Instructions to use pcuenq/tiny-gemma-test2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pcuenq/tiny-gemma-test2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="pcuenq/tiny-gemma-test2")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("pcuenq/tiny-gemma-test2") model = AutoModel.from_pretrained("pcuenq/tiny-gemma-test2") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "GemmaModel" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 2, | |
| "eos_token_id": 1, | |
| "head_dim": 256, | |
| "hidden_act": "gelu_pytorch_tanh", | |
| "hidden_activation": "gelu_pytorch_tanh", | |
| "hidden_size": 512, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 24576, | |
| "max_position_embeddings": 8192, | |
| "model_type": "gemma", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 1, | |
| "num_key_value_heads": 16, | |
| "pad_token_id": 0, | |
| "rms_norm_eps": 1e-06, | |
| "rope_theta": 10000.0, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.40.0.dev0", | |
| "use_cache": true, | |
| "vocab_size": 512 | |
| } | |