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
File size: 676 Bytes
5dc1237 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | {
"_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
}
|