Instructions to use lamm-mit/x-lora-gemma-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lamm-mit/x-lora-gemma-7b with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lamm-mit/x-lora-gemma-7b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "base_model_id": "google/gemma-7b-it", | |
| "hidden_size": 3072, | |
| "adapters": { | |
| "adapter_1": "lamm-mit/x-lora-gemma-7b/adapter_1", | |
| "adapter_2": "lamm-mit/x-lora-gemma-7b/adapter_2", | |
| "adapter_3": "lamm-mit/x-lora-gemma-7b/adapter_3", | |
| "adapter_4": "lamm-mit/x-lora-gemma-7b/adapter_4" | |
| }, | |
| "enable_softmax": true, | |
| "enable_softmax_topk": false, | |
| "layerwise_scalings": true, | |
| "xlora_depth": 2, | |
| "xlora_size": 2048, | |
| "enable_relu_and_dropout": true, | |
| "use_bias": true, | |
| "xlora_dropout_p": 0.2, | |
| "use_trainable_adapters": false, | |
| "softmax_temperature": 1, | |
| "top_k_lora": null, | |
| "scaling_pass_value": 0, | |
| "global_scaling_weight": 1 | |
| } |