FlagAI is a comprehensive toolkit designed for the efficient training, fine-tuning, and deployment of large-scale AI models, particularly those with multi-modal capabilities. It caters to researchers and developers working with large language and vision models, offering a streamlined experience for complex tasks and emphasizing support for Chinese language processing.

How It Works

FlagAI integrates seamlessly with popular parallel training libraries like PyTorch, Deepspeed, Megatron-LM, and BMTrain, enabling users to scale training with minimal code changes. It provides an AutoLoader for quick access to over 30 mainstream models (including Aquila, AltCLIP, AltDiffