| --- |
| base_model: unsloth/qwen2.5-7b-unsloth-bnb-4bit |
| tags: |
| - text-generation-inference |
| - transformers |
| - unsloth |
| - qwen2 |
| - gguf |
| license: apache-2.0 |
| language: |
| - en |
| --- |
| |
| # Finance Earnings Call Q&A Bot |
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| **Fine-tuned LLM for financial question-answering and earnings call simulation** |
| By Mrinal Kumar |
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| ## 🚀 Model Overview |
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| 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: |
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| - Summarize complex financial calls into key insights |
| - Simulate Q&A between analysts and CFOs/CEOs |
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| ## 🏆 Example Use Cases |
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| - **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 |
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| ## 🗂️ Training Data |
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| - **Dataset:** Manually curated Q&A pairs extracted from publicly available earnings calls ([Kaggle Earnings Call Datasets](https://www.kaggle.com/)) |
| - **Format:** Each example consists of an analyst question (`input`) and a CFO/CEO response (`output`) |
| - **Size:** 1,000+ Q&A pairs for diverse scenarios |
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| --- |
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| ## 💡 Example Questions |
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| | 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. | |
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| --- |
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| ## 🔬 Training Details |
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| * **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 |
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| --- |
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| ## 🤝 Acknowledgements |
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| * **Hugging Face & Kaggle** for model hosting and data |
| * **Open source communities** for technical guidance |
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| --- |
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| ## 🌐 License |
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| Apache 2.0 |
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| --- |
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