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
Download config.json from Angshul/SpliNet: direct link, hf CLI and curl.
- Browser
- Download file 1.04 kB
-
https://huggingface.co/Angshul/SpliNet/resolve/main/config.json
- Command line
-
hf download hf://Angshul/SpliNet/config.json
-
curl -L -o config.json https://huggingface.co/Angshul/SpliNet/resolve/main/config.json
1.04 kB
| { | |
| "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 | |
| } | |