| --- |
| pipeline_tag: text-generation |
| library_name: gguf |
| tags: |
| - finance |
| - phi3 |
| - unsloth |
| - llama-cpp |
| - gguf-4bit |
| --- |
| |
|
|
|
|
| # 📈 Financial Analyst AI (Phi-3 Mini 4K Instruct) |
|
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| This is a fine-tuned, 4-bit quantized (GGUF) version of Microsoft's `Phi-3-Mini-4k-instruct`, specialized in professional financial analysis, stock market valuation, and corporate finance. |
|
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| The model was trained using **Unsloth** on a financial instruction dataset and has been aggressively optimized for low-memory environments. It easily runs on standard laptops with less than **3GB of RAM** while maintaining high factual accuracy. |
|
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| --- |
|
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| # 🧠 Model Persona & Use Cases |
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| This model is explicitly trained to act as a **Professional Financial Analyst**. |
|
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| It is best used for: |
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| - Stock market analysis and valuation metrics |
| - Corporate finance and accounting principles |
| - Investment strategy and portfolio management |
| - Explaining economic trends and market indicators |
| - Risk assessment and financial modeling |
|
|
| --- |
|
|
| # 🚀 How to Use |
|
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| You can interact with this model directly in your browser, via Ollama, or using Python. |
|
|
| --- |
|
|
| ## Option 1: Hugging Face Widget |
|
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| You can test the model immediately using the **Hosted Inference API** widget on the right side of this page. |
|
|
| > **Note:** Because this is a GGUF model, it may take **15–30 seconds** to load into Hugging Face's server RAM on the first prompt. |
|
|
| --- |
|
|
| ## Option 2: Run Locally via Ollama |
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| If you have Ollama installed, you can pull and run the model directly from this repository with a single command. |
|
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| It will automatically download the weights and apply the correct system prompt. |
|
|
| ```bash |
| ollama run hf.co/Wellwisher12/finance-phi3-gguf |
| ``` |
|
|
| --- |
|
|
| ## Option 3: Run via Python (`main.py`) |
|
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| This repository includes `main.py` script that utilizes `llama-cpp-python` to run the model with strict memory constraints (`n_ctx=1024`) to prevent out-of-memory errors on local machines. |
|
|
| ### Prerequisites |
|
|
| ```bash |
| pip install llama-cpp-python huggingface-hub |
| ``` |
|
|
| ### Execution |
|
|
| ```bash |
| # Clone the repository |
| git clone https://huggingface.co/Wellwisher12/finance-phi3-gguf |
| |
| # Navigate into the directory |
| cd finance-phi3-gguf |
| |
| # Launch the interactive terminal |
| python main.py |
| ``` |
|
|
| --- |
|
|
| # ⚙️ Required System Prompt |
|
|
| To achieve the best and most accurate results, the model should be initialized with the following system prompt. |
|
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| > **Note:** This is automatically handled if you use the provided `Modelfile` or `main.py` script. |
|
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| ```text |
| You are a professional Financial Analyst with expertise in: |
| |
| - Stock market analysis and valuation |
| - Corporate finance and accounting |
| - Investment strategy and portfolio management |
| - Economic trends and market indicators |
| - Risk assessment and financial modeling |
| |
| Your responses should be: |
| |
| - Accurate and data-driven |
| - Professional and neutral in tone |
| - Comprehensive yet concise |
| - Based on sound financial principles |
| |
| Always provide specific examples and metrics when relevant. |
| ``` |
|
|
| --- |
|
|
| # 📊 Technical Specifications |
|
|
| | Specification | Details | |
| |---|---| |
| | Base Model | `unsloth/phi-3-mini-4k-instruct` | |
| | Dataset | `gbharti/finance-alpaca` | |
| | Quantization | `Q4_K_M (4-bit)` | |
| | Format | `GGUF` | |
| | Recommended Temperature | `0.2` | |
| | Recommended Context Window | `1024 - 2048 tokens` | |
|
|
| --- |
|
|
| # ✅ Key Features |
|
|
| - Fine-tuned specifically for financial reasoning tasks |
| - Lightweight and optimized for low-RAM systems |
| - Compatible with Ollama and `llama.cpp` |
| - Quantized GGUF format for efficient local inference |
| - Professional analyst-style responses |
| - Reduced hallucinations with low-temperature inference |
|
|
| --- |
|
|
| # 💡 Recommended Hardware |
|
|
| | Hardware | Recommendation | |
| |---|---| |
| | RAM | Minimum 4GB | |
| | CPU | Modern multi-core CPU | |
| | GPU | Optional | |
| | Storage | ~2-3GB free space | |
|
|
| --- |
|
|
| # 📌 Example Prompt |
|
|
| ```text |
| Analyze Apple's current valuation using P/E ratio, revenue growth, and free cash flow trends. |
| ``` |
|
|
| --- |
|
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| # 📜 License |
|
|
| Please follow the licensing terms of the original base model and dataset used in this project. |
|
|
| - Base Model: `Microsoft Phi-3 Mini` |
| - Dataset: `finance-alpaca` |
|
|
| --- |
|
|
| # 🙌 Credits |
|
|
| - Microsoft for the Phi-3 architecture |
| - Unsloth for efficient fine-tuning |
| - Hugging Face ecosystem |
| - Finance-Alpaca dataset contributors |
