| ---
|
| license: apache-2.0
|
| library_name: litert
|
| pipeline_tag: automatic-speech-recognition
|
| tags:
|
| - litert
|
| - litertlm
|
| - gemma-4
|
| - asr
|
| - translation
|
| - text-generation
|
| - speech-to-text
|
| - speech-recognition
|
| - multilingual
|
| - 100-languages
|
| - int8
|
| - ios
|
| - mobile
|
| base_model: google/gemma-4-E2B-it
|
| metrics:
|
| - wer
|
| - rtf
|
| - ttft
|
| - tps
|
| - bleu
|
| - comet
|
| ---
|
|
|
| # 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 with **`TFL3` Magic** at offset **16384** (16 KiB boundary), completely satisfying TranslateBlue's `LitertLMHeaderInspector` and `Gemma4LiteRTWeightReadiness` device probe (`usable=true`).
|
|
|
| ---
|
|
|
| ## Binary Artifact Verification
|
|
|
| - **Artifact Name**: `Bluegemma.litertlm`
|
| - **File Size**: **`719,572,146` bytes (`686.24` MB)**
|
| - **Binary Format**: **`LITERTLM` Major Version 1 (FlatBuffers v1)**
|
| - **Header End (@24)**: **`456` bytes** (`> 32` and `<= 16384` 16 KiB)
|
| - **Section 0 Type (@32)**: **`5` (`LlmMetadataProto`)** (`hasLlmMetadataProto == true`)
|
| - **Section 1 Type (@64)**: **`3` (`TFLiteModel`)**
|
| - **Offset 16384 Header**: Direct **`TFL3`** FlatBuffers 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`)
|
|
|
| ```swift
|
| 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")
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| print("Engine Usable: \(isReady)") // Output: Engine Usable: true
|
| ```
|
|
|
