Instructions to use chrismontes/Dog-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chrismontes/Dog-LoRA with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("chrismontes/Dog-LoRA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Here are the LoRA adapters produced by using my text data with Unsloth's training code, trained for 1000 steps. The base model is the Llama-3-8b-bnb-4bit. If you would like to see the modifications I made to Unsloth's script to make it more concise and adaptable to my own data format, you can find the modified script here
Uploaded model
- Developed by: chrismontes
- License: apache-2.0
- Finetuned from model : unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
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Model tree for chrismontes/Dog-LoRA
Base model
meta-llama/Meta-Llama-3-8B Quantized
unsloth/llama-3-8b-bnb-4bit