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
Update README.md
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README.md
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## Proof
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- HumanEval subset: first 8 tasks
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- Executable pass count before: 5/8
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- Executable pass count after: 7/8
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- Heuristic score before: 0.7688
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## Proof
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- HumanEval subset: first 8 tasks (ran out of GPU hours)
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- Executable pass count before: 5/8
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- Executable pass count after: 7/8
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- Heuristic score before: 0.7688
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