Instructions to use nllg/poetry-bygpt5-small-de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nllg/poetry-bygpt5-small-de with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nllg/poetry-bygpt5-small-de")# Load model directly from transformers import AutoModelWithLMHead model = AutoModelWithLMHead.from_pretrained("nllg/poetry-bygpt5-small-de", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "models/bygpt5-small/de", | |
| "architectures": [ | |
| "ByGPT5LMHeadModel" | |
| ], | |
| "d_ff": 3584, | |
| "d_kv": 64, | |
| "d_model": 1472, | |
| "decoder_start_token_id": 0, | |
| "dense_act_fn": "gelu_new", | |
| "dropout_rate": 0.1, | |
| "eos_token_id": 1, | |
| "feed_forward_proj": "gated-gelu", | |
| "gradient_checkpointing": false, | |
| "initializer_factor": 1.0, | |
| "is_decoder": true, | |
| "is_encoder_decoder": false, | |
| "is_gated_act": true, | |
| "layer_norm_epsilon": 1e-06, | |
| "model_type": "bygpt5", | |
| "num_decoder_layers": 4, | |
| "num_heads": 6, | |
| "num_layers": 4, | |
| "pad_token_id": 0, | |
| "relative_attention_max_distance": 128, | |
| "relative_attention_num_buckets": 32, | |
| "tie_word_embeddings": false, | |
| "tokenizer_class": "ByT5Tokenizer", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.20.0", | |
| "use_cache": true, | |
| "vocab_size": 384 | |
| } | |