codegemma-7b-it-int4-litertlm

CodeGemma 7B IT (INT4 LiteRT LM Model for Android)

This repository contains Google's CodeGemma 7B Instruction-Tuned (google/codegemma-7b-it) quantized to INT4 and exported into the .litertlm model bundle format for on-device deployment on Android using Google's LiteRT runtime (formerly TensorFlow Lite / AI Edge Torch).

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Model Summary

• Developed by: Google DeepMind / Google AI Edge
• Model Architecture: Gemma Transformer (GemmaForCausalLM)
• Base Checkpoint: google/codegemma-7b-it
• Quantization: INT4 Weight-Only Dynamic Quantization (dynamic_wi4_afp32)
• Export Tool: litert-torch (litert_torch.generative.export_hf)
• Target Platform: Android (ARM64-v8a / Qualcomm Snapdragon / MediaTek / Google Tensor)
• Output File Format: .litertlm (Unified model flatbuffer + tokenizer + prompt templates)

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Technical Specifications

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On-Device Android Integration (LiteRT-LM SDK)

1. Model Deployment

Place the downloaded .litertlm file inside your Android app's assets/ directory or internal storage directory.

2. Dependencies (build.gradle.kts)

dependencies {
    implementation("com.google.ai.edge.litert:litert-lm:1.0.0")
}

3. Execution Code (Kotlin)

import com.google.ai.edge.litert.lm.LiteRtLmEngine
import com.google.ai.edge.litert.lm.ModelOptions
import java.io.File

fun runCodeGemma(context: Context, userPrompt: String): String {
    val modelFile = File(context.filesDir, "codegemma-7b-it-int4.litertlm")
    
    val options = ModelOptions.builder()
        .setModelPath(modelFile.absolutePath)
        .build()

    val engine = LiteRtLmEngine.create(options)
    
    // CodeGemma instruction format is automatically handled by the .litertlm bundle metadata
    val response = engine.generate(userPrompt)
    
    engine.close()
    return response
}

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Prompt Template Format

The .litertlm bundle automatically packages the official CodeGemma chat template:

<bos><start_of_turn>user
Write a Kotlin function to check prime numbers.<end_of_turn>
<start_of_turn>model

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Citation & Attribution

If you use this model in your research or applications, please cite the official Google CodeGemma paper and Hugging Face repository:

@article{codegemma2024,
  title={CodeGemma: Open Code Models},
  author={Google CodeGemma Team},
  year={2024}
}

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Converted using Google LiteRT Torch (litert-torch).