69 lines
2.2 KiB
Markdown
69 lines
2.2 KiB
Markdown
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---
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tags:
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- unsloth
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- sft
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- reasoning
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- finance
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license: apache-2.0
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datasets:
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- Akhil-Theerthala/Kuvera-PersonalFinance-V2.1
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language:
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- en
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base_model:
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- khazarai/Personal-Finance-R2
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pipeline_tag: text-generation
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---
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# Model Card for Model ID
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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
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- Investment strategies
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- Credit management
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- Retirement planning
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- Insurance and financial planning concepts
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- Personalized financial reasoning
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### Model Description
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- **License:** MIT
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- **Finetuned from model:** unsloth/Qwen3-1.7B
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- **Dataset:** The model was fine-tuned on the Kuvera-PersonalFinance-V2.1, curated and published by Akhil-Theerthala.
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### Model Capabilities
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- Understands and provides contextual financial advice based on user queries.
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- Responds in a chat-like conversational format.
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- Trained to follow multi-turn instructions and deliver clear, structured, and accurate financial reasoning.
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- Generalizes well to novel personal finance questions and explanations.
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## Uses
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### Direct Use
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- Chatbots for personal finance
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- Educational assistants for financial literacy
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- Decision support for simple financial planning
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- 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.
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- The model's advice is based on training data and may not reflect region-specific laws, regulations, or financial products.
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- May occasionally hallucinate or give generic responses in ambiguous scenarios.
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- Assumes user input is well-formed and relevant to personal finance.
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## Training Data
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- Dataset Overview:
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Kuvera-PersonalFinance-V2.1 is a collection of high-quality instruction-response pairs focused on personal finance topics.
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It covers a wide range of subjects including budgeting, saving, investing, credit management, retirement planning, insurance, and financial literacy.
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- Data Format:
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The dataset consists of conversational-style prompts paired with detailed and well-structured responses.
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It is formatted to enable instruction-following language models to understand and generate coherent financial advice and reasoning.
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