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README.md

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 ---
+language:
+- en
+license: apache-2.0
+tags:
+- text-to-sql
+- sql
+- tally
+- accounting
+- erp
+- llama-3
+- sqlcoder
+- postgresql
+- finance
 base_model: defog/llama-3-sqlcoder-8b
 library_name: peft
+pipeline_tag: text-generation
 ---
 
-# Model Card for Model ID
+# Tally SQLCoder - Fine-tuned for TallyPrime ERP
 
-<!-- Provide a quick summary of what the model is/does. -->
+**Created by:** Jay Viramgami
 
+A fine-tuned LLaMA 3 SQLCoder model specialized for converting natural language questions to PostgreSQL queries for **TallyPrime ERP** systems.
 
+## 🎯 Model Description
 
-## Model Details
+This model is specifically trained to understand accounting and business terminology used in TallyPrime ERP and generate accurate SQL queries for a PostgreSQL database migrated from Tally.
 
-### Model Description
+### Key Features
 
-<!-- Provide a longer summary of what this model is. -->
+- 🏦 **Accounting Domain Expertise** - Understands financial terms, GST, vouchers, ledgers
+- 📊 **28 Database Tables** - Covers all master and transaction tables from Tally
+- 🎓 **ICAI Compliant** - Based on Indian accounting standards
+- 🚀 **Fast Inference** - Optimized with QLoRA for efficient deployment
+- 💯 **High Accuracy** - Fine-tuned on 5,000+ Tally-specific query pairs
+
+### Use Cases
+
+- Customer receivables and vendor payables analysis
+- Sales and purchase reporting
+- Inventory and stock management queries
+- GST and tax compliance reports
+- Financial statements (Profit & Loss, Balance Sheet)
+- Voucher and transaction searches
+
+## 📊 Model Details
+
+- **Base Model:** [defog/llama-3-sqlcoder-8b](https://huggingface.co/defog/llama-3-sqlcoder-8b)
+- **Fine-tuning Method:** QLoRA (4-bit quantization)
+- **Training Data:** 5,000 synthetic Tally accounting text-to-SQL pairs
+- **Target Database:** PostgreSQL (Tally migration schema with 28 tables)
+- **Training Platform:** Kaggle (NVIDIA T4 GPU)
+- **Training Time:** ~4 hours
+- **Final Training Loss:** 0.05-0.07
+
+### Query Categories Supported
+
+| Category | Templates | Examples |
+|----------|-----------|----------|
+| Simple Filters | 20 | "Show all customers", "List bank accounts" |
+| Date Ranges | 20 | "Sales in March 2024", "Payments this quarter" |
+| Aggregations | 25 | "Total sales amount", "Top 10 customers" |
+| Joins | 25 | "Customer-wise outstanding", "Item sales by godown" |
+| Accounting | 20 | "P&L items", "Assets and liabilities" |
+| GST/Tax | 15 | "GST collected", "TDS deducted" |
+| Inventory | 15 | "Stock movements", "Items with zero balance" |
+| Financial Statements | 10 | "Trial balance", "Balance sheet data" |
+
+## 🚀 Quick Start
+
+### Installation
+
+```bash
+pip install transformers peft torch accelerate bitsandbytes
+```
+
+### Basic Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+
+# Load base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    "defog/llama-3-sqlcoder-8b",
+    device_map="auto",
+    torch_dtype=torch.float16
+)
+
+# Load fine-tuned adapter
+model = PeftModel.from_pretrained(base_model, "jaykv/tally-sqlcoder-finetuned")
+tokenizer = AutoTokenizer.from_pretrained("jaykv/tally-sqlcoder-finetuned")
+
+# Generate SQL
+question = "Show all customers with outstanding balance above 50000"
+schema = """CREATE TABLE mst_ledger (
+    name VARCHAR(1024),
+    parent VARCHAR(1024),
+    closing_balance DECIMAL(17,2)
+);"""
+
+prompt = f"""### Task
+Generate a SQL query to answer [QUESTION]{question}[/QUESTION]
+
+### Database Schema
+The query will run on a database with the following schema:
+{schema}
+
+### Answer
+Given the database schema, here is the SQL query that answers [QUESTION]{question}[/QUESTION]
+[SQL]"""
+
+inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=2048).to(model.device)
+
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=300,
+        temperature=0.1,
+        do_sample=True,
+        pad_token_id=tokenizer.eos_token_id
+    )
+
+result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+sql = result.split("[SQL]")[-1].strip()
+print(sql)
+```
+
+**Output:**
+```sql
+SELECT name, closing_balance
+FROM mst_ledger
+WHERE parent = 'Sundry Debtors'
+AND closing_balance > 50000
+```
+
+## 📝 Example Queries
+
+### Simple Customer Query
+**Question:** "Show all customers"
+**Generated SQL:**
+```sql
+SELECT name FROM mst_ledger WHERE parent = 'Sundry Debtors'
+```
+
+### Sales Analysis
+**Question:** "What is the total sales amount for March 2024?"
+**Generated SQL:**
+```sql
+SELECT SUM(ABS(amount)) as total_sales
+FROM trn_accounting ta
+JOIN trn_voucher tv ON ta.guid = tv.guid
+WHERE tv.voucher_type = 'Sales'
+AND tv.date BETWEEN '2024-03-01' AND '2024-03-31'
+```
+
+### Top Customers
+**Question:** "Show top 10 customers by sales"
+**Generated SQL:**
+```sql
+SELECT tv.party_name, SUM(ABS(ta.amount)) as total_sales
+FROM trn_voucher tv
+JOIN trn_accounting ta ON tv.guid = ta.guid
+WHERE tv.voucher_type = 'Sales'
+GROUP BY tv.party_name
+ORDER BY total_sales DESC
+LIMIT 10
+```
+
+### GST Query
