Update README.md

README.md

@@ -103,18 +103,88 @@ for the budget task while both Fino-1 8B and FinPlan-1 performed well on the goa
 were benchmarked on GSM8K (grade school mathematics reasoning) as well as MMLU (general reasoning). While the domain specific LoRA tuning certainly led to a degredation in FinPlan-1's
 benchmark scores with respect to its underlying model Fino-1 8B, the drop in performance is rather small for MMLU and GSM8K performance remains above Llama 3.2 -3B Instruct. 
 
-
-
-
-## 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
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+## Intended Useage
+
+As described above this model is intended to be used to assist with the creation of simple financial plans for individuals, specifically for assistance with the creation of a budget
+spreadsheet for tracking expenseses as well as planning for, short, medium and long term savings goals. While this model can be prompted on a wide range of other tasks, it is
+not recommended to use this model for those purposes as it has been speficially fine tuned for these two tasks and perforamnce on tasks outside that scope could be diminished.
+
+See below for the basic code required in order to import the model from huggingface using torch. Note the tokenizer is pulled from the Fino-1 8B repository as it was not changed 
+from the base Fino-1 8B model.
+
+```{python}\n
+import os
+os.environ['HF_HOME'] = "your/directory/here"
+import torch 
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from datasets import load_dataset #datasets is huggingface's dataset package
+from peft import get_peft_model, LoraConfig, TaskType
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import PIL
+
+import lm_eval
+
+tokenizer = AutoTokenizer.from_pretrained("TheFinAI/Fino1-8B")
+model = AutoModelForCausalLM.from_pretrained("ThinkTim21/FinPlan-1")
+
+# Prepare the model and tokenizer 
+tokenizer.pad_token = tokenizer.eos_token # set padding token to EOS token
+model.config.poad_token_id = tokenizer.pad_token_id # set the padding token for model
+
+
+budget = pd.read_csv("budget_dataset.csv") # use the dataset attached to this repo
+goals = pd.read_csv("goals_dataset.csv") # use the dataset attached to this repo
+
+budget['instruct_lora'] = budget.apply(
+    lambda row: f"Q: {row['question']}\n\nA: ",
+    axis=1
+)
+
+goals['instruct_lora'] = goals.apply(
+    lambda row: f"Q: {row['question']}\n\nA: ",
+    axis=1
+)
+
+from datasets import load_dataset, Dataset #datasets is huggingface's dataset package
+budget = budget.sample(frac = 1, random_state = 42) # randomly shuffle DF
+train_budget = budget[:2500]
+val_budget = budget[2500:]
+train_budget = Dataset.from_pandas(train_budget)
+val_budget = Dataset.from_pandas(val_budget)
+train_budget = train_budget.map(lambda samples: tokenizer(samples['instruct']), batched = True)
+val_budget = val_budget.map(lambda samples: tokenizer(samples['instruct']), batched = True)
+
+goals = goals.sample(frac = 1, random_state = 42) # randomly shuffle DF
+train_goals = goals[:2500]
+val_goals = goals[2500:]
+train_goals = Dataset.from_pandas(train_goals)
+val_goals = Dataset.from_pandas(val_goals)
+train_goals = train_goals.map(lambda samples: tokenizer(samples['instruct']), batched = True)
+val_goals = val_goals.map(lambda samples: tokenizer(samples['instruct']), batched = True)
+
+formatted_prompt = f"Q: {val_goals[0]['question']}\n\nA: "
+inputs = tokenizer.encode(formatted_prompt, return_tensors = "pt").to(model.device)
+output = model.generate(inputs, max_new_tokens = 800, pad_token_id = tokenizer.pad_token_id, do_sample = False)
+generated_text = tokenizer.decode(output[0], skip_special_tokens = True)
+print(generated_text)
+```
+
+### Prompt Format
+
+The prompt format varies between the budget task and the goals task. 
+
+For the budget task, the following prompt method is reccomended.
+
+```Q: I  have an income of about 53255 a year and my monthly expenses include 2208 a month in rent and utilities, a 700 car payment, $300 in food, and about 205 a month in other expenses. Using python, can you create for me a budget spreadsheet and export it to excel?
+```
+
+For the goals task, I reccomend using Few Shot Prompting, making use of the goals_dataset.csv file as your base and then adding your prefered prompt in the following format 
+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]