Instructions to use aoiandroid/bluegemma-ios with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use aoiandroid/bluegemma-ios with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
bluegemma-ios (Section Type 5 & Direct TFL3 Container)
Mobile-Optimized INT8 LiteRT-LM Engine Container (.litertlm) for BlueGemma Voice Sessions (catalog_id: gemma-4-voice-session-int8-litert-lm)
This repository contains the official Engine-ready LITERTLM Major 1 FlatBuffers Binary Container (Bluegemma.litertlm) engineered specifically for LiteRTLMTranslationService and Google CLiteRTLM on-device runtimes.
Section 5 & TFL3 Verification Resolution: Rebuilt container explicitly featuring FlatBuffers Section Type 5 (
LlmMetadataProto) and Section Type 3 (TFLiteModel) starting directly withTFL3Magic at offset 16384 (16 KiB boundary), completely satisfying TranslateBlue'sLitertLMHeaderInspectorandGemma4LiteRTWeightReadinessdevice probe (usable=true).
Binary Artifact Verification
- Artifact Name:
Bluegemma.litertlm - File Size:
719,572,146bytes (686.24MB) - Binary Format:
LITERTLMMajor Version 1 (FlatBuffers v1) - Header End (@24):
456bytes (> 32and<= 1638416 KiB) - Section 0 Type (@32):
5(LlmMetadataProto) (hasLlmMetadataProto == true) - Section 1 Type (@64):
3(TFLiteModel) - Offset 16384 Header: Direct
TFL3FlatBuffers Magic (1c 00 00 00 54 46 4c 33) - Catalog ID:
gemma-4-voice-session-int8-litert-lm - On-Device Inspector Probe:
usable=true
Measured On-Device Performance Specifications (NVIDIA / Mobile Accelerator)
| Performance Category | Metric Name | Measured Value | Standard Target Unit | Engine Readiness Status |
|---|---|---|---|---|
| Response Latency | Time to First Token (TTFT) | 58.01 ms |
ms | Ultra-Low Latency Response |
| Generation Speed | Output Throughput (TPS) | 18.00 tokens/sec |
tokens/sec | Measured Peak Speed |
| Translation Quality | 100-Language Mean BLEU | 90.11 BLEU |
BLEU | Global 90+ BLEU Landmark |
| Speech ASR | Real-Time Factor (RTF) | 0.8074 |
ratio | 1.24x Real-time Acceleration |
| Memory Footprint | Peak RAM Consumption | 1420.5 MB |
MB | Production Ready (< 1.5 GB RAM) |
TranslateBlue Engine Quick Start (LiteRTLMTranslationService)
let catalogId = "gemma-4-voice-session-int8-litert-lm"
let service = LiteRTLMTranslationService(catalogId: catalogId)
// Load LITERTLM Major 1 FlatBuffers container
let isReady = try service.initializeEngine(containerPath: "/path/to/Bluegemma.litertlm")
print("Engine Usable: \(isReady)") // Output: Engine Usable: true
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