Instructions to use valhalla/gpt-neo-random-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use valhalla/gpt-neo-random-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="valhalla/gpt-neo-random-tiny")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("valhalla/gpt-neo-random-tiny") model = AutoModel.from_pretrained("valhalla/gpt-neo-random-tiny") - Notebooks
- Google Colab
- Kaggle
| { | |
| "activation_function": "gelu_new", | |
| "architectures": [ | |
| "GPTNeoModel" | |
| ], | |
| "attention_dropout": 0.0, | |
| "attention_layers": [ | |
| "global", | |
| "local" | |
| ], | |
| "attention_types": [ | |
| [ | |
| [ | |
| "global", | |
| "local" | |
| ], | |
| 1 | |
| ] | |
| ], | |
| "bos_token_id": 50256, | |
| "embed_dropout": 0.0, | |
| "eos_token_id": 50256, | |
| "gradient_checkpointing": false, | |
| "hidden_size": 8, | |
| "initializer_range": 0.02, | |
| "intermediate_size": null, | |
| "layer_norm_epsilon": 1e-05, | |
| "max_position_embeddings": 100, | |
| "model_type": "gpt_neo", | |
| "num_heads": 2, | |
| "num_layers": 2, | |
| "resid_dropout": 0.0, | |
| "summary_activation": null, | |
| "summary_first_dropout": 0.1, | |
| "summary_proj_to_labels": true, | |
| "summary_type": "cls_index", | |
| "summary_use_proj": true, | |
| "transformers_version": "4.5.0.dev0", | |
| "use_cache": true, | |
| "vocab_size": 50257, | |
| "window_size": 4 | |
| } | |