Instructions to use hf-tiny-model-private/tiny-random-XGLMModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-XGLMModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-XGLMModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-XGLMModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-XGLMModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "tiny_models/xglm/XGLMModel", | |
| "activation_dropout": 0.1, | |
| "activation_function": "gelu", | |
| "architectures": [ | |
| "XGLMModel" | |
| ], | |
| "attention_dropout": 0.1, | |
| "attention_heads": 4, | |
| "bos_token_id": 0, | |
| "d_model": 32, | |
| "decoder_start_token_id": 2, | |
| "dropout": 0.1, | |
| "eos_token_id": 2, | |
| "ffn_dim": 37, | |
| "gradient_checkpointing": false, | |
| "init_std": 0.02, | |
| "initializer_range": 0.02, | |
| "layerdrop": 0.0, | |
| "max_position_embeddings": 512, | |
| "model_type": "xglm", | |
| "num_layers": 5, | |
| "pad_token_id": 1, | |
| "scale_embedding": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.28.0.dev0", | |
| "use_cache": true, | |
| "vocab_size": 256008 | |
| } | |