Instructions to use crochereau/lobster-mgm-11M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use crochereau/lobster-mgm-11M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="crochereau/lobster-mgm-11M")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("crochereau/lobster-mgm-11M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 918 Bytes
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"add_embedding_noise": false,
"architectures": [
"LMBaseForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"conditioning_type": null,
"emb_layer_norm_before": null,
"has_conditioning": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 384,
"initializer_range": 0.02,
"intermediate_bias": false,
"intermediate_size": 1024,
"key_bias": false,
"layer_norm_eps": 1e-12,
"mask_token_id": 7,
"max_position_embeddings": 512,
"model_type": "pmlm",
"n_concepts": 0,
"noise_mean": 0.0,
"noise_std_max": 0.25,
"noise_std_min": 0.1,
"num_attention_heads": 12,
"num_hidden_layers": 8,
"pad_token_id": 9,
"position_embedding_type": "rotary",
"query_bias": false,
"token_dropout": false,
"torch_dtype": "float32",
"transformers_version": "4.46.3",
"use_cache": true,
"value_bias": false,
"vocab_size": 68
}
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