Instructions to use optimum-internal-testing/tiny_random_bert_neuronx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use optimum-internal-testing/tiny_random_bert_neuronx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="optimum-internal-testing/tiny_random_bert_neuronx")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("optimum-internal-testing/tiny_random_bert_neuronx") model = AutoModel.from_pretrained("optimum-internal-testing/tiny_random_bert_neuronx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload config.json with huggingface_hub
Browse files- config.json +1 -6
config.json
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{
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"add_cross_attention": false,
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"architectures": [
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"BertModel"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": null,
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"classifier_dropout": null,
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"dtype": "float32",
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"eos_token_id": null,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 32,
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"initializer_range": 0.02,
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"intermediate_size": 37,
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"is_decoder": false,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_hidden_layers": 5,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"tie_word_embeddings": true,
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"torchscript": true,
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"transformers_version": "
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"type_vocab_size": 16,
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"use_cache": true,
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"vocab_size": 1124
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{
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"architectures": [
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"BertModel"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"dtype": "float32",
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 32,
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"initializer_range": 0.02,
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"intermediate_size": 37,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_hidden_layers": 5,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"torchscript": true,
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"transformers_version": "4.57.6",
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"type_vocab_size": 16,
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"use_cache": true,
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"vocab_size": 1124
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