Instructions to use josephmayo/gemma-4-E4B-it-coding-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use josephmayo/gemma-4-E4B-it-coding-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-4-E4B-it") model = PeftModel.from_pretrained(base_model, "josephmayo/gemma-4-E4B-it-coding-lora") - Notebooks
- Google Colab
- Kaggle
Sanitize public training data description
Browse files
README.md
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## Training
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- Runtime: Kaggle 2x Tesla T4
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- Safe rows used: 1024
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- Steps: 200
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- LoRA: r=16, alpha=32, target_modules=`all-linear`
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## Training
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- Runtime: Kaggle 2x Tesla T4
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- Data: filtered benign coding instruction data
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- Safe rows used: 1024
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- Steps: 200
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- LoRA: r=16, alpha=32, target_modules=`all-linear`
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