drz-sql-llama3

This model is a fine-tuned version of Llama 3 (8B) for generating SQL queries specific to the Daraz e-commerce platform.

Model Description

• Base Model: Llama 3 8B (4-bit quantized)
• Fine-tuning Method: LoRA (Low-Rank Adaptation)
• Training Data: 20 Daraz-specific SQL query examples
• Use Case: Converting natural language questions to SQL queries for Daraz analytics

Training Details

• Framework: Unsloth
• LoRA Rank: 16
• Training Steps: 100
• Batch Size: 2
• Gradient Accumulation: 4
• Learning Rate: 0.0002

Key Features

This model understands Daraz-specific:

• Table schemas (e.g., daraz_cdm.dwd_drz_trd_core_df, daraz_cdm.dwd_drz_prd_sku_extension)
• Business logic (Choice classification, KAM assignments, industry mapping)
• Query patterns (MAX_PT for partitions, DATEADD for date filtering)
• Metrics (GMV, L7/L30 calculations, order types)

Usage

from unsloth import FastLanguageModel

# Load model
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "Bilal326/drz-sql-llama3",
    max_seq_length = 2048,
    dtype = None,
    load_in_4bit = True,
)

FastLanguageModel.for_inference(model)

# Generate SQL
alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

prompt = alpaca_prompt.format(
    "Generate SQL for the following request:",
    "Get total GMV for last 30 days in Pakistan",
    ""
)

inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.5)
print(tokenizer.decode(outputs[0]))

Example Queries

The model can handle:

• Simple aggregations: "Get total GMV and orders for last 30 days"
• Complex joins: "Get seller performance with KAM assignments"
• Time-based analysis: "Show monthly GMV trend by industry"
• Advanced logic: "Compare Choice vs Non-Choice GMV in Crossborder"

Limitations

• Trained specifically for Daraz schema and business logic
• May not generalize to other SQL dialects or schemas
• Requires Daraz-specific tables to be available

Training Dataset

Custom dataset of 20 SQL query examples covering:

• Revenue and GMV analysis
• Product performance metrics
• Seller segmentation
• Category and brand analysis
• Time-based trends

Citation

If you use this model, please cite:

@misc{drz-sql-llama3,
  author = {Bilal326},
  title = {drz-sql-llama3: Daraz SQL Generation Model},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/Bilal326/drz-sql-llama3}
}

Acknowledgments

• Built with Unsloth
• Based on Meta's Llama 3
• Fine-tuned for Daraz e-commerce analytics