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Model: Arogyasami/Llama-2-7b-text2sql-finetune
Source: Original Platform

README.md

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+---
+license: mit
+---
+```markdown
+# Llama-2 7B Text-to-SQL (Fine-Tuned with LoRA)
+
+## 📌 Model Overview
+This repository contains a fine-tuned version of Meta's **Llama-3 (8B)**, optimized specifically for the **Natural Language to SQL (Text-to-SQL)** generation task. By converting conversational English into executable SQL queries, this model is designed to bridge the gap between non-technical stakeholders and complex relational databases.
+
+- **Developer:** DanielMartin Arogyasami
+- **Base Model:** Meta-Llama-3-8B
+- **Task:** Text-to-SQL (Code Generation)
+- **Fine-Tuning Methodology:** Low-Rank Adaptation (LoRA) / PEFT
+- **Language:** English, SQL
+- **License:** Meta Llama 3 Community License / MIT (for fine-tuned weights)
+
+## 🎯 Intended Use Cases
+This model is highly specialized for deployment in regulated enterprise environments (e.g., healthcare, finance), where data sovereignty is paramount.
+- **Enterprise Data Retrieval:** Empowering business users to query databases using natural language, significantly reducing reliance on specialized SQL programmers.
+- **Agentic AI Workflows:** Serving as the SQL-generation agent within larger Retrieval-Augmented Generation (RAG) and enterprise AI architectures.
+- **Privacy-Preserving Analytics:** Allowing companies to run text-to-SQL conversions entirely on-premises or within air-gapped Virtual Private Clouds (VPCs). This ensures compliance with HIPAA and FDA CFR Part 11, as no sensitive data is transmitted to external proprietary APIs.
+
+## ⚙️ Technical Details & Training
+This model was trained using **Parameter-Efficient Fine-Tuning (PEFT)**. Specifically, **LoRA (Low-Rank Adaptation)** was applied to the foundational Llama-3 model. This approach adapts the foundational reasoning capabilities of Llama-3 to the strict syntax requirements of SQL generation, while significantly reducing computational overhead.
+
+* **Adapter:** LoRA
+* **Target Modules:** Attention weights (`q_proj`, `v_proj`)
+* **Precision:** FP16 / 4-bit Quantization (QLoRA) supported for edge-deployment.
+* **Architecture:** Auto-Regressive Transformer.
+
+## 🚀 How to Use (Inference)
+You can load this model and run inference using the `transformers` and `peft` libraries from Hugging Face.
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+# Load base model and tokenizer
+base_model_id = "meta-llama/Meta-Llama-3-8B"
+adapter_id = "Arogyasami/Llama-3-8b-text2sql-finetune"
+
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+base_model = AutoModelForCausalLM.from_pretrained(base_model_id, torch_dtype=torch.float16, device_map="auto")
+
+# Load LoRA adapter
+model = PeftModel.from_pretrained(base_model, adapter_id)
+
+# Define your schema and question
+schema = "CREATE TABLE Employees (ID int, Name varchar(255), Department varchar(255), Salary int);"
+question = "What is the average salary of employees in the Sales department?"
+
+prompt = f"Given the following database schema:\n{schema}\n\nWrite a SQL query to answer this question: {question}\n\nSQL:"
+
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+
+# Generate SQL
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## ⚠️ Limitations & Bias
+- **Deterministic Requirement:** Generative AI models can "hallucinate." While fine-tuned to minimize this, generated queries must be validated before execution, especially in clinical contexts where erroneous data retrieval could impact regulatory submissions.
+- **Schema Complexity:** While the model performs exceptionally well on standard schemas, highly complex schemas with dozens of nested joins may reduce accuracy.
+- **Security:** Always execute generated SQL in a safe, read-only environment. The model does not inherently enforce database permissions.
+
+## 🌍 Environmental Impact
+By utilizing LoRA instead of full-parameter fine-tuning, the computational cost and carbon footprint required to train this model were drastically reduced. Its 8B parameter size allows it to be served efficiently on consumer-grade hardware (e.g., NVIDIA RTX 4090 or A10G), democratizing advanced AI access.
+
+## 🤝 Acknowledgements & Citation
+- Meta for the foundational Llama-3 architecture.
+- If this model assists in your research or enterprise architecture, please cite:
+  Arogyasami, DanielMartin. (2024). *Llama-3 8B Text-to-SQL (Fine-Tuned with LoRA)*. Hugging Face.
+```