README.md

metadata

title: Finance-Llama-8B App
emoji: 📈
colorFrom: purple
colorTo: blue
sdk: docker
app_port: 7860
pinned: false

Finance-Llama-8B Gradio App

Two-tab financial agent UI powered by tarun7r/Finance-Llama-8B.

• Price Prediction: pulls OHLCV candles from Finnhub and asks the model for a short-term forecast with brief rationale.
• Equity Research Report: fetches Alpha Vantage fundamentals (via RapidAPI) and generates a concise equity research note.

Model

• Hugging Face model: tarun7r/Finance-Llama-8B
  • Fine-tuned unsloth/Meta-Llama-3.1-8B on Josephgflowers/Finance-Instruct-500k
• Loaded with bitsandbytes 4-bit quantization for efficient GPU memory usage.

Tech Stack

• Python 3.12
• Gradio UI
• Transformers + Accelerate + BitsAndBytes (GPU)
• Data: Finnhub (market data), Alpha Vantage via RapidAPI (fundamentals)

Prerequisites

• NVIDIA GPU with recent CUDA drivers (recommended)
• Python 3.12
• API keys:
  • FINNHUB_API_KEY from https://finnhub.io
  • RAPIDAPI_KEY with access to Alpha Vantage host alpha-vantage.p.rapidapi.com

Setup

Install dependencies:

pip install -r requirements.txt

Set environment variables (PowerShell example):

$env:FINNHUB_API_KEY = "YOUR_FINNHUB_KEY"
$env:RAPIDAPI_KEY = "YOUR_RAPIDAPI_KEY"

Optional server overrides:

$env:GRADIO_SERVER_NAME = "0.0.0.0"
$env:GRADIO_SERVER_PORT = "7860"

Run Locally

python app.py

Open http://localhost:7860.

Hugging Face Spaces (Docker)

This folder includes a Dockerfile suitable for Spaces (Docker) runtime. Ensure Secrets are set in the Space:

• FINNHUB_API_KEY
• RAPIDAPI_KEY

The app will start on port 7860 by default.

How It Works

• Price Prediction tab

  • Fetch last N candles from Finnhub.
  • Build a compact JSON context and prompt the LLM to provide a short-term view and bullet-point drivers.

• Equity Research Report tab

  • Call Alpha Vantage OVERVIEW via RapidAPI host alpha-vantage.p.rapidapi.com.
  • Prompt the LLM to synthesize a research note: business summary, performance, profitability, leverage, valuation, risks, and a Buy/Hold/Sell view.

Troubleshooting

• CUDA/torch version mismatch: use compatible PyTorch wheels for your environment or the provided Dockerfile on Spaces.
• bitsandbytes load errors: ensure GPU is available; for CPUs, remove 4-bit loading and use standard FP16/FP32 (will be slower, higher memory).
• API errors or empty responses: verify FINNHUB_API_KEY and RAPIDAPI_KEY, and check provider rate limits.

Notes

• Respect provider rate limits and terms of use.
• Outputs are model-generated and for research/education purposes only, not financial advice.