shinysup/YouronMath

Model Card for Model ID

This model is from paper "Can A Gamer Train A Mathematical Reasoning Model?" which examines training a mathematical reasoning model on a single gaming GPU, namely RTX 3080 TI.

Model Details

Model Description

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("shinysup/YouronMath")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Math-1.5B")

• Developed by: Andrew Shin
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• Language(s) (NLP): [More Information Needed]
• License: MIT License
• Finetuned from model [optional]: Qwen2.5-Math-1.5B

Model Sources [optional]

• Repository: https://github.com/shinandrew/YouronMath
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Uses

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

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Training Hyperparameters

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Evaluation

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Results

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: Nvidia RTX 3080 Ti
• Hours used: Less than 30 hours
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Technical Specifications [optional]

Model Architecture and Objective

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Citation [optional]

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Framework versions

• PEFT 0.14.0