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finance-analyzer
DhruvBajaji/finance-analyzer
finance-analyzer is a machine learning model from DhruvBajaji. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
A purpose-built LLM fine-tuned for answering finance questions and recreating earnings-call dialogues Created by Dhruv Bajaji
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At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- qwen2
- Access
- Public
- Created
- Aug 13, 2025
- Updated
- Oct 28, 2025
- SHA
- 0553db3c
Base models
Model card
From the Hugging Face model README
Finance Earnings-Call Q&A Assistant
A purpose-built LLM fine-tuned for answering finance questions and recreating earnings-call dialogues
Created by Dhruv Bajaji
🚀 Model Snapshot
This release is a quantized version of the Qwen-2.5 B model, refined on authentic earnings-call transcripts and analyst exchanges from S&P 500 firms and other major markets. It excels at:
- Distilling lengthy calls into clear, actionable takeaways
- Emulating the back-and-forth between analysts and corporate leadership
🏆 Ideal Applications
- Students – Observe the language and framing used by real analysts and executives
- Investors – Request quick call summaries or run mock Q&A sessions
- Researchers & Builders – Integrate a finance-savvy chatbot or mine structured insights from transcripts
🗂 Training Corpus
- Source – Hand-selected Q&A snippets from publicly available earnings-call datasets (e.g., Kaggle)
- Structure – JSONL where each record pairs an analyst’s query with a CFO/CEO reply
- Scale – 1 000 + Q&A examples covering a wide range of scenarios
💡 Sample Prompts
| Analyst Query | Model Reply (sample) |
|---|---|
| “What fueled the 20 % year-over-year revenue jump?” | “Growth stemmed mainly from new subscription tiers and price lifts.” |
| “Why did EU margins narrow this quarter?” | “Logistics inflation and unfavorable FX rates pressured margins.” |
| “Top risks you foresee for next quarter?” | “We see supply-chain uncertainty and currency swings as key threats.” |
🔬 Training Setup
- Base – Qwen-2.5 B (GGUF, 4-bit)
- Environment – Google Colab; low-memory 4-bit quantization
- Method – Fine-tuned with Unsloth + PEFT on the curated dataset
🤝 Credits
- Hugging Face and Kaggle for infrastructure and datasets
- The broader open-source community for continual inspiration
🌐 License
Released under the Apache 2.0 license.
- Library
- transformers
- Type
- qwen2
- License
- apache-2.0
- Languages
- en
- Created
- Aug 13, 2025
- Updated
- Oct 28, 2025