| --- |
| tags: |
| - unsloth |
| - sft |
| - reasoning |
| - finance |
| license: apache-2.0 |
| datasets: |
| - Akhil-Theerthala/Kuvera-PersonalFinance-V2.1 |
| language: |
| - en |
| base_model: |
| - khazarai/Personal-Finance-R2 |
| pipeline_tag: text-generation |
| --- |
| |
| # Model Card for Model ID |
|
|
| GGUF version of https://huggingface.co/khazarai/Personal-Finance-R2 |
|
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| This model is fine-tuned for instruction-following in the domain of personal finance, with a focus on: |
|
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| - Budgeting advice |
| - Investment strategies |
| - Credit management |
| - Retirement planning |
| - Insurance and financial planning concepts |
| - Personalized financial reasoning |
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|
|
| ### 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 |
|
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| - 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 |
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| ### Direct Use |
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| - Chatbots for personal finance |
| - Educational assistants for financial literacy |
| - Decision support for simple financial planning |
| - Interactive personal finance Q&A systems |
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|
| ## Bias, Risks, and Limitations |
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| - 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. |
|
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| ## Training Data |
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| - 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. |
