Model Card for Model ID

This model is fine-tuned for instruction-following in the domain of personal finance, with a focus on:

Budgeting advice
Investment strategies
Credit management
Retirement planning
Insurance and financial planning concepts
Personalized financial reasoning

Model Description

License: MIT
Finetuned from model: unsloth/Qwen3-1.7B
Dataset: The model was fine-tuned on the Kuvera-PersonalFinance-V2.1, curated and published by Akhil-Theerthala.

Model Capabilities

Understands and provides contextual financial advice based on user queries.
Responds in a chat-like conversational format.
Trained to follow multi-turn instructions and deliver clear, structured, and accurate financial reasoning.
Generalizes well to novel personal finance questions and explanations.

Uses

Direct Use

Chatbots for personal finance
Educational assistants for financial literacy
Decision support for simple financial planning
Interactive personal finance Q&A systems

Bias, Risks, and Limitations

Not a substitute for licensed financial advisors.
The model's advice is based on training data and may not reflect region-specific laws, regulations, or financial products.
May occasionally hallucinate or give generic responses in ambiguous scenarios.
Assumes user input is well-formed and relevant to personal finance.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel


tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen3-1.7B",)
base_model = AutoModelForCausalLM.from_pretrained(
    "unsloth/Qwen3-1.7B",
    device_map={"": 0}
)

model = PeftModel.from_pretrained(base_model,"khazarai/Personal-Finance-R2")


question = """ I just got accepted into Flatiron's full-time software engineering bootcamp, but I have basically no savings and the $19k price tag is freaking me out.
I really love coding and want to break into tech, but I'm looking at taking out a loan through Climb or Ascent with around 6.5% interest—that'd mean paying like $600 a month after.
Is this a smart move? I'm torn between chasing this opportunity and being terrified of the debt. Any advice?
"""

messages = [
    {"role" : "user", "content" : question}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize = False,
    add_generation_prompt = True, 
    enable_thinking = True, 
)

from transformers import TextStreamer
_ = model.generate(
    **tokenizer(text, return_tensors = "pt").to("cuda"),
    max_new_tokens = 3000,
    temperature = 0.6, 
    top_p = 0.95, 
    top_k = 20,
    streamer = TextStreamer(tokenizer, skip_prompt = True),
)

For pipeline:

from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen3-1.7B")
base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3-1.7B")
model = PeftModel.from_pretrained(base_model, "khazarai/Personal-Finance-R2")


question="""
I just got accepted into Flatiron's full-time software engineering bootcamp, but I have basically no savings and the $19k price tag is freaking me out.
I really love coding and want to break into tech, but I'm looking at taking out a loan through Climb or Ascent with around 6.5% interest—that'd mean paying like $600 a month after.
Is this a smart move? I'm torn between chasing this opportunity and being terrified of the debt. Any advice?
"""

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
messages = [
    {"role": "user", "content": question}
]

pipe(messages)

Training Details

Training Data

Dataset Overview: Kuvera-PersonalFinance-V2.1 is a collection of high-quality instruction-response pairs focused on personal finance topics. It covers a wide range of subjects including budgeting, saving, investing, credit management, retirement planning, insurance, and financial literacy.
Data Format: The dataset consists of conversational-style prompts paired with detailed and well-structured responses. It is formatted to enable instruction-following language models to understand and generate coherent financial advice and reasoning.