MathRoBERTa cyberinfrastructure: a transformer-based model, which has been trained with 8 Nvidia GPUs and over 3,000,000 math discussion posts by students and facilitators on Algebra Nation. MathRoBERTa has 24 layers, and 355 million parameters and its published model weights take up to 1.5 gigabytes of disk space. Researchers can easily download and utilize this model to conduct a series of natural language processing tasks (e.g., text classification, semantic search, Q&A) in similar math learning environments.

SafeMathBot cyberinfrastructure: build a transformer model using state-of-the-art language GPT-2 which has been trained with 8 Nvidia GPUs and enhanced with conversation safety policies (e.g., threat, profanity, identity attack) using 3,000,000 math discussion posts by students and facilitators on Algebra Nation. SafeMathBot consists of 48 layers and over 1.5 billion parameters, consuming up to 6 gigabytes of disk space. Researchers can experiment with and finetune the model to help construct math conversational AI that can effectively avoid unsafe response generation.

Fair-LR Python Model: create a fairness-enhanced logistic regression (Fair-LR) model of prediction based on Seldonian Framework. Fair-LR allows researchers to define their own fairness evaluation metrics, utilize existing popular fairness metrics, and incorporate these metrics into model learning to learn to be fairer. Fair-LR has demonstrated capabilities to achieve both fair and accurate predictions.

Fair-NE Python Model: construct a fair peer recommender using network embeddings. Fair-NE allows researchers to debias the peer recommender system by specifying multiple categorical demographic variables such as nationality, gender, and race. The model learns to adjust its internal embedding system to recommend peers without being influenced by students’ demographics. Fair-NE adopts a Bayesian approach for building recommender systems with dynamic update to provide fair and accurate insights.

SPAC3: takes an innovative approach to address these gaps and provide evidence-based insights. It aims to develop visual programming functions suitable for upper elementary students, helping them learn spatial programming effectively. By doing so, it will contribute to our understanding of how this tool impacts students' spatial reasoning, computational thinking, and their interest in computationally-intensive careers.

Git_20 Model: is fine-tuned with Microsoft GIT with 1 Nvidia A100-80G GPU. We extracted 100,000 student assignments containing teacher feedback from 3 million student assignments as training data. The training data is divided into the image part of student assignments and the text part of teacher feedback. git_20 consists of 18 layers and over 170 million parameters, consuming up to 0.7 gigabytes of disk space. The project aims to use multi-modal and multi-task deep learning models to create a machine learning pipeline that provides automatic diagnostic feedback for students' mathematical reasoning. Researchers can experiment with and finetune the model to help construct multimodel that can effectively provide automatic diagnostic feedback for students' mathematical reasoning.

Llama_Lora: is fine-tuned with LLaMA with 8 Nvidia A100-80G GPUs using 3,000,000 groups of conversations in the context of mathematics by students and facilitators on Algebra Nation (https://www.mathnation.com/). Llama-mt-lora consists of 32 layers and over 7 billion parameters, consuming up to 13.5 gigabytes of disk space. Researchers can experiment with and finetune the model to help construct math conversational AI that can effectively respond generation in a mathematical context.

BLIP-Math: has been fine-tuned on a comprehensive mathematical multi-modal dataset and incorporates two distinct output heads: text generation and scoring. We have made available the weight file specifically for the text generation component.

Math-GPT-J: is a fine-tuned GPT-J-6B model (GPT-J 6B is a transformer model trained using Ben Wang's Mesh Transformer JAX) trained with 8 Nvidia A-100 GPUs using 3,000,000 math discussion posts by students and facilitators on Algebra Nation (https://www.mathnation.com/). Math-GPT-J uses Rotary Position Embeddings, which has been found to be a superior method of injecting positional information into transformers. It has 28 layers, and 6 billion parameters and its published model weights take up to 24 gigabytes of disk space. It can potentially provide a good base performance on NLP related tasks (e.g., text classification, semantic search, Q&A) in similar math learning environments.

Llama-2-QLoRA: is fine-tuned with LLaMA-2 with 8 Nvidia A100-80G GPUs using 3,000,000 groups of conversations in the context of mathematics by students and facilitators on Algebra Nation (https://www.mathnation.com/). Llama-2-Qlora consists of 32 layers and over 7 billion parameters, consuming up to 13.5 gigabytes of disk space. Researchers can experiment with and finetune the model to help construct math conversational AI that can effectively respond generation in a mathematical context.