CPU / GPU

CPU and GPU-accelerated inference for Nexa Android SDK using GGUF format models.

LLM Usage

Large Language Models for text generation and chat applications.

Streaming Conversation

We support CPU/GPU inference for GGUF format models. You can pick any GGUF models from the community and run with the cpu_gpu plugin.

LlmWrapper.builder()
    .llmCreateInput(
        LlmCreateInput(
            model_name = "", // For GGUF CPU/GPU models, leave model_name empty.
            model_path = "<your-model-path>",
            config = ModelConfig(
                nCtx = 4096,
                nGpuLayers = 0  // 0 for CPU, > 0 for GPU
            ),
            plugin_id = "cpu_gpu",
            device_id = null  // null for CPU, "gpu" for GPU
        )
    )
    .build()
    .onSuccess { llmWrapper = it }
    .onFailure { error -> println("Error: ${error.message}") }

val chatList = arrayListOf(ChatMessage("user", "What is AI?"))

llmWrapper.applyChatTemplate(chatList.toTypedArray(), null, false).onSuccess { template ->
    val genConfig = GenerationConfig(maxTokens = 2048)
    
    llmWrapper.generateStreamFlow(template.formattedText, genConfig).collect { result ->
        when (result) {
            is LlmStreamResult.Token -> println(result.text)
            is LlmStreamResult.Completed -> println("Done!")
            is LlmStreamResult.Error -> println("Error: ${result.throwable}")
        }
    }
}

CPU/GPU Configuration

Control whether your model runs on CPU or GPU using a combination of device_id and nGpuLayers:

GPU Execution Requirements:

• device_id must be set to "gpu"
• nGpuLayers must be greater than 0 (typically set to 999 to offload all layers)

CPU Execution:

• device_id is null (default)
• OR nGpuLayers is 0

Example: Running on GPU

LlmWrapper.builder()
    .llmCreateInput(
        LlmCreateInput(
            model_name = "",
            model_path = "<your-model-path>",
            config = ModelConfig(
                nCtx = 4096,
                nGpuLayers = 999  // Offload all layers to GPU
            ),
            plugin_id = "cpu_gpu",
            device_id = "gpu"  // Use GPU device
        )
    )
    .build()
    .onSuccess { llmWrapper = it }
    .onFailure { error -> println("Error: ${error.message}") }

Example: Running on CPU (Default)

LlmWrapper.builder()
    .llmCreateInput(
        LlmCreateInput(
            model_name = "",
            model_path = "<your-model-path>",
            config = ModelConfig(
                nCtx = 4096,
                nGpuLayers = 0  // All on CPU
            ),
            plugin_id = "cpu_gpu",
            device_id = null  // Default to CPU
        )
    )
    .build()
    .onSuccess { llmWrapper = it }

---

Multimodal Usage

Vision-Language Models for image understanding and multimodal applications.

Streaming Conversation

We support CPU/GPU inference for GGUF format models.

VlmWrapper.builder()
    .vlmCreateInput(
        VlmCreateInput(
            model_name = "",  // For GGUF on CPU/GPU, leave empty (no model name needed)
            model_path = <your-model-path>,
            mmproj_path = <your-mmproj-path>,  // vision projection weights
            config = ModelConfig(
                nCtx = 4096,
                nGpuLayers = 0  // 0 for CPU, > 0 for GPU
            ),
            plugin_id = "cpu_gpu",
            device_id = null  // null for CPU, "gpu" for GPU
        )
    )
    .build()
    .onSuccess { vlmWrapper = it }
    .onFailure { error -> 
        println("Error: ${error.message}")
    }

// Use the loaded VLM with image and text
val contents = listOf(
    VlmContent("image", <your-image-path>),
    VlmContent("text", <your-text>)
)

val chatList = arrayListOf(VlmChatMessage("user", contents))

vlmWrapper.applyChatTemplate(chatList.toTypedArray(), null, false).onSuccess { template ->
    // Create base GenerationConfig with maxTokens
    val baseConfig = GenerationConfig(maxTokens = 2048)
    
    // Inject media paths from chatList into config
    val configWithMedia = vlmWrapper.injectMediaPathsToConfig(
        chatList.toTypedArray(),
        baseConfig
    )
    
    vlmWrapper.generateStreamFlow(template.formattedText, configWithMedia).collect { result ->
        when (result) {
            is LlmStreamResult.Token -> println(result.text)
            is LlmStreamResult.Completed -> println("Done!")
            is LlmStreamResult.Error -> println("Error: ${result.throwable}")
        }
    }
}

---

ASR Usage

Automatic Speech Recognition for audio transcription.

Basic Usage

We support CPU inference for whisper.cpp models.

// Load ASR model for whisper.cpp inference
AsrWrapper.builder()
    .asrCreateInput(
        AsrCreateInput(
            model_name = "",  // Empty for whisper.cpp
            model_path = <your-model-path>,  // e.g., "ggml-base-q8_0.bin"
            config = ModelConfig(
                nCtx = 4096  // Context size (use nCtx instead of max_tokens)
            ),
            plugin_id = "whisper_cpp"  // Use whisper.cpp backend
        )
    )
    .build()
    .onSuccess { asrWrapper = it }
    .onFailure { error -> 
        println("Error: ${error.message}")
    }

// Transcribe audio file
asrWrapper.transcribe(
    AsrTranscribeInput(
        audioPath = <your-audio-path>,  // Path to .wav file (16kHz recommended)
        language = "en",                // Language code: "en", "zh", "es", etc.
        timestamps = null               // Optional timestamp format
    )
).onSuccess { result ->
    println("Transcription: ${result.result.transcript}")
}

---

TTS Usage

Text-to-Speech synthesis for converting text into natural-sounding speech.

Basic Usage

We support CPU inference for TTS models in GGUF format.

// Load TTS model for CPU inference
TtsWrapper.builder()
    .ttsCreateInput(
        TtsCreateInput(
            model_name = "",  // Empty for CPU/GPU models
            model_path = <your-model-path>,  // Path to TTS model (e.g., Kokoro GGUF model)
            config = ModelConfig(
                nCtx = 4096  // Context size
            ),
            plugin_id = "tts_cpp"  // Use TTS backend
        )
    )
    .build()
    .onSuccess { ttsWrapper = it }
    .onFailure { error -> 
        println("Error: ${error.message}")
    }

// Synthesize speech from text
ttsWrapper.synthesize(
    TtsSynthesizeInput(
        textUtf8 = "Hello, this is a text to speech demo using Nexa SDK.",
        outputPath = <your-output-audio-path>  // Path where audio will be saved (e.g., "/path/to/output.wav")
    )
).onSuccess { result ->
    println("Speech synthesized successfully!")
    println("Audio saved to: ${result.outputPath}")
}.onFailure { error ->
    println("Error during synthesis: ${error.message}")
}

---

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