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  # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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  ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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  - **Developed by:** [More Information Needed]
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  - **Funded by [optional]:** [More Information Needed]
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  - **Shared by [optional]:** [More Information Needed]
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  ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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-
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  - **Repository:** [More Information Needed]
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  - **Paper [optional]:** [More Information Needed]
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  - **Demo [optional]:** [More Information Needed]
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  <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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-
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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-
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- ### Downstream Use [optional]
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-
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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-
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- ### Out-of-Scope Use
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-
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Bias, Risks, and Limitations
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  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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-
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- Use the code below to get started with the model.
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- [More Information Needed]
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  ## Training Details
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  ### Training Data
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  <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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-
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Results
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- [More Information Needed]
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Environmental Impact
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  # Model Card for Model ID
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+ The Prob-Gen-8B Large Language Model (LLM) is a fine-tuned model of the [Llama-3-8B from Meta](https://huggingface.co/meta-llama/Meta-Llama-3-8B). The intend of the Prob-Gen-8B LLM is to generate math problems under different contexts and tested knowledge for 8th graders.
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  ### Model Description
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  - **Developed by:** [More Information Needed]
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  - **Funded by [optional]:** [More Information Needed]
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  - **Shared by [optional]:** [More Information Needed]
 
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  ### Model Sources [optional]
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  - **Repository:** [More Information Needed]
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  - **Paper [optional]:** [More Information Needed]
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  - **Demo [optional]:** [More Information Needed]
 
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  <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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+ The model can be loaded with HuggingFace's Transformers library:
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+ ``` python
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+ import transformers
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+ import torch
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+
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+ model_id = "duke-nlp/Porb-Gen-8B"
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+
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+ model = AutoModelForCausalLM.from_pretrained(
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+ model_id,
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+ device_map="auto",
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+ torch_dtype=torch.bfloat16
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+ )
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+
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+ tokenizer = AutoTokenizer.from_pretrained(
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+ model_id,
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+ use_fast=False,
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+ legacy=False
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+ )
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+
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+ model_input = tokenizer(
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+ """Please generate a math problem and 2 to 4 options for 8th graders with the following requirements:
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+ Problem context: <specified-context>
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+ Tested knowledge: <specified-knowledge>""",
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+ return_tensors="pt",
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+ ).to("cuda")
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+
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+ model_output = model.generate(
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+ model_input['input_ids'],
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+ max_new_tokens=256,
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+ do_sample=True,
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+ ...
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+ )
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+
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+ tokenizer.batch_decode(model_output)
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+ ```
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  ## Bias, Risks, and Limitations
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  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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  ## Training Details
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  ### Training Data
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  <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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+ The model is finetuned on 3,644 GPT-4 generated 8th-grade problems, which are also annotated and evaluated by humans, an example of our data point is shown below:
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+ ``` json
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+ "options": [
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+ {
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+ "optionText": "Multiply 500 by 3/5 to get 300 tons.",
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+ "correct": true
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+ },
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+ {
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+ "optionText": "Divide 500 by 3 to get 166.67 tons.",
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+ "correct": false
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+ }
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+ ],
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+ "problemContext": "Environmental issues",
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+ "evaluated_problem": "A town's recycling plant recycles plastic and glass in a ratio of 3:2. If the plant processes 500 tons of recyclables, how much of it is plastic?",
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+ "unitTitle": "Solving Multi-Step Problems with Proportional Relationships"
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+ ```
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+
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+ ### Prompting
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+ The model is trained by the following prompt:
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+ ``` python
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+ """Please generate a math problem and 2 to 4 options for 8th graders with the following requirements:
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+ Problem context: <specified-context>
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+ Tested knowledge: <specified-knowledge>"""
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+ ```
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+ Where the contexts shown in the dataset are:
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+ ```
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+ "Video Games",
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+ "Fashion",
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+ "Influencers/YouTubers",
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+ "Apps and Technology",
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+ "Movies/TV shows",
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+ "Sports",
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+ "Music and Concerts",
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+ "Social Media",
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+ "Environmental issues"
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+ ```
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+ And the tested knowledge shown in the dataset are:
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+ ```
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+ "Operations with Rational Numbers",
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+ "Expressions and Equations",
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+ "Surface Area and Volume",
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+ "Arithmetic in Base Ten",
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+ "Evaluating Numeric Expressions",
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+ "Properties and Theorems of Angles",
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+ "Data Sets",
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+ "Rational Number Arithmetic",
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+ "Functions and Volume",
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+ "Linear Equations and Linear Systems",
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+ "Representing Data and Distributions",
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+ "Algebraic Expressions",
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+ "Ratios and Rates",
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+ "Solving Equations and Systems of Equations",
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+ "Operations with Integers",
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+ "Scatter Plots",
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+ "Solving Percentage Problems with Proportional Relationships",
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+ "Associations in Data",
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+ "Expressions, Equations, and Inequalities",
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+ "Linear Relationships",
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+ "Representing Data",
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+ "Solving Multi-Step Problems with Proportional Relationships",
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+ "Dividing Fractions",
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+ "Area, Surface Area, and Volume",
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+ "Equivalent Algebraic Expressions",
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+ "Key Features of Linear Equations",
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+ "Proportional Relationships and Percentages",
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+ "Transformations",
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+ "Representing Proportional Relationships"
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+ ```
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  ### Results
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+ Here is an example passage from the training data:
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+ ```
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+ Please generate a math problem and options for 8th graders with the following requirements:
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+ Problem context: Movies/TV shows
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+ Tested knowledge: Representing Data and Distributions
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+ Question: Lucas counted the number of episodes in 12 seasons of a TV show. He recorded: 48, 51, 52, 55, 56, 58, 59, 60, 61, 62, 65, 67. How should he create a frequency table for the number of episodes?
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+ Option 1: Group the data into intervals of 10, then count the number of seasons in each interval.
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+ Is correct: False
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+ Option 2: Group the data into intervals of 5 starting from 45, then count the number of seasons in each interval.
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+ Is correct: True
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+ ```
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+ And here is an example passage generated from the fine-tuned model:
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+ ```
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+ Please generate a math problem and 2 to 4 options for 8th graders with the following requirements:
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+ Problem context: Video Games
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+ Tested knowledge: Expressions and Equations
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+ Question: In a video game, the power of a character's weapon is calculated by the formula \(a^b\). If the weapon's power is \(2^{4}\), what is the value of \(a\) and \(b\)?
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+ Option 1: \(a = 2\) and \(b = 4\)
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+ Is correct: True
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+ Option 2: \(a = 4\) and \(b = 2\)
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+ Is correct: False
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+ Option 3: \(a = 2\) and \(b = 2\)
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+ Is correct: False
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+ Option 4: \(a = 2\) and \(b = 8\)
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+ Is correct: False
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+ ```
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  ## Environmental Impact
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