Update README.md

README.md

@@ -11,12 +11,67 @@ language:
 - en
 ---
 
-## Uploaded  model 
+# Finance Earnings Call Q&A Bot
 
-- **Developed by:** 2random4u
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/qwen2.5-7b-unsloth-bnb-4bit
+**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
+
+---
 
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)