Instructions to use togethercomputer/m2-bert-80M-8k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use togethercomputer/m2-bert-80M-8k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="togethercomputer/m2-bert-80M-8k", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("togethercomputer/m2-bert-80M-8k", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Dan Fu commited on
Commit ·
7f3a69e
1
Parent(s): 624c2de
Initial model
Browse files- config.json +4 -0
- config.yaml +38 -0
- model.pt +3 -0
- version.txt +1 -0
config.json
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{
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"model_type": "m2_bert"
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}
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config.yaml
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# Note that some of the fields in this template haven't been filled in yet.
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# Please resolve any `null` fields before launching!
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precision: amp_bf16
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max_seq_len: 8192
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# Tokenizer for dataset creation
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tokenizer_name: bert-base-uncased
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# Base model config
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model:
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name: bert
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pretrained_model_name: ${tokenizer_name}
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tokenizer_name: ${tokenizer_name}
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model_config:
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num_attention_heads: 12
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num_hidden_layers: 12
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attention_probs_dropout_prob: 0.0
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max_position_embeddings: 8192
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monarch_mixer_sequence_mixing: True
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long_conv_l_max: 8192
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long_conv_kernel_learning_rate: 1e-3
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hyena_lr_pos_emb: 1e-5
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hyena_w: 10
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hyena_wd: 0.1
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hyena_emb_dim: 5
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hyena_filter_order: 128
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hyena_training_additions: False
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bidirectional: true
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residual_long_conv: true
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use_glu_mlp: True
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use_monarch_mlp: True
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monarch_mlp_nblocks: 4
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use_positional_encodings: True
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model.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:e7155c21c85cc6a169064a007fad01d2a38b8e1e3d7c98b300f2b2dfaace01b4
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size 1050873856
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version.txt
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