+**Question:** "Show GST payable amount"
+**Generated SQL:**
+```sql
+SELECT name, closing_balance
+FROM mst_ledger
+WHERE parent = 'Duties & Taxes'
+AND name LIKE '%GST%'
+```
+
+## 🗄️ Database Schema
+
+The model is trained on a PostgreSQL schema with **28 tables** from TallyPrime:
+
+### Master Tables (15)
+- `mst_ledger` - Customers, vendors, banks, expenses, incomes
+- `mst_group` - Account group hierarchy
+- `mst_stock_item` - Inventory items with GST details
+- `mst_stock_group` - Stock categories
+- `mst_vouchertype` - Voucher type definitions
+- `mst_godown` - Warehouse locations
+- `mst_cost_centre` - Cost centers
+- And 8 more...
+
+### Transaction Tables (13)
+- `trn_voucher` - All financial transactions
+- `trn_accounting` - Ledger-wise entries
+- `trn_inventory` - Item-wise stock movements
+- `trn_bill` - Bill allocations
+- `trn_bank` - Bank transaction details
+- And 8 more...
+
+## 📈 Training Details
+
+### Dataset
+- **Size:** 5,000 text-to-SQL pairs
+- **Source:** Synthetically generated using 150 query templates
+- **Split:** 90/10 train/test
+- **Categories:** 8 query types covering all Tally operations
+
+### Training Configuration
+- **Method:** QLoRA (Quantized Low-Rank Adaptation)
+- **Quantization:** 4-bit (NF4)
+- **LoRA Rank:** 16
+- **LoRA Alpha:** 32
+- **Target Modules:** q_proj, k_proj, v_proj, o_proj
+- **Batch Size:** 2 per device
+- **Gradient Accumulation:** 4 steps
+- **Learning Rate:** 2e-4
+- **Epochs:** ~3 (1,600 steps)
+- **Optimizer:** PagedAdamW 8-bit
+- **Max Sequence Length:** 2048 tokens
+
+### Hardware
+- **Platform:** Kaggle Notebooks
+- **GPU:** NVIDIA T4 (16GB)
+- **Training Time:** ~4 hours
+
+## 📊 Performance
+
+- **Valid SQL Syntax:** >95%
+- **Keyword Match:** >85%
+- **Exact Match (normalized):** >70%
+
+## ⚠️ Limitations
+
+- **Tally-Specific:** Optimized for TallyPrime PostgreSQL schema
+- **PostgreSQL Only:** SQL generated for PostgreSQL dialect
+- **Schema Required:** Needs database schema in the prompt
+- **Context Window:** Limited to 2048 tokens
+- **Custom Schemas:** May require additional fine-tuning for non-Tally schemas
+
+## 🔧 Deployment Tips
+
+### For Production Use:
+1. **Add validation** - Verify generated SQL before execution
+2. **Read-only mode** - Restrict to SELECT queries only
+3. **Query timeout** - Set execution time limits
+4. **Error handling** - Catch and handle syntax errors
+5. **Logging** - Track all queries for audit
+
+### Optimization:
+- Use GPU for faster inference (2-3 seconds per query)
+- CPU inference works but is slower (~10-15 seconds)
+- Consider caching frequently asked queries
+
+## 📄 License
+
+This model is released under the **Apache 2.0** license, inheriting from the base model.
+
+## 🙏 Acknowledgments
+
+- **Base Model:** [defog/llama-3-sqlcoder-8b](https://huggingface.co/defog/llama-3-sqlcoder-8b) by Defog.ai
+- **Training Standards:** ICAI (Institute of Chartered Accountants of India) Foundation Course
+- **Platform:** Trained on Kaggle's free GPU infrastructure
+
+## 📧 Contact
+
+**Author:** Jay Viramgami
+
+For questions, feedback, or collaboration inquiries, please open an issue on the model's discussion page.
+
+## 🔗 Related Resources
+
+- [TallyPrime ERP](https://tallysolutions.com/)
+- [Defog SQLCoder](https://github.com/defog-ai/sqlcoder)
+- [PEFT Library](https://github.com/huggingface/peft)
 
+---
 
+## Citation
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
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-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
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-
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-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
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-## Training Details
-
-### Training Data
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-### Training Procedure
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-
-#### Preprocessing [optional]
-
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-
-
-#### Training Hyperparameters
-
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-
-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-#### Factors
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-## Environmental Impact
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-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-### Compute Infrastructure
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-## Citation [optional]
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-## Glossary [optional]
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-## Model Card Authors [optional]
-
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+If you use this model in your work, please cite:
 
-## Model Card Contact
+```bibtex
+@misc{tally-sqlcoder-finetuned,
+  author = {Jay Viramgami},
+  title = {Tally SQLCoder - Fine-tuned for TallyPrime ERP},
+  year = {2024},
+  publisher = {HuggingFace},
+  url = {https://huggingface.co/jaykv/tally-sqlcoder-finetuned}
+}
+```
 
-[More Information Needed]
-### Framework versions
+---
 
-- PEFT 0.10.0
+**Model Card created by Jay Viramgami | March 2024**