Instructions to use Hebisuke/Llama-3.2-1B-Instruct_lora16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hebisuke/Llama-3.2-1B-Instruct_lora16 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Hebisuke/Llama-3.2-1B-Instruct_lora16", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use Hebisuke/Llama-3.2-1B-Instruct_lora16 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 Hebisuke/Llama-3.2-1B-Instruct_lora16 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 Hebisuke/Llama-3.2-1B-Instruct_lora16 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Hebisuke/Llama-3.2-1B-Instruct_lora16 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Hebisuke/Llama-3.2-1B-Instruct_lora16", max_seq_length=2048, )
- Xet hash:
- c20623c22cd0ab35c51ab5bfdff3480f69ba647b539fe63867066f830b1840a3
- Size of remote file:
- 17.2 MB
- SHA256:
- 1412329e6e5d24736da8086ae981a852dd43944d24603fd90ed939e2706ad87f
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