Text Generation
PEFT
Safetensors
llama
lora
math
reasoning
adaption
word-problems
sft
conversational
Instructions to use Minutor/adaption_math_word_problem_sub_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Minutor/adaption_math_word_problem_sub_2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("togethercomputer/Meta-Llama-3.2-3B-Instruct-Reference__TOG__FT") model = PeftModel.from_pretrained(base_model, "Minutor/adaption_math_word_problem_sub_2") - Notebooks
- Google Colab
- Kaggle
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### Training Data
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The model was trained on 19,573 rows of adapted data with the following domain distribution: math (99%), language (0%), science (0%), personal-finance (0%), fitness-sports (0%), animal-nature (0%), agriculture (0%), how-to (0%), sports (0%), travel (0%), data-analysis-visualization (0%).
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Trained on the adapted dataset:
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→ [Minutor/adaption-math-word-problem-sub-2](https://huggingface.co/datasets/Minutor/adaption-math-word-problem-sub-2)
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### Training Data
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The model was trained on 19,573 rows of adapted data with the following domain distribution: math (99%), language (0%), science (0%), personal-finance (0%), fitness-sports (0%), animal-nature (0%), agriculture (0%), how-to (0%), sports (0%), travel (0%), data-analysis-visualization (0%).
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Trained on the adapted dataset:
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→ [Minutor/adaption-math-word-problem-sub-2](https://huggingface.co/datasets/Minutor/adaption-math-word-problem-sub-2)
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