📈 FinancialGPT — Domain-Specific LLM for Financial Forecasts

FinancialGPT is a small, domain-adapted language model fine-tuned on real financial forecast data to replicate ideas from FinGPT on consumer hardware.

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🧩 Project Overview

Goal: Fine-tune an open-source LLM to generate earnings outlooks for Dow30 companies using modern parameter-efficient methods.

• Base Model: Pythia-410M
• Techniques: LoRA + QLoRA (4-bit quantization)
• Domain: Financial news & company earnings forecasts
• Hardware: Google Colab (free tier)
• Framework: 🤗 Transformers + PEFT

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📚 Dataset

• Source: FinGPT/fingpt-forecaster-dow30-202305-202405
• Content: Recent news, earnings reports & forecasts for Dow30 companies (May 2023 — May 2024)

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⚙️ Method

• Used LoRA (Low-Rank Adaptation) for parameter-efficient fine-tuning.
• Applied QLoRA (4-bit quantization) to train with minimal hardware.
• Trained for 1 epoch as a proof-of-concept.
• Tokenized with padding & truncation to ensure proper batching.

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from transformers import AutoModelForCausalLM, AutoTokenizer

# Replace with your actual repo!
model_name = "Mahendra1742/FinancialGPT"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = """Company: TSLA
Period: 2024-09
Prompt: What is the market sentiment for Tesla's earnings next quarter?
Answer:"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
    **inputs,
    max_new_tokens=100,
    temperature=0.7,
    do_sample=True,
    top_p=0.9
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🔍 Example Prompt & Expected Output

Below is a realistic example showing the intended model behavior:

Company: AAPL
Period: 2024-09
Prompt: Provide a detailed market sentiment analysis for Apple's earnings forecast next quarter.
Answer: Analysts expect Apple to report steady revenue growth driven by strong iPhone sales and growth in services. Market sentiment remains positive, with a projected earnings per share increase of around 6% year-over-year.