Instructions to use rayraycano/finetune-demo-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rayraycano/finetune-demo-lora with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rayraycano/finetune-demo-lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| title: Learning Rate Groups | |
| description: "Setting different learning rates by module name" | |
| ## Background | |
| Inspired by LoRA+, Axolotl allows practitioners to specify separate learning rates for each module or groups of | |
| modules in a model. | |
| ## Example | |
| ```yaml | |
| lr_groups: | |
| - name: o_proj | |
| modules: | |
| - self_attn.o_proj.weight | |
| lr: 1e-6 | |
| - name: q_proj | |
| modules: | |
| - model.layers.2.self_attn.q_proj.weight | |
| lr: 1e-5 | |
| learning_rate: 2e-5 | |
| ``` | |
| In this example, we have a default learning rate of 2e-5 across the entire model, but we have a separate learning rate | |
| of 1e-6 for all the self attention `o_proj` modules across all layers, and a learning are of 1e-5 to the 3rd layer's | |
| self attention `q_proj` module. | |