Instructions to use Jumpr/HF_model_ci_test-AutoModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jumpr/HF_model_ci_test-AutoModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Jumpr/HF_model_ci_test-AutoModel", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Jumpr/HF_model_ci_test-AutoModel", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "LightningTransformerModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_lightningtransformer.LightningTransformerModelConfig", | |
| "AutoModel": "modeling_lightningtransformer.LightningTransformerModel", | |
| "AutoModelForCausalLM": "modeling_lightningtransformer.LightningTransformerModelForCausalLM" | |
| }, | |
| "cfg": { | |
| "batch_size": 1, | |
| "block_num": 1, | |
| "embed_dims": 4, | |
| "head_size": 4, | |
| "iterations": 10, | |
| "lr": 0.00025, | |
| "num_heads": 1, | |
| "seq_len": 1, | |
| "use_liger": false, | |
| "vocab_size": 49152 | |
| }, | |
| "dtype": "float32", | |
| "model_type": "lightning_transformer", | |
| "num_hidden_layers": 4, | |
| "transformers_version": "5.12.1" | |
| } | |