Instructions to use verbalyze/Llama3.1_finetuned_original with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use verbalyze/Llama3.1_finetuned_original with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("verbalyze/Llama3.1_finetuned_original", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use verbalyze/Llama3.1_finetuned_original 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 verbalyze/Llama3.1_finetuned_original 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 verbalyze/Llama3.1_finetuned_original to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for verbalyze/Llama3.1_finetuned_original to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="verbalyze/Llama3.1_finetuned_original", max_seq_length=2048, )
- Xet hash:
- 2f9e968de81a037b1c1e724f0c543199a0986dc1cd18b22460c106c705ad95a2
- Size of remote file:
- 168 MB
- SHA256:
- ed25518957588902bbfd7563518a23de0977282872e675cca1070e0e1b1793f2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.