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: Mac M-series | |
| description: Mac M-series support | |
| Currently Axolotl on Mac is partially usable, many of the dependencies of Axolotl including Pytorch do not support MPS or have incomplete support. | |
| Current support: | |
| - [x] Support for all models | |
| - [x] Full training of models | |
| - [x] LoRA training | |
| - [x] Sample packing | |
| - [ ] FP16 and BF16 (awaiting AMP support for MPS in Pytorch) | |
| - [ ] Tri-dao's flash-attn (until it is supported use spd_attention as an alternative) | |
| - [ ] xformers | |
| - [ ] bitsandbytes (meaning no 4/8 bits loading and bnb optimizers) | |
| - [ ] qlora | |
| - [ ] DeepSpeed | |
| Untested: | |
| - FSDP | |