This document is intended for all developers using D-Robotics LLM and provides comprehensive guidance for the entire development process.
The following is a brief overview of each chapter. You may refer to the corresponding sections as needed.
Provides an overview of the content within each chapter of this document.
Displays the list of models supported on the D-Robotics edge side and their mapping to on-device execution examples.
Describes the release package contents supported by the D-Robotics LLM toolchain.
| Chapter | Description |
| X86 Environment Setup | Instruction on setting up the development environment on an x86 machine. |
| On-device Environment Setup | Instruction on setting up the development environment on the edge device. |
| Chapter | Description |
| On-device Preparation | General preparation steps required before running models on the edge device. |
| LLM Development Guide | Development workflow for the LLM (Large Language Model) series. |
| VLM Development Guide | Development workflow for the VLM (Vision Language Model) series. |
| VLA Development Guide | Development workflow for the VLA (Vision-Language-Action) series. |
| ASR Development Guide | Development workflow for the ASR (Automatic Speech Recognition) series. |
| Appendix | Parameter descriptions for configuration files required for on-device execution. |
| Chapter | Description |
| Overview | Illustrates the basic API call workflow for model loading and inference. |
| API Details Introduction | Detailed technical specifications, including basic types, structures, enums, callbacks, and core functions. |
| Chapter | Description |
| Benchmark Specifications | Defines testing conditions and statistical terminology used in benchmarks. |
| LLM Benchmark | Performance metrics for the LLM series. |
| VLM Benchmark | Performance metrics for the VLM series. |
| VLA Benchmark | Performance metrics for the VLA series. |
| ASR Benchmark | Performance metrics for the ASR series. |
The authorized user agreement. Please read this carefully before using the D-Robotics LLM toolchain.