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:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("chrismontes/Dog-LoRA", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use chrismontes/Dog-LoRA with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for chrismontes/Dog-LoRA to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for chrismontes/Dog-LoRA to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for chrismontes/Dog-LoRA to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="chrismontes/Dog-LoRA", max_seq_length=2048, )
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.
Inference Providers NEW
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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