Instructions to use seanmor5/tiny-random-GemmaModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seanmor5/tiny-random-GemmaModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="seanmor5/tiny-random-GemmaModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("seanmor5/tiny-random-GemmaModel") model = AutoModel.from_pretrained("seanmor5/tiny-random-GemmaModel") - Notebooks
- Google Colab
- Kaggle
File size: 664 Bytes
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"_name_or_path": "Xenova/tiny-random-GemmaForCausalLM",
"architectures": [
"GemmaModel"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 2,
"eos_token_id": 1,
"head_dim": 2,
"hidden_act": "gelu",
"hidden_size": 8,
"initializer_range": 0.02,
"intermediate_size": 16,
"max_position_embeddings": 8192,
"model_type": "gemma",
"num_attention_heads": 4,
"num_hidden_layers": 2,
"num_key_value_heads": 2,
"pad_token_id": 0,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 10000.0,
"torch_dtype": "float32",
"transformers_version": "4.38.1",
"use_cache": true,
"vocab_size": 256000
}
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