FlagRelease Overview#
FlagRelease is a platform dedicated to the automatic migration, adaptation and release of large models across different AI hardwares.
FlagRelease is built upon the unified and open-source AI system software stack FlagOS, which provides cross-hardware adaptation capabilities. FlagRelease establishes a standardized pipeline that supports the following:
Automatic migration of large models to different hardware architectures
Automated evaluation of migration results
Built-in automated deployment and tuning
Multi-chip model packaging and release
The artifacts released through the FlagRelease platform are published on the following under the FlagRelease organization:
Users can obtain different hardware-specific versions of open-source large models. These models can be downloaded and used directly on the corresponding hardware environments without requiring users to perform model migration themselves.
The outputs of the FlagRelease platform include validated, hardware-adapted model files and integrated Docker images. Each image contains the core components of FlagOS along with all required model dependencies, allowing users to deploy and use the models directly on the target chips. In addition, each model release provides evaluation results as technical references, enabling users to clearly understand the model’s correctness and performance characteristics across different hardware platforms.
Furthermore, every released model is accompanied by configuration and usage instructions for AnythingLLM, helping users quickly verify the availability of the migrated models and facilitating downstream development and application based on these models.
You may use FlagRelease for quick deployment and execution in the following scenarios:
Research and experimentation: rapidly deploy large models for inference without concern for underlying hardware differences.
Production environments: directly deploy hardware-specific versions of models as services, ensuring performance and stability across different AI chips.