Instructions to use hts98/temp1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hts98/temp1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hts98/temp1")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("hts98/temp1") model = AutoModelForSpeechSeq2Seq.from_pretrained("hts98/temp1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 30
Browse files- config.json +1 -1
- pytorch_model.bin +1 -1
- training_args.bin +1 -1
config.json
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"scale_embedding": false,
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"suppress_tokens": [],
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"torch_dtype": "float32",
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"transformers_version": "4.
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"use_cache": true,
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"use_weighted_layer_sum": false,
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"vocab_size": 51865
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"scale_embedding": false,
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"suppress_tokens": [],
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"torch_dtype": "float32",
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"transformers_version": "4.29.0.dev0",
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"use_cache": true,
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"use_weighted_layer_sum": false,
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"vocab_size": 51865
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pytorch_model.bin
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size 3055754841
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:f0706b90e2ffc19f95adf0c9c2e659f63698104d7bf2c16fa64fdc77c63ba3de
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size 3707
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