Wellwisher12/finance-phi3-gguf

📈 Financial Analyst AI (Phi-3 Mini 4K Instruct)

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.

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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🧠 Model Persona & Use Cases

This model is explicitly trained to act as a Professional Financial Analyst.

It is best used for:

• 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

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🚀 How to Use

You can interact with this model directly in your browser, via Ollama, or using Python.

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Option 1: Hugging Face Widget

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.

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Option 2: Run Locally via Ollama

If you have Ollama installed, you can pull and run the model directly from this repository with a single command.

It will automatically download the weights and apply the correct system prompt.

ollama run hf.co/Wellwisher12/finance-phi3-gguf

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Option 3: Run via Python (main.py)

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

pip install llama-cpp-python huggingface-hub

Execution

# 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

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⚙️ Required System Prompt

To achieve the best and most accurate results, the model should be initialized with the following system prompt.

Note: This is automatically handled if you use the provided Modelfile or main.py script.

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.

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📊 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

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✅ 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

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💡 Recommended Hardware

Hardware	Recommendation
RAM	Minimum 4GB
CPU	Modern multi-core CPU
GPU	Optional
Storage	~2-3GB free space

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📌 Example Prompt

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

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🙌 Credits

• Microsoft for the Phi-3 architecture
• Unsloth for efficient fine-tuning
• Hugging Face ecosystem
• Finance-Alpaca dataset contributors