output = llm(
"Once upon a time,",
max_tokens=512,
echo=True
)
print(output)Finance Earnings Call Q&A Bot
Fine-tuned LLM for financial question-answering and earnings call simulation
By Mrinal Kumar
🚀 Model Overview
This model is a quantized [Qwen-2.5B / your base model] transformer fine-tuned on real earnings call transcripts and Q&A pairs from S&P 500 companies and global markets. It is designed to:
- Summarize complex financial calls into key insights
- Simulate Q&A between analysts and CFOs/CEOs
🏆 Example Use Cases
- Students: Learn how real-world analysts and CFOs communicate
- Investors: Get concise summaries or simulate earnings call Q&A
- Researchers: Build finance chatbots or extract structured knowledge from transcripts
🗂️ Training Data
- Dataset: Manually curated Q&A pairs extracted from publicly available earnings calls (Kaggle Earnings Call Datasets)
- Format: Each example consists of an analyst question (
input) and a CFO/CEO response (output) - Size: 1,000+ Q&A pairs for diverse scenarios
💡 Example Questions
| Analyst Question | Model Response |
|---|---|
| What drove the 20% YoY revenue growth? | Revenue growth was driven by new subscriptions and higher pricing. |
| Can you explain margin contraction in the EU market? | Margins contracted due to logistics costs and currency headwinds. |
| What are your key risks for next quarter? | Potential supply-chain delays and FX volatility. |
🔬 Training Details
- Base model: Qwen-2.5B quantized GGUF
- Environment: Google Colab, 4-bit quantization for memory efficiency
- Optimization: Fine-tuned using Unsloth/PEFT on curated JSONL dataset
🤝 Acknowledgements
- Hugging Face & Kaggle for model hosting and data
- Open source communities for technical guidance
🌐 License
Apache 2.0
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# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="MrinalKumar/finance-analyzer-V2", filename="unsloth.Q4_K_M.gguf", )