all-MiniLM-L6-v2-tflite

all-MiniLM-L6-v2-tflite

A TensorFlow Lite version of the popular sentence-transformers/all-MiniLM-L6-v2 model, optimized for mobile and embedded devices.

Model Overview

This model is a lightweight sentence transformer that maps sentences and paragraphs to a 384-dimensional dense vector space. It's designed for efficient on-device inference while maintaining good semantic understanding capabilities.

Key Features

• Optimized for Mobile: Converted to TensorFlow Lite format for fast inference on Android, iOS, and embedded devices
• Lightweight: Significantly reduced model size compared to the original PyTorch version
• High Performance: Maintains strong semantic similarity performance with ~22M parameters
• Cross-Platform: Compatible with Android Studio, Flutter, and other mobile development frameworks
• Privacy-First: Enables completely offline text embeddings without sending data to external servers

Use Cases

• Semantic Search: Find relevant documents or passages based on meaning rather than keywords
• Text Clustering: Group similar sentences or documents together
• Similarity Detection: Compare semantic similarity between text passages
• Information Retrieval: Build recommendation systems based on text content
• Mobile NLP Apps: Add intelligent text processing to mobile applications
• Edge AI: Deploy text understanding capabilities on IoT and embedded devices

Model Specifications

Quick Start

Android/Kotlin Integration

// Load the TFLite model in your Android app
val interpreter = Interpreter(loadModelFile())

// Prepare input tensors
val inputShape = intArrayOf(1, maxSequenceLength)
val inputBuffer = TensorBuffer.createFixedSize(inputShape, DataType.INT32)

// Run inference
interpreter.run(inputBuffer.buffer, outputBuffer.buffer)

Flutter Integration

// Use with tflite_flutter package
import 'package:tflite_flutter/tflite_flutter.dart';

// Load and run inference
final interpreter = await Interpreter.fromAsset('all_minilm_l6_v2.tflite');
final output = List.filled(384, 0.0).reshape([1, 384]);
interpreter.run(input, output);

Python Validation

# Verify model outputs match original
import tensorflow as tf

# Load TFLite model
interpreter = tf.lite.Interpreter(model_path="model.tflite")
interpreter.allocate_tensors()

# Get input and output tensors
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# Run inference
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])

Input/Output Format

Input

• Tokenized text sequences as integer arrays
• Attention masks for proper sequence handling
• Maximum length: 256 tokens (longer sequences are truncated)

Output

• 384-dimensional float vectors representing sentence embeddings
• L2 normalized for direct cosine similarity calculations
• Ready for downstream tasks like clustering or search

Performance Benchmarks

Requirements

• TensorFlow Lite Runtime (mobile deployment)
• Android API Level 16+ (for Android apps)
• iOS 11.0+ (for iOS apps)
• Flutter 2.0+ (for cross-platform apps)

Contributing

Found an issue or want to improve the model? Feel free to:

• Open an issue for bugs or suggestions
• Submit pull requests for improvements
• Share your use cases and results

License

This model follows the same license terms as the original sentence-transformers/all-MiniLM-L6-v2 model.

Related Models

• Original Model: sentence-transformers/all-MiniLM-L6-v2
• Larger Variant: sentence-transformers/all-MiniLM-L12-v2
• Higher Dimension: sentence-transformers/all-mpnet-base-v2

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Enabling powerful text understanding directly on your device