Instructions to use arabellako22/gifted_maths_lora_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use arabellako22/gifted_maths_lora_model with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-4-e2b-it-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "arabellako22/gifted_maths_lora_model") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
| license: gemma | |
| base_model: unsloth/gemma-4-E2B-it | |
| library_name: peft | |
| tags: | |
| - gemma | |
| - unsloth | |
| - lora | |
| - mathematics | |
| - latex | |
| # Gifted Maths LoRA Adapter | |
| This repository contains the separated LoRA adapter for `maths_engine`, a local gifted mathematics reasoning model. | |
| Base model: `unsloth/gemma-4-E2B-it` | |
| Training goal: | |
| - step-by-step mathematical reasoning | |
| - readable LaTeX formatting | |
| - local/offline deployment after GGUF conversion | |
| Important: | |
| - This is the adapter only. | |
| - To create a full local model, load it with the base model and merge using Unsloth `save_pretrained_merged(..., save_method="merged_16bit")`. | |
| - Do not rely on ordinary `PeftModel.merge_and_unload()` for this Gemma 4 workflow. | |