初始化项目，由ModelHub XC社区提供模型

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

.gitattributes

@@ -0,0 +1,36 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+Personal-Finance-R2-f16.gguf filter=lfs diff=lfs merge=lfs -text

Personal-Finance-R2-f16.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bc52462a05bec138c0eb0c1ad148b4d8519adf4b43290d986bc8ba288c942e85
+size 3447349504

README.md

@@ -0,0 +1,69 @@
+---
+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.