Instructions to use Angshul/SpliNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Angshul/SpliNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Angshul/SpliNet", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Angshul/SpliNet", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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4ec5e47 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | {
"architectures": [
"SpliNetForMaskedLM"
],
"auto_map": {
"AutoConfig": "configuration_splinet.SpliNetConfig",
"AutoModel": "modeling_splinet.SpliNetModel",
"AutoModelForMaskedLM": "modeling_splinet.SpliNetForMaskedLM"
},
"bos_token_id": 1,
"dtype": "float32",
"eos_token_id": 2,
"hidden_act": "gelu_new",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "splinet",
"num_hidden_layers": 12,
"pad_token_id": 3,
"splinet_num_heads": 12,
"splinet_order": 2,
"splinet_radius": 16,
"splinet_sidedness": "single",
"tie_word_embeddings": true,
"tokenizer_class": "SpliNetTokenizer",
"tokenizer_sha256": "119ec6b2af9cbbc56f297bd606b69f79f6e3130a34ee56122ee813a58d15bb9d",
"tpu_short_seq_length": 512,
"training_tokens": 2000000000,
"transformers_version": "5.16.1",
"type_vocab_size": 4,
"use_tpu_fourier_optimizations": false,
"vocab_size": 32000
}
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