Finetuning Overview:
Model Used: HuggingFaceH4/zephyr-7b-alpha
Dataset: meta-math/MetaMathQA
Dataset Insights:
The MetaMathQA dataset is a newly created dataset specifically designed for enhancing the mathematical reasoning capabilities of large language models (LLMs). It is built by bootstrapping mathematical questions and rewriting them from multiple perspectives, providing a comprehensive and challenging environment for LLMs to develop and refine their mathematical problem-solving skills.
Finetuning Details:
Using MonsterAPI's LLM finetuner, this finetuning:
- Was conducted with efficiency and cost-effectiveness in mind.
- Completed in a total duration of 10.9 hours for 0.5 epoch using an A6000 48GB GPU.
- Costed
$22.01for the entire finetuning process.
Hyperparameters & Additional Details:
- Epochs: 0.5
- Total Finetuning Cost: $22.01
- Model Path: HuggingFaceH4/zephyr-7b-alpha
- Learning Rate: 0.0001
- Data Split: 95% train 5% validation
- Gradient Accumulation Steps: 4
Prompt Structure
Below is an instruction that describes a task. Write a response that appropriately completes the request.
###Instruction:[query]
###Response:[response]
Training loss:
Benchmark Results:
GSM8K is a dataset of 8.5K high quality linguistically diverse grade school math word problems, These problems take between 2 and 8 steps to solve, and solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the final answer. A bright middle school student should be able to solve every problem. Its a industry wide used benchmark for testing an LLM for for multi-step mathematical reasoning.
license: apache-2.0