Instructions to use ydl233/t5_small_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ydl233/t5_small_model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ydl233/t5_small_model") model = AutoModelForSeq2SeqLM.from_pretrained("ydl233/t5_small_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from ydl233/t5_small_model: direct link, hf CLI and curl.
- Browser
- Download file 478 Bytes
-
https://huggingface.co/ydl233/t5_small_model/resolve/main/config.json
- Command line
-
hf download hf://ydl233/t5_small_model/config.json
-
curl -L -o config.json https://huggingface.co/ydl233/t5_small_model/resolve/main/config.json
478 Bytes
| { | |
| "architectures": [ | |
| "T5ForConditionalGeneration" | |
| ], | |
| "d_ff": 2048, | |
| "d_kv": 64, | |
| "d_model": 512, | |
| "decoder_start_token_id": 101, | |
| "dropout_rate": 0.1, | |
| "initializer_factor": 1.0, | |
| "is_encoder_decoder": true, | |
| "layer_norm_epsilon": 1e-06, | |
| "model_type": "t5", | |
| "n_positions": 512, | |
| "num_heads": 8, | |
| "num_layers": 6, | |
| "pad_token_id": 0, | |
| "relative_attention_num_buckets": 32, | |
| "tokenizer_class": "BertTokenizer", | |
| "vocab_size": 21228 | |
| } |