README.md

metadata

title: Llama Address Intelligence
emoji: 🦙
colorFrom: purple
colorTo: pink
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: apache-2.0

Llama 3.2-1B Address Intelligence Demo

This Space demonstrates the capabilities of shiprocket-ai/open-llama-1b-address-completion, a fine-tuned Llama 3.2-1B model specialized for Indian address processing.

What it does

This application showcases three main capabilities, with varying performance levels:

1. Component Extraction ⭐ BEST PERFORMANCE - Parse addresses into structured components (building, locality, pincode, etc.)
2. Address Completion ⚠️ LIMITED - Complete partial addresses (trained on limited data)
3. Format Standardization ⚠️ LIMITED - Convert informal addresses to standardized format (trained on limited data)

Note: This model performs best at entity extraction. The completion and standardization features have limited training data and may not always produce optimal results.

Features

• Lightweight: Only 1.24B parameters for fast inference
• Specialized: Fine-tuned specifically for Indian address patterns
• Versatile: Handles multiple address intelligence tasks
• Interactive: Three separate tabs for different use cases
• Real-time: Optimized for quick responses

How to use

Component Extraction

1. Go to the "Extract Components" tab
2. Enter a complete address
3. Click "Extract Components" to see structured breakdown

Address Completion

1. Go to the "Complete Address" tab
2. Enter a partial address
3. Click "Complete Address" to see AI completion

Format Standardization

1. Go to the "Standardize Format" tab
2. Enter an informal or messy address
3. Click "Standardize Format" to see cleaned version

Example addresses

• Complete: C-704, Gayatri Shivam, Thakur Complex, Kandivali East, 400101
• Partial: C-704, Gayatri Shivam, Thakur Complex
• Informal: c704 gayatri shivam thakur complex kandivali e 400101

Model Information

• Base Model: meta-llama/Llama-3.2-1B-Instruct
• Parameters: 1.24B
• Model Size: ~2.47GB
• Max Context: 131K tokens
• License: Apache 2.0

Supported Components

The model can handle:

• Building names, localities, pincodes
• Cities, states, sub-localities
• Road names, landmarks
• Various Indian address formats

Performance Notes

⭐ Entity Extraction: Excellent performance - primary use case ⚠️ Address Completion: Limited training data - experimental feature
⚠️ Standardization: Limited training data - experimental feature

Recommendation: Use this model primarily for address component extraction where it performs best.