Instructions to use dongbobo/MyAwesomeModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dongbobo/MyAwesomeModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dongbobo/MyAwesomeModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dongbobo/MyAwesomeModel") model = AutoModel.from_pretrained("dongbobo/MyAwesomeModel") - Notebooks
- Google Colab
- Kaggle
Upload model weights and config from step_1000 checkpoint
Browse files- config.json +3 -19
- pytorch_model.bin +2 -2
config.json
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{
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"num_hidden_layers": 12,
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"num_attention_heads": 12,
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"intermediate_size": 3072,
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"hidden_act": "gelu",
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"vocab_size": 32000,
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"max_position_embeddings": 2048,
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"initializer_range": 0.02,
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"layer_norm_eps": 1e-12,
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"pad_token_id": 0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"tie_word_embeddings": false,
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"special_tokens_map_file": "tokenizer_files/special_tokens/map.json",
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"torch_dtype": "float32",
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"transformers_version": "4.40.0"
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}
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{
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"model_type": "bert",
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"architectures": ["BertModel"]
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}
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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
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version https://git-lfs.github.com/spec/v1
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oid sha256:965362299a238de576a92dfdd3e32aea7a2bacc94b2c41541c8c9258b923f587
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size 23
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