| --- |
| library_name: transformers |
| tags: |
| - unsloth |
| - sft |
| - reasoning |
| license: apache-2.0 |
| datasets: |
| - Akhil-Theerthala/Kuvera-PersonalFinance-V2.1 |
| language: |
| - en |
| base_model: |
| - unsloth/Qwen3-1.7B |
| pipeline_tag: text-generation |
| --- |
| |
| # 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. |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| |
| tokenizer = AutoTokenizer.from_pretrained("khazarai/Personal-Finance-R2") |
| model = AutoModelForCausalLM.from_pretrained( |
| "khazarai/Personal-Finance-R2", |
| device_map={"": 0} |
| ) |
| |
| |
| 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), |
| ) |
| ``` |
|
|
| ## 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. |
