Instructions to use sjjung-june/llama3.1-8b-kowiki-instruct-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sjjung-june/llama3.1-8b-kowiki-instruct-lora with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sjjung-june/llama3.1-8b-kowiki-instruct-lora", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use sjjung-june/llama3.1-8b-kowiki-instruct-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 sjjung-june/llama3.1-8b-kowiki-instruct-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 sjjung-june/llama3.1-8b-kowiki-instruct-lora to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for sjjung-june/llama3.1-8b-kowiki-instruct-lora to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="sjjung-june/llama3.1-8b-kowiki-instruct-lora", max_seq_length=2048, )
- Xet hash:
- f6dcbe1eaf64b6daf186197eee6b29892341004b027244e82928af69e69f8ebb
- Size of remote file:
- 3.44 GB
- SHA256:
- f3cda489507479e22b259eb00d3c076d3b0bb93093fd5b8b0d537706e8ac9284
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