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Model: monsterapi/zephyr-7b-alpha_metamathqa
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags:
+- meta-math
+- code
+- instruct
+- Zephyr-7B-Alpha
+datasets:
+- meta-math/MetaMathQA
+base_model: HuggingFaceH4/zephyr-7b-alpha
+license: apache-2.0
+---
+
+### 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](https://monsterapi.ai)'s [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm), 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 `$22.01` for 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:
+![training loss](zephyr-mmqa-1.png "Training loss")
+
+
+
+---
+### Benchmark Results:
+
+![GSM8K Accuracy ](benchmark.png "GSM8K Accuracy")
+
+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