metadata
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
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.
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.