Instructions to use lisabdunlap/Llama-3.1-8B-Instruct-unsloth-bnb-4bit-r128-e3-lr0.0002-new with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lisabdunlap/Llama-3.1-8B-Instruct-unsloth-bnb-4bit-r128-e3-lr0.0002-new with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lisabdunlap/Llama-3.1-8B-Instruct-unsloth-bnb-4bit-r128-e3-lr0.0002-new", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use lisabdunlap/Llama-3.1-8B-Instruct-unsloth-bnb-4bit-r128-e3-lr0.0002-new 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 lisabdunlap/Llama-3.1-8B-Instruct-unsloth-bnb-4bit-r128-e3-lr0.0002-new 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 lisabdunlap/Llama-3.1-8B-Instruct-unsloth-bnb-4bit-r128-e3-lr0.0002-new to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for lisabdunlap/Llama-3.1-8B-Instruct-unsloth-bnb-4bit-r128-e3-lr0.0002-new to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="lisabdunlap/Llama-3.1-8B-Instruct-unsloth-bnb-4bit-r128-e3-lr0.0002-new", max_seq_length=2048, )
- Xet hash:
- eb6c31cfd75863c43f7c90b67b902e5c020e600e4d1ce18259a9e02d0977cabe
- Size of remote file:
- 3.44 GB
- SHA256:
- 230839387f64d0d87684580ea139a0e321d5a57229189e4e7d92111eab1fd892
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