Instructions to use kristinehara/tmp_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kristinehara/tmp_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="kristinehara/tmp_trainer")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("kristinehara/tmp_trainer") model = AutoModel.from_pretrained("kristinehara/tmp_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "openai-gpt", | |
| "afn": "gelu", | |
| "architectures": [ | |
| "OpenAIGPTModel" | |
| ], | |
| "attn_pdrop": 0.1, | |
| "embd_pdrop": 0.1, | |
| "initializer_range": 0.02, | |
| "layer_norm_epsilon": 1e-05, | |
| "model_type": "openai-gpt", | |
| "n_ctx": 512, | |
| "n_embd": 768, | |
| "n_head": 12, | |
| "n_layer": 12, | |
| "n_positions": 512, | |
| "n_special": 0, | |
| "predict_special_tokens": true, | |
| "resid_pdrop": 0.1, | |
| "summary_activation": null, | |
| "summary_first_dropout": 0.1, | |
| "summary_proj_to_labels": true, | |
| "summary_type": "cls_index", | |
| "summary_use_proj": true, | |
| "task_specific_params": { | |
| "text-generation": { | |
| "do_sample": true, | |
| "max_length": 50 | |
| } | |
| }, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.29.2", | |
| "vocab_size": 40478 | |
| } | |