Instructions to use EmTpro01/CodeLlama-7b-java-peft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EmTpro01/CodeLlama-7b-java-peft with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("EmTpro01/CodeLlama-7b-java-peft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use EmTpro01/CodeLlama-7b-java-peft 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 EmTpro01/CodeLlama-7b-java-peft 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 EmTpro01/CodeLlama-7b-java-peft to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for EmTpro01/CodeLlama-7b-java-peft to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="EmTpro01/CodeLlama-7b-java-peft", max_seq_length=2048, )
- Xet hash:
- 21a4013c3babbd1d19ce2548081915de83619deca19a4251cfac88d58f2e6354
- Size of remote file:
- 160 MB
- SHA256:
- 6ab28e78fc88c1d0979f5b7c3b1460cbb58aa518ec1817ab276f5ca9dfbbc9bd
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