Instructions to use underactuated/opt-350m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use underactuated/opt-350m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="underactuated/opt-350m")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("underactuated/opt-350m") model = AutoModel.from_pretrained("underactuated/opt-350m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from underactuated/opt-350m: direct link, hf CLI and curl.
- Browser
- Download file 743 Bytes
-
https://huggingface.co/underactuated/opt-350m/resolve/main/config.json
- Command line
-
hf download hf://underactuated/opt-350m/config.json
-
curl -L -o config.json https://huggingface.co/underactuated/opt-350m/resolve/main/config.json
743 Bytes
| { | |
| "_name_or_path": "facebook/opt-350m", | |
| "_remove_final_layer_norm": false, | |
| "activation_dropout": 0.0, | |
| "activation_function": "relu", | |
| "architectures": [ | |
| "OPTModel" | |
| ], | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 2, | |
| "do_layer_norm_before": false, | |
| "dropout": 0.1, | |
| "enable_bias": true, | |
| "eos_token_id": 2, | |
| "ffn_dim": 4096, | |
| "hidden_size": 1024, | |
| "init_std": 0.02, | |
| "layer_norm_elementwise_affine": true, | |
| "layerdrop": 0.0, | |
| "max_position_embeddings": 2048, | |
| "model_type": "opt", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "pad_token_id": 1, | |
| "prefix": "</s>", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.25.1", | |
| "use_cache": true, | |
| "vocab_size": 50272, | |
| "word_embed_proj_dim": 512 | |
| } | |