File size: 2,286 Bytes
7088a7d 90b9009 7088a7d 90b9009 7088a7d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | ---
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
**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](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
---
## 💡 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
---
|