Instructions to use hf-internal-testing/tiny-random-led with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-led with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-led")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-led") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-led") - Notebooks
- Google Colab
- Kaggle
File size: 953 Bytes
8de94a3 | 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 38 39 40 | {
"activation_dropout": 0.0,
"activation_function": "gelu",
"architectures": [
"LEDModel"
],
"attention_dropout": 0.1,
"attention_window": [
4,
4
],
"bos_token_id": 0,
"classifier_dropout": 0.0,
"d_model": 16,
"decoder_attention_heads": 4,
"decoder_ffn_dim": 4,
"decoder_layerdrop": 0.0,
"decoder_layers": 2,
"decoder_start_token_id": 2,
"dropout": 0.1,
"encoder_attention_heads": 4,
"encoder_ffn_dim": 4,
"encoder_layerdrop": 0.0,
"encoder_layers": 2,
"eos_token_id": 2,
"gradient_checkpointing": false,
"init_std": 0.02,
"is_encoder_decoder": true,
"max_decoder_position_embeddings": 1024,
"max_encoder_position_embeddings": 16384,
"max_position_embeddings": 32,
"model_type": "led",
"num_hidden_layers": 2,
"pad_token_id": 1,
"torch_dtype": "float32",
"transformers_version": "4.52.0.dev0",
"use_cache": true,
"vocab_size": 1000
}
|