Update README.md

README.md

@@ -188,152 +188,161 @@ to the few shot examples derived from the goals dataset.
 Q: My short term goal is to save for a $3357 vacation in the next year, my medium term goal is to save for down payment for a new car, around 6867 in the next 2 or 3 years, and my long term goal is to save for a down payment for a house around 115061 in the next ten years, can you help me integrate these goals into my budget as well as where I should store these savings?
 ```
 
-### Downstream Use [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+I reccomend the following code to set up few shot prompting for the goals task:
 
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+```{python}\n
 
-### Recommendations
+test_goals = goals[2500:]
+
+pipe = pipeline(
+    "text-generation", 
+    model=model, 
+    torch_dtype=torch.bfloat16, 
+    device_map="auto", 
+    tokenizer = tokenizer, 
+    max_new_tokens = 750,
+    do_sample = False,
+    temperature = 0
+)
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+def few_shot_goal(df3,pipe,n = 1,q = 10):
+    examples = []
+    for i in range(n):
+        instruct = df3['instruct'].iloc[i]
+        examples.append(instruct)
+    examples.append('YOUR PROMPT HERE')
+    examples = "\n\n".join(examples)
+    text = pipe(examples)
+    print(text[0]['generated_text'])
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+few_shot_goal(test_goals,pipe,n = 3,q=10)
 
-## How to Get Started with the Model
+```
 
-Use the code below to get started with the model.
+### Example Response
 
-[More Information Needed]
+For the budget task, you can generally expect a response like the following from the model. It should almost exclusively contain python code which integrates your provided budget figures
+and creates for you a budget spreadsheet to assist with tracking your expenses.
 
-## Training Details
+```{python}\n
+A:  Lets use the python library pandas for data manipulation and export the spreadsheet using openpyxl. First, ensure you have both libraries installed: ```bash
+pip install pandas openpyxl
 
-### 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. -->
+Now, let's create a Python script to generate your budget spreadsheet:
 
-[More Information Needed]
+python
+import pandas as pd
+import openpyxl
 
-### Training Procedure
+# Define your income and expenses
+annual_income = 220034
+monthly_income = annual_income / 12
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+expenses = {
+    "Rent & Utilities": 2379,
+    "Car Payment": 302,
+    "Food": 300,
+    "Other Expenses": 351
+}
 
-#### Preprocessing [optional]
+total_expenses = sum(expenses.values())
+net_savings = monthly_income - total_expenses
 
-[More Information Needed]
+# Create a dictionary with your budget data
+budget_data = {
+    "Category": ["Monthly Income"] + list(expenses.keys()) + ["Total Expenses", "Net Savings"],
+    "Amount ($)": [monthly_income] + list(expenses.values()) + [total_expenses, net_savings]
+}
 
+# Convert the dictionary into a DataFrame
+budget_df = pd.DataFrame(budget_data)
 
-#### Training Hyperparameters
+# Save the DataFrame to an Excel file
+file_name = "budget.xlsx"
+budget_df.to_excel(file_name, index=False, engine='openpyxl')
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+print(f"Budget spreadsheet saved as {file_name}")
 
-#### Speeds, Sizes, Times [optional]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+This script will create an Excel file named `budget.xlsx` in the same directory, containing your income and expenses breakdown. Make sure to run this in a Python environment where you have write access.
+```
 
-[More Information Needed]
 
-## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+For the second task you can reasonably expect a response along the lines of the output below. This should reasonably follow the structure of walking you through the breakdown of your
+short, medium and long term goals into monthly savings targets depending on their timeframe as well as suggesting reasonable investment vehicles for them. 
 
-### Testing Data, Factors & Metrics
+```{python}\n
+A: 1. First, let's figure out how much you can realistically save each month. We know you want to save for three things: a vacation costing $1741, a car down payment of $9356, and a house down payment of $83333. 
 
-#### Testing Data
+2. To make it easier, let's add up those numbers: $1741 + $9356 + $83333 gives us a total savings goal of $98390. 
 
-<!-- This should link to a Dataset Card if possible. -->
+3. Now, we need to think about how long you have to reach this goal. You've got a year for the vacation, 2-3 years for the car, and 10 years for the house. Let's take the 10 years as our benchmark since it's the longest time frame. That means you have 10 years to save $98390.
