LiteRT-LM
Understand the key differences between LiteRT-LM Engine, Session, and Conversation objects. Learn how Engine creates Sessions for low-level control and Conversations for high-level chat abstraction and multi-turn history manage...
Supported Model Formats for LiteRT-LM: Conversion Requirements ExplainedExplore LiteRT-LM supported model formats including tflite, task, and litertlm. Understand conversion requirements for efficient on-device deployment. Turn your models into production-ready litertlm files.
Thread Model for Handling Asynchronous Inference Operations in LiteRT-LMExplore the LiteRT-LM thread model for asynchronous inference. Discover how its task-centric pipeline with dedicated ThreadPools ensures non-blocking LLM operations and efficient callback dispatch.
How to Build LiteRT-LM from Source Using Bazel or CMakeLearn to build LiteRT-LM from source with Bazel or CMake. Compile the inference engine easily for your AI projects. Get started today!
How LiteRT-LM Manages Multi-Turn Conversation State: Architecture and ImplementationLearn how LiteRT-LM manages multi turn conversation state using its C++ Conversation class for efficient chat history and KV cache management. Discover the architecture.
How to Use Streaming Inference with LiteRT-LM's Callback-Based APILearn how LiteRT-LM's callback-based API enables non-blocking streaming inference for real-time LLM output processing.
LiteRT-LM Sampler Parameters: Complete Guide to TopK, TopP, Greedy, and TemperatureMaster LiteRT-LM sampler parameters like TopK, TopP, Greedy, and Temperature. Optimize your text generation with this complete guide to LiteRT-LM's SamplerParameters.
How to Configure Backend Options for LiteRT-LM: CPU, GPU, Vision, and Audio BackendsLearn how to configure LiteRT-LM backend options for CPU, GPU, vision, and audio. Control inference engines via CLI or Python API for optimized performance.
LiteRT-LM Multimodal Input Types: Text, Images, and Audio SupportExplore LiteRT-LM's multimodal input capabilities. Learn how it processes text, images, and audio for advanced AI applications.
How the C API Manages the Lifecycle of LiteRT-LM Engine and SessionsLearn how the LiteRT-LM C API manages engine and session lifecycles with explicit create/delete functions for clear ownership from start to finish.
LoRA Adapter Architecture in LiteRT-LM: Runtime Integration Deep DiveExplore the six-layer LoRA adapter architecture in LiteRT-LM. Learn how LiteRT-LM integrates LoRA for efficient runtime adaptation via memory-mapped files, regex validation, and manager components.
How LiteRT-LM Handles Task Cancellation and Sequential Execution in the ExecutionQueueDiscover how LiteRT-LM ensures task cancellation and sequential execution with its FIFO ExecutionQueue. Learn about monotonic IDs and the safe Remove API.
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