- Improve API server with better error handling and response formatting
- Enhance CLI with additional options for DiT stages and configuration
- Add comprehensive examples in examples/basic/ including:
- API client with health checks and text generation
- Docker setup and usage examples
- Simple usage patterns for different scenarios
- Add utility scripts for model downloading and Docker setup
- Update documentation with implementation details and testing guidelines
- Fix configuration validation issues in OmniLLM
- Improve stage configuration handling for AR and DiT stages
- Add proper error handling and fallback mechanisms
Tested with Qwen3-0.6B model:
- Server starts successfully on port 8000
- Health and info endpoints working correctly
- Text generation with various parameters functioning
- API client examples working as expected
- CLI help and configuration options working properly
- Add comprehensive PRD, architecture design, and test design documents
- Implement core modules: OmniLLM, AsyncOmniLLM, stage configurations
- Add DiT scheduler and cache manager for diffusion models
- Implement CLI integration with --omni flag support
- Add API server and plugin system for vLLM integration
- Create comprehensive test suite with fixtures
- Update dependencies to vLLM 0.10.2 and PyTorch 2.8.0
- Add conda environment setup and package installation
- Implement stage-based processing architecture
- Add multimodal output processing capabilities
This commit establishes the foundation for multi-modality models
inference and serving with non-autoregressive structures.
hsliu