70 lines
2.1 KiB
Markdown
70 lines
2.1 KiB
Markdown
---
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library_name: transformers
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tags:
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- meta-math
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- code
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- instruct
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- Zephyr-7B-Alpha
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datasets:
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- meta-math/MetaMathQA
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base_model: HuggingFaceH4/zephyr-7b-alpha
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license: apache-2.0
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---
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### Finetuning Overview:
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**Model Used:** HuggingFaceH4/zephyr-7b-alpha
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**Dataset:** meta-math/MetaMathQA
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#### Dataset Insights:
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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.
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#### Finetuning Details:
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Using [MonsterAPI](https://monsterapi.ai)'s [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm), this finetuning:
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- Was conducted with efficiency and cost-effectiveness in mind.
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- Completed in a total duration of 10.9 hours for 0.5 epoch using an A6000 48GB GPU.
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- Costed `$22.01` for the entire finetuning process.
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#### Hyperparameters & Additional Details:
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- **Epochs:** 0.5
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- **Total Finetuning Cost:** $22.01
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- **Model Path:** HuggingFaceH4/zephyr-7b-alpha
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- **Learning Rate:** 0.0001
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- **Data Split:** 95% train 5% validation
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- **Gradient Accumulation Steps:** 4
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---
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Prompt Structure
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```
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Below is an instruction that describes a task. Write a response that appropriately completes the request.
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###Instruction:[query]
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###Response:[response]
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```
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---
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### Training loss:
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---
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### Benchmark Results:
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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.
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---
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license: apache-2.0
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