Instructions to use aaljabari/code_deobfuscation_codallama_python_php_javascript with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aaljabari/code_deobfuscation_codallama_python_php_javascript with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("aaljabari/code_deobfuscation_codallama_python_php_javascript", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use aaljabari/code_deobfuscation_codallama_python_php_javascript 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 aaljabari/code_deobfuscation_codallama_python_php_javascript 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 aaljabari/code_deobfuscation_codallama_python_php_javascript to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for aaljabari/code_deobfuscation_codallama_python_php_javascript to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="aaljabari/code_deobfuscation_codallama_python_php_javascript", max_seq_length=2048, )
- Xet hash:
- bc51aa0c226f39ed7fe35811ce4d513cfb0194875a59ff665bdafc29b55a71a4
- Size of remote file:
- 1.48 GB
- SHA256:
- 55213039acf086c2441563e64b5320bca8f909d8e2a5dddebce1778eb7f33c5f
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