Rustamshry/Qwen3-1.7B-finance-reasoning

Model Card for Model ID

Model Details

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 PersonalFinance_v2 dataset, 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 huggingface_hub import login
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

login(token="")

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

model = PeftModel.from_pretrained(base_model,"Rustamshry/Qwen3-1.7B-finance-reasoning")


question =
"""
$19k for a coding bootcamp

Hi!

I was just accepted into the full-time software engineering program with Flatiron and have approx. $0 to my name.
I know I can get a loan with either Climb or accent with around 6.50% interest, is this a good option?
I would theoretically be paying near $600/month.

I really enjoy coding and would love to start a career in tech but the potential $19k price tag is pretty scary. 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 = 2048,
    temperature = 0.6, 
    top_p = 0.95, 
    top_k = 20,
    streamer = TextStreamer(tokenizer, skip_prompt = True),
)

Training Details

Training Data

• Dataset Overview: PersonalFinance_v2 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.

Framework versions

• PEFT 0.14.0