Text Generation
PEFT
Safetensors
age-verification
explanation
lora
qlora
multilingual
africa
conversational
Instructions to use Shinzmann/gemma-explain-lora-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Shinzmann/gemma-explain-lora-v0 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, "Shinzmann/gemma-explain-lora-v0") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add W&B training curves to model card
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docs: add Links + tags; clean card
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docs: real eval report
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fix: real eval metrics + converged train loss
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docs: real eval results (served endpoint), supersede broken-harness 0.0
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docs: comprehensive model card (v0 results, methodology, safety)
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Upload README.md with huggingface_hub
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Upload folder using huggingface_hub
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initial commit
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