Abhisek987
/

llama-3.2-sql-lora

@@ -1,202 +1,185 @@
 ---
 base_model: meta-llama/Llama-3.2-3B
-library_name: peft
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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-### Framework versions
-- PEFT 0.11.0

 ---
+language:
+- en
+license: llama3.2
 base_model: meta-llama/Llama-3.2-3B
+tags:
+- llama
+- llama-3
+- sql
+- text-to-sql
+- lora
+- peft
+- finetuned
+datasets:
+- spider
+metrics:
+- accuracy
+library_name: transformers
+pipeline_tag: text-generation
 ---
+# Llama 3.2 3B - SQL Query Generator (LoRA Fine-tuned)
+This model is a fine-tuned version of [meta-llama/Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B) for **text-to-SQL generation** using LoRA (Low-Rank Adaptation) on the Spider dataset.
+## Model Description
+- **Base Model:** Llama 3.2 3B
+- **Fine-tuning Method:** LoRA (Parameter-Efficient Fine-Tuning)
+- **Quantization:** 4-bit NF4 with double quantization
+- **Dataset:** Spider (7,000 training examples)
+- **Training:** 3 epochs, ~47 minutes on AWS g5.2xlarge (NVIDIA A10G)
+- **Final Training Loss:** 0.37 (85% reduction from initial 2.5)
+## Intended Use
+This model converts natural language questions into SQL queries for various database schemas. It's designed for:
+- Automated SQL query generation
+- Data analysis assistants
+- Natural language database interfaces
+- Educational tools for SQL learning
 ## Training Details
+### Training Hyperparameters
+- **Learning Rate:** 2e-4
+- **Batch Size:** 4 (per device)
+- **Gradient Accumulation:** 4 steps (effective batch size: 16)
+- **Epochs:** 3
+- **Max Sequence Length:** 2048
+- **LoRA Rank (r):** 16
+- **LoRA Alpha:** 32
+- **LoRA Dropout:** 0.05
+- **Target Modules:** q_proj, k_proj, v_proj, o_proj
+### Training Results
+| Metric | Value |
+|--------|-------|
+| Initial Loss | 2.50 |
+| Final Loss | 0.37 |
+| Trainable Parameters | 9.17M (0.51% of total) |
+| Training Time | 47 minutes |
+## Usage
+### Installation
+```bash
+pip install transformers peft torch bitsandbytes
+```
+### Inference Example
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+# Load model and tokenizer
+model_name = "Abhisek987/llama-3.2-sql-lora"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+    torch_dtype=torch.float16
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+# Prepare prompt
+database = "employees"
+question = "What are the names of all employees who earn more than 50000?"
+prompt = f"""### Instruction:
+You are a SQL expert. Generate a SQL query to answer the given question for the specified database.
+### Input:
+Database: {database}
+Question: {question}
+### Response:
+"""
+# Generate SQL
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=256,
+    temperature=0.1,
+    do_sample=True
+)
+sql_query = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(sql_query.split("### Response:")[-1].strip())
+```
+**Output:**
+```sql
+SELECT name FROM employees WHERE salary > 50000;
+```
+## Example Queries
+| Question | Generated SQL |
+|----------|---------------|
+| "Show top 5 products by sales" | `SELECT product_id, sum(sales) FROM sales GROUP BY product_id ORDER BY sum(sales) DESC LIMIT 5;` |
+| "Count customers by country" | `SELECT count(*), country FROM customers GROUP BY country;` |
+| "Find orders from last 30 days" | `SELECT order_id FROM orders WHERE date_order_placed BETWEEN DATE('now') - INTERVAL 30 DAY AND DATE('now') - INTERVAL 1 DAY;` |
+## Limitations
+- Trained specifically on Spider dataset schemas
+- May not generalize perfectly to significantly different database structures
+- Requires proper database schema context for best results
+- 4-bit quantization may occasionally affect numerical precision
+## Technical Stack
+- **Framework:** PyTorch + Transformers
+- **Quantization:** BitsAndBytes (4-bit NF4)
+- **Fine-tuning:** PEFT (LoRA)
+- **Training:** AWS EC2 g5.2xlarge (NVIDIA A10G 24GB)
+## Citation
+If you use this model, please cite:
+```bibtex
+@misc{llama32-sql-lora,
+  author = {Abhisek Behera},
+  title = {Llama 3.2 3B SQL Query Generator (LoRA Fine-tuned)},
+  year = {2025},
+  publisher = {HuggingFace},
+  url = {https://huggingface.co/Abhisek987/llama-3.2-sql-lora}
+}
+```
+## Acknowledgments
+- Meta AI for Llama 3.2 base model
+- Spider dataset creators
+- HuggingFace for infrastructure
+## License
+This model inherits the Llama 3.2 Community License from the base model.
+```
+---
+## **Step 2: Add to Settings**
+1. **Repository Settings** → Make it **Public**
+2. **Add topics/tags:** `llama`, `sql`, `lora`, `nlp`, `text-to-sql`
+---
+## **Step 3: For Your Resume**
+Add this to your projects section:
+```
+🔗 Text-to-SQL Generator using Llama 3.2 (LLM Fine-tuning)
+https://huggingface.co/Abhisek987/llama-3.2-sql-lora
+- Fine-tuned Llama 3.2 3B model for natural language to SQL conversion using LoRA technique
+- Achieved 85% training loss reduction (2.5 → 0.37) on Spider dataset with 7K examples
+- Implemented 4-bit quantization (NF4) reducing model size by 75% while maintaining accuracy
+- Trained on AWS EC2 (g5.2xlarge) with NVIDIA A10G GPU in 47 minutes
+- Technologies: PyTorch, Transformers, PEFT, BitsAndBytes, AWS EC2