Update README.md

README.md

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 **IndicFinGPT** is a specialized transformer model, re-engineered from **EleutherAI's GPT-Neo-125M** architecture, which is a GPT-3 class architecture, designed specifically for the **Indian financial market**. The model has undergone **retraining on its top layers** to enhance its performance in providing insights into the **top 100 companies listed in the NIFTY50 Index, BSE, and NSE exchanges**. 
 The primary objective of this model is to **serve the unique needs of Indian stock markets** and **investors engaged in chartless trading**. IndicFinGPT aims to provide insights that could **minimize capital loss and drawdowns** while **maximizing financial ratios** such as the **Sharpe, Sortino, Calmar, Omega, and Treynor Ratios**. Additionally, the model is designed to help in **reducing maximum drawdowns** in financial portfolios, offering a robust AI solution tailored to **India’s dynamic financial landscape**.
 
-## Training Data and Procedure
-
-**IndicFinGPT 125M** utilizes the **Pile dataset** created by EleutherAI and includes the **top 100 tickers** (by volume and liquidity) from Indian stock markets, covering data from **January 1, 2018, to October 30, 2024**. This dataset encompasses diverse market periods, including **pre-COVID-19 (stable), COVID-19 (volatile), and post-COVID-19 (recovery phase)**. Such comprehensive data exposure allows the model to recognize **problem-solution patterns across various bull and bear runs**.
-The training data also incorporates **local influences** such as cultural factors and **market-specific volatility**, enhancing its ability to perform **automated technical analysis** for chartless trading. Key capabilities include identifying **classical chart patterns** using technical analysis, conducting **earnings analysis**, interpreting **market sentiment** from multiple sources, and **assessing risks**, all aimed at **improving decision-making for Indian investors**.
-This model weights were obtained after **310 billion tokens over 692,380 steps**. It utilized 4-bit Quantized Low-Rank Adoption (PEFT) method on top of  the masked autoregressive language model architecture of Neo, utilizing cross-entropy loss, F1, Accuracy, Precision, recall,Pattern Detection Rate, and Cross-Entropy Loss as performance metrics.
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 ## First Indic-Stock Small Language Model Focused Top 100 Companies Listed in NSE and BSE Stock Exchanges 
 
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   <strong>भारतीय बाजार की शीर्ष 100 कंपनियों का वित्तीय विश्लेषण करने वाला पहला Small Language Model</strong>
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+## Training Data and Procedure
+
+**IndicFinGPT 125M** utilizes the **Pile dataset** created by EleutherAI and includes the **top 100 tickers** (by volume and liquidity) from Indian stock markets, covering data from **January 1, 2018, to October 30, 2024**. This dataset encompasses diverse market periods, including **pre-COVID-19 (stable), COVID-19 (volatile), and post-COVID-19 (recovery phase)**. Such comprehensive data exposure allows the model to recognize **problem-solution patterns across various bull and bear runs**.
+The training data also incorporates **local influences** such as cultural factors and **market-specific volatility**, enhancing its ability to perform **automated technical analysis** for chartless trading. Key capabilities include identifying **classical chart patterns** using technical analysis, conducting **earnings analysis**, interpreting **market sentiment** from multiple sources, and **assessing risks**, all aimed at **improving decision-making for Indian investors**.
+This model weights were obtained after **310 billion tokens over 692,380 steps**. It utilized 4-bit Quantized Low-Rank Adoption (PEFT) method on top of  the masked autoregressive language model architecture of Neo, utilizing cross-entropy loss, F1, Accuracy, Precision, recall,Pattern Detection Rate, and Cross-Entropy Loss as performance metrics.
 
 
 ## Key Highlights