 
-[More Information Needed]
+4. So, let's calculate your monthly savings. Divide $98390 by 10 years, which is 120 months, and you get about $816.75 per month. 
 
-#### Factors
+5. Now, where should you store these savings? Considering you've got a long-term goal, it might be wise to explore low-risk options like high-yield savings accounts or certificates of deposit (CDs). These can help your money grow slowly over time while remaining easily accessible.
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+6. Finally, make sure to set up automatic transfers from your checking account to your chosen savings vehicle to make saving easier and less prone to being neglected.
 
-[More Information Needed]
+Let's see how this plan looks: You'll aim to save about $816.75 monthly for 10 years. This should help you reach your combined savings goal without much hassle, and you can monitor your progress along the way. Oh, and don't forget to review and adjust as needed!
 
-#### Metrics
+## Thinking
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+Alright, let's break this down step by step. First, I need to figure out how much I can realistically save each month to reach my savings goals. I have three main goals: saving for a $1741 vacation in a year, a $9356 car down payment in 2-3 years, and a $83333 house down payment in 10 years.
 
-[More Information Needed]
+Okay, so let's add up these numbers: $1741 plus $9356 plus $83333 gives me a total savings goal of $98390. Now, I need to think about how long I have to reach this goal. Since 10 years is the longest period, I'll use that as my timeframe.
 
-### Results
+Next, I'll calculate my monthly savings. I divide $98390 by 10 years, which is 120 months, and get about $816.75 per month. This is my target savings amount each month.
 
-[More Information Needed]
+Now, where should I store these savings? Given that my goal is long-term, I should look into low-risk options like high-yield savings accounts or certificates of deposit (CDs). These will help my money grow slowly over time while keeping it easily accessible.
 
-#### Summary
+Finally, to make saving easier, I'll set up automatic transfers from my checking account to my chosen savings vehicle. This way, saving won't be so hard to stick to, and I can monitor my progress along the way. It looks like saving $816.75 monthly for 10 years should help me reach my combined savings goal without much hassle.
 
+```
 
+## Bias, Risks, and Limitations
 
-## Model Examination [optional]
+There are several risks and limitations of this model that are worth mentioning. First, in a handful of cases this model produced responses in which the math inherent in the
+savings goals responses was not correct, sometimes failing to add numbers up correctly or having slight rounding errors when dividing long term goals into monthly targets. 
+While it is well known that LLMs can struggle with mathematics given that their knolwedge is language based and not numerically based, this can be a problem for a finance focused LLM. 
+I strongly reccomend double checking the figures presented by this model. While this issue is sidestepped in the budget task through the use of python code to prevent math errros 
+that safeguard is not implemented for the goals task. Further, this model should be limited in its use for out of scope tasks as the generalization benchmarks demonstrated that
+compared to its base model, this model exhibits decreased reasoning ability outside its domain specific task. 
 
-<!-- Relevant interpretability work for the model goes here -->
+In order to imprve theis model I woudl reccomend future model trainers and tuners focus on adjusting this model to default to producing python code for all mathematics based 
+prompts. Sticking with python for mathematics processing shoula allow the model to perform more highly on the goals task while retaining performance on the 
 
 [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]
+Rivanna High Performance Computing Cluster (University of Virginia)
 
 #### Hardware
 
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
+1 node
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+32GB system RAM
 
-**BibTeX:**
+GPU NVIDIA A6000
 
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
+2 hours of LoRA PEFT Tuning. 
 
-## Glossary [optional]
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## Works Cited
 
-[More Information Needed]
+Major Credit to TheFinAI team who created the Fino-1 8B model that this model is based on. 
 
-## More Information [optional]
+  title={Fino1: On the Transferability of Reasoning Enhanced LLMs to Finance},
+  author={Qian, Lingfei and Zhou, Weipeng and Wang, Yan and Peng, Xueqing and Huang, Jimin and Xie, Qianqian},
+  journal={arXiv preprint arXiv:2502.08127},
+  year={2025}
+}
 
-[More Information Needed]
 
 ## Model Card Authors