| # 📊 Nifty50GPT-Final — India's First Financial SQL LLM (Offline, Open-Source) | |
| **Nifty50GPT-Final** is a lightweight, offline-ready transformer model fine tuned on structured Indian stock market data. | |
| It was created by [Shubham Sood] at **Student One** to make financial analysis transparent, free, and locally usable — without APIs or cloud dependencies. | |
| This release includes: | |
| - A fully fine-tuned language model that generates **SQL queries** on structured prompts | |
| - A bundled DuckDB database (`student_data.duckdb`) for instant access to 10+ years of data across 50+ NIFTY stocks, Indian indices, and global indices | |
| - Support for both CPU and GPU inference with zero dependencies on internet or live data | |
| --- | |
| ## What's Included | |
| | File | Description | | |
| |---------------------------|-------------------------------------------------| | |
| | `pytorch_model.bin` | Final trained LLM weights (TinyLLaMA-1.1B base) | | |
| | `tokenizer.json` | Tokenizer configuration | | |
| | `config.json` | Model configuration | | |
| | `special_tokens_map.json` | Special token mapping | | |
| | `student_data.duckdb` | Historical Indian financial data | | |
| | `README.md` | You're reading it | | |
| --- | |
| ## How to Run (Inference on CPU or GPU) | |
| ### Local Inference (CPU) | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import torch | |
| model = AutoModelForCausalLM.from_pretrained("StudentOne/Nifty50GPT-Final") | |
| tokenizer = AutoTokenizer.from_pretrained("StudentOne/Nifty50GPT-Final") | |
| tokenizer.pad_token = tokenizer.eos_token | |
| prompt = "### Instruction: What was the net_profit of INFY on 2021-03-31?\n### Output:" | |
| inputs = tokenizer(prompt, return_tensors="pt") | |
| outputs = model.generate( | |
| **inputs, | |
| max_new_tokens=100, | |
| do_sample=False, | |
| pad_token_id=tokenizer.pad_token_id, | |
| eos_token_id=tokenizer.eos_token_id | |
| ) | |
| print(tokenizer.decode(outputs[0])) | |
| ###DuckDB Integration (student_data.duckdb) | |
| All responses from Nifty50GPT-Final are SQL-ready and designed to be run on student_data.duckdb. | |
| How to Use It: | |
| Download DuckDB | |
| Launch DuckDB in terminal or PowerShell: | |
| Paste the SQL query generated by the model, for example: | |
| SELECT value FROM fundamentals | |
| WHERE stock_symbol = 'INFY' | |
| AND metric = 'net_profit' | |
| AND date = DATE '2021-03-31'; | |
| Works instantly. No server, no latency, no setup. | |
| You can extend the dataset while keeping the schema the same | |
| ### What Can I Ask Nifty50-GPT? | |
| 1. Fundamental Metric Lookups | |
| What was the net_profit of TCS on 2022-03-31? | |
| Give me the EPS of INFY on 2023-03-31 | |
| 2. All Fundamentals for a Company | |
| Give me all fundamental metrics for RELIANCE on 2021-03-31 | |
| 3. Year-over-Year Growth | |
| What was the YoY growth of net_profit for HDFCBANK? | |
| 4. CAGR | |
| What was the 5-year CAGR of EPS for INFY from 2017 to 2022? | |
| 5. OHLCV for Stocks or Indices | |
| Give me OHLCV for ASIANPAINT on 2023-01-20 | |
| What was the close price of NIFTY50 on 2022-12-12? | |
| 6. Rolling Metrics | |
| What was the 30-day moving average of close price for INFY on 2023-03-15? | |
| 7. NIFTY-Wide Aggregates | |
| What was the average ROE across all NIFTY50 companies in 2022? | |
| # Supported Stock Symbols | |
| # Use stock symbols, not full company names. Examples: | |
| INFY, TCS, RELIANCE, HDFCBANK, ITC, ASIANPAINT, WIPRO, | |
| KOTAKBANK, ADANIENT, SBIN, LT, TECHM, COALINDIA, NESTLEIND | |
| indian indices- | |
| NIFTY50, SENSEX, NSEBANK, CNXIT, CNXPHARMA, | |
| CNXMEDIA, CNXENERGY, CNXAUTO, CNXMETAL, CNXREALTY | |
| global indices- | |
| S&P 500, Nasdaq 100, Dow Jones, FTSE 100, | |
| DAX, Nikkei 225, Hang Seng, Shanghai Composite | |
| Supported Date Format | |
| YYYY-03-31 - all yearly fundamentals have been shown to be reported on 31st of every march | |
| YYYY-MM-DD for the rest | |
| Tips to Avoid Hallucination | |
| Use exact stock symbols (INFY, not Infosys) | |
| Use supported metric names and date formats | |
| Follow the formats in this README for best results | |
| --- | |
| ## Legal Disclaimer | |
| This model and its associated database are provided **strictly for research, experimentation, and educational purposes** only. | |
| - **Nifty50GPT-Final does not provide financial advice.** | |
| - It does not guarantee accuracy, completeness, or relevance of any output. | |
| - It should not be used to make or inform investment decisions. | |
| - Outputs may be outdated, incorrect, or misinterpreted if used outside the documented prompt structure. | |
| - The included `student_data.duckdb` file is a static, sample database and may not reflect the most current financial data. | |
| ### No Liability | |
| By using this model or dataset: | |
| - You acknowledge that **all responsibility lies with the user**. | |
| - Neither the author(s), contributors, nor affiliated organizations shall be held liable for any outcome, financial or otherwise, resulting from the use or misuse of this model or its outputs. | |
| This project is **not affiliated with the NSE, SEBI, or any regulatory or financial institution.** | |
| This model is **not certified**, **audited**, or**approved** by any authority. | |
| > Use responsibly. Fork freely. Trust nothing. Verify everything. | |
| Credits | |
| Created by Shubham Sood | |
| Maintained by Student One | |
| 🌐 www.studentone.tech | |