khazarai/Personal-Finance-R1

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:2BQuant:BF16Ctx Length:32kPublished:Mar 23, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Warm

The khazarai/Personal-Finance-R1 model is a fine-tuned instruction-following language model based on unsloth/Qwen3-1.7B, developed by khazarai. It specializes in providing contextual financial advice across budgeting, investments, credit, and retirement planning. This model is optimized for generating clear, structured, and accurate financial reasoning in a conversational format, making it suitable for personal finance chatbots and educational assistants.

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Model Overview

khazarai/Personal-Finance-R1 is an instruction-following language model fine-tuned from unsloth/Qwen3-1.7B under an MIT license. Developed by khazarai, this model is specifically designed for the domain of personal finance, leveraging the PersonalFinance_v2 dataset curated by Akhil-Theerthala.

Key Capabilities

  • Contextual Financial Advice: Understands and provides relevant advice on budgeting, investment strategies, credit management, and retirement planning.
  • Conversational Interaction: Responds in a chat-like format, capable of handling multi-turn instructions.
  • Structured Reasoning: Delivers clear, structured, and accurate financial reasoning.
  • Generalization: Exhibits strong generalization capabilities for novel personal finance questions and explanations.

Good For

  • Personal Finance Chatbots: Ideal for creating interactive assistants that offer financial guidance.
  • Educational Tools: Can serve as an educational assistant for financial literacy programs.
  • Decision Support: Provides support for basic financial planning scenarios.
  • Interactive Q&A Systems: Powers systems designed for answering personal finance queries.

Limitations

It is important to note that this model is not a substitute for licensed financial advisors. Its advice is based on training data and may not account for region-specific regulations or products. The model may occasionally hallucinate or provide generic responses, and it assumes well-formed, relevant user input.