float32",
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"transformers_version": "4.29.0.dev0",
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"type_vocab_size": 16,
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"use_cache": true,
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"vocab_size": 1024
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 320820
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version https://git-lfs.github.com/spec/v1
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oid sha256:b2cfdff981a51685249ebd7f1e43162535e87db414b776ddf5a285bfb79939ce
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size 320820
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