初始化项目，由ModelHub XC社区提供模型

Model: Rustamshry/Personal-Finance-R2-GGUF
Source: Original Platform

README.md

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+---
+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.