Update README.md

README.md

@@ -5,18 +5,13 @@ tags: []
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+This model is a fine-tuned based on [Llama-3-70B from Meta](https://huggingface.co/meta-llama/Meta-Llama-3-8B) for 3,644 GPT-4 generated grade school math word problems. The model generates math word problems with multiple choices under given contexts.
 
+<!-- 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
 - **Developed by:** [More Information Needed]
 - **Funded by [optional]:** [More Information Needed]
 - **Shared by [optional]:** [More Information Needed]
@@ -27,173 +22,163 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
-
 - **Repository:** [More Information Needed]
 - **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed] -->
 
 ## Uses
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-### Direct Use
+The model can be loaded with HuggingFace's Transformers library:
+``` python
+import transformers
+import torch
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+model_id = "duke-nlp/Prob-Gen-70B"
 
-[More Information Needed]
+model = AutoModelForCausalLM.from_pretrained(
+	model_id,
+	device_map="auto",
+	torch_dtype=torch.bfloat16
+)
 
-### Downstream Use [optional]
+tokenizer = AutoTokenizer.from_pretrained(
+	model_id, 
+	use_fast=False,
+	legacy=False
+)
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+model_input = tokenizer(
+	"""Please generate a math problem and 2 to 4 options for 8th graders with the following requirements:
+Problem context: <specified-context>
+Tested knowledge: <specified-knowledge>""", 
+	return_tensors="pt",
+).to("cuda")
 
-[More Information Needed]
+model_output = model.generate(
+	model_input['input_ids'], 
+	max_new_tokens=256, 
+	do_sample=True,
+	...
+)
 
-### Out-of-Scope Use
+tokenizer.batch_decode(model_output)
+```
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
+<!-- ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
+<!-- [More Information Needed]
 
-### Recommendations
+<!-- ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations.
 
 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.
-
-[More Information Needed]
-
-## Training Details
+<!-- ## Training Details -->
 
 ### Training Data
 
 <!-- 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. -->
 
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+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: 
+``` json
+"options": [
+	{
+		"optionText": "Multiply 500 by 3/5 to get 300 tons.",
+		"correct": true
+	},
+	{
+		"optionText": "Divide 500 by 3 to get 166.67 tons.",
+		"correct": false
+	}
+],
+"problemContext": "Environmental issues",
+"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?", 
+"unitTitle": "Solving Multi-Step Problems with Proportional Relationships"
+```
+
+### Prompting
+The model can be evaluated by using the following prompt:
+``` python
+"""Please generate a math problem and 2 to 4 options for 8th graders with the following requirements:
+Problem context: <specified-context>
+Tested knowledge: <specified-knowledge>"""
+```
+The contexts used in the dataset are:
+```
+"Video Games",
+"Fashion",
+"Influencers/YouTubers",
+"Apps and Technology",
+"Movies/TV shows",
+"Sports",
+"Music and Concerts",
+"Social Media",
+"Environmental issues"
+```
+The tested knowledge in the dataset are:
+```
+"Operations with Rational Numbers",
+"Expressions and Equations",
+"Surface Area and Volume",
+"Arithmetic in Base Ten",
+"Evaluating Numeric Expressions",
+"Properties and Theorems of Angles",
+"Data Sets",
+"Rational Number Arithmetic",
+"Functions and Volume",
+"Linear Equations and Linear Systems",
+"Representing Data and Distributions",
+"Algebraic Expressions",
+"Ratios and Rates",
+"Solving Equations and Systems of Equations",
+"Operations with Integers",
+"Scatter Plots",
+"Solving Percentage Problems with Proportional Relationships",
+"Associations in Data",
+"Expressions, Equations, and Inequalities",
+"Linear Relationships",
+"Representing Data",
+"Solving Multi-Step Problems with Proportional Relationships",
+"Dividing Fractions",
+"Area, Surface Area, and Volume",
+"Equivalent Algebraic Expressions",
+"Key Features of Linear Equations",
+"Proportional Relationships and Percentages",
+"Transformations",
+"Representing Proportional Relationships"
+```
+
+### Sample Generation
+
+Here is an example passage from the training data:
+```
+Please generate a math problem and options for 8th graders with the following requirements:
+Problem context: Movies/TV shows
+Tested knowledge: Representing Data and Distributions
+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?
+Option 1: Group the data into intervals of 10, then count the number of seasons in each interval.
+Is correct: False
+Option 2: Group the data into intervals of 5 starting from 45, then count the number of seasons in each interval.
+Is correct: True
+```
+And here is an example passage generated from the fine-tuned model:
+```
+Please generate a math problem and 2 to 4 options for 8th graders with the following requirements:
+Problem context: Video Games
+Tested knowledge: Expressions and Equations
+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 \(b\)?
+Option 1: \(a = 2\) and \(b = 4\)
+Is correct: True
+Option 2: \(a = 4\) and \(b = 2\)
+Is correct: False
+Option 3: \(a = 2\) and \(b = 2\)
+Is correct: False
+Option 4: \(a = 2\) and \(b = 8\)
+Is correct: False
+```