Instructions to use Devenvaruv/Grad_Only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Devenvaruv/Grad_Only with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="Devenvaruv/Grad_Only")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Devenvaruv/Grad_Only") model = AutoModelForQuestionAnswering.from_pretrained("Devenvaruv/Grad_Only", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from Devenvaruv/Grad_Only: direct link, hf CLI and curl.
- Browser
- Download file 544 Bytes
-
https://huggingface.co/Devenvaruv/Grad_Only/resolve/main/config.json
- Command line
-
hf download hf://Devenvaruv/Grad_Only/config.json
-
curl -L -o config.json https://huggingface.co/Devenvaruv/Grad_Only/resolve/main/config.json
544 Bytes
| { | |
| "_name_or_path": "distilbert/distilbert-base-uncased", | |
| "activation": "gelu", | |
| "architectures": [ | |
| "DistilBertForQuestionAnswering" | |
| ], | |
| "attention_dropout": 0.1, | |
| "dim": 768, | |
| "dropout": 0.1, | |
| "hidden_dim": 3072, | |
| "initializer_range": 0.02, | |
| "max_position_embeddings": 512, | |
| "model_type": "distilbert", | |
| "n_heads": 12, | |
| "n_layers": 6, | |
| "pad_token_id": 0, | |
| "qa_dropout": 0.1, | |
| "seq_classif_dropout": 0.2, | |
| "sinusoidal_pos_embds": false, | |
| "tie_weights_": true, | |
| "transformers_version": "4.40.1", | |
| "vocab_size": 30522 | |
| } | |