xLLM is an open-source framework developed by JD Open Source for building, training, and deploying vision-language models and other large-scale AI models. It provides a unified toolchain covering training, fine-tuning, and inference with comprehensive documentation and example code to help research and engineering teams bring multimodal systems from experimentation to production.
Model Architecture Support
- Joint training and inference pipelines for LLM, VLM, DiT, and REC model architectures
- Multimodal feature fusion and cross-modal alignment through extensible model components
- Composable training strategies for diverse training scenarios
- Optimizations tailored for diverse AI accelerators including GPUs and domestic chips
Training and Fine-Tuning
- Distributed training with efficient parallelism and memory management for large-scale parameters
- Large-scale fine-tuning workflows for adapting foundation models to domain-specific tasks
- Multimodal data processing utilities included out of the box
- Evaluation tooling for measuring model performance across benchmarks
Deployment and Documentation
- Inference engine optimized for throughput across multiple accelerator types
- Cross-device optimization layer for heterogeneous hardware deployments
- Cost-effective deployment on mixed hardware clusters
- Detailed ReadTheDocs documentation and runnable examples lower the learning curve