Instructions to use togethercomputer/m2-bert-80M-32k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use togethercomputer/m2-bert-80M-32k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="togethercomputer/m2-bert-80M-32k", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("togethercomputer/m2-bert-80M-32k", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +1 -1
config.json
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"long_conv_kernel_learning_rate": 0.001,
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"long_conv_l_max": 32768,
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"max_position_embeddings": 32768,
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"model_type": "
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"monarch_mlp_nblocks": 4,
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"long_conv_kernel_learning_rate": 0.001,
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"long_conv_l_max": 32768,
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"max_position_embeddings": 32768,
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"model_type": "m2_bert",
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"monarch_mlp_nblocks": 4,
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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