---
license: llama3.1
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
- llama-3.1
- sql
- fine-tuned
- agent
- unsloth
- text-generation
language:
- en
pipeline_tag: text-generation
datasets:
- custom
metrics:
- loss
---

# Better SQL Agent - Llama 3.1 8B

## Training Results
- **Training Samples**: 19,480 (SQL analytics + technical conversations)
- **Hardware**: NVIDIA 4x A10G GPU (96GB VRAM)

## Model Description
This is a high-performance fine-tuned version of **Meta-Llama-3.1-8B-Instruct**, specifically optimized for:
- **SQL query generation and optimization**
- **Data analysis and insights**  
- **Technical assistance and debugging**
- **Tool-based workflows**

## Training Configuration
- **Base Model**: `meta-llama/Llama-3.1-8B-Instruct`
- **Training Method**: LoRA (Low-Rank Adaptation)
  - Rank: 16, Alpha: 32, Dropout: 0.05
- **Quantization**: 4-bit with BF16 training precision
- **Context Length**: 128K tokens (extended from base)
- **Optimizer**: AdamW with cosine scheduling

## Quick Start
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load the fine-tuned model
model_name = "abhishekgahlot/better-sql-agent-llama"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)

# Generate SQL query
prompt = """<|begin_of_text|><|start_header_id|>user<|end_header_id|>

Create a SQL query to find the top 5 customers by total revenue in 2024:

<|eot_id|><|start_header_id|>assistant<|end_header_id|>

"""

inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=256,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], 
                          skip_special_tokens=True)
print(response)
```

## Performance Metrics
| Metric | Value |
|--------|-------|
| **Starting Loss** | 1.53 |
| **Final Loss** | 0.0508 |
| **Loss Reduction** | **96.7%** |
| **Training Time** | 8.9 hours |

## Use Cases
- **SQL Generation**: Create complex queries from natural language
- **Data Analysis**: Generate insights and analytical queries
- **Code Assistance**: Debug and optimize SQL code
- **Technical Support**: Answer database and analytics questions
- **Learning Aid**: Explain SQL concepts and best practices

## Training Data
The model was trained on a curated dataset of **19,480 high-quality examples** including:
- SQL query generation tasks
- Data analysis conversations
- Technical problem-solving dialogues
- Tool usage patterns and workflows

## Optimization Features
- **4-bit Quantization**: Reduced memory footprint
- **Flash Attention**: Optimized attention mechanism
- **Mixed Precision**: BF16 training for efficiency

## License
This model inherits the **Llama 3.1 license** from the base model. Please review the [official license](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE) for usage terms.

## Acknowledgments
- Based on Meta's Llama 3.1 8B Instruct model

## Model Card Contact
For questions about this model, please open an issue in the repository or contact the model author.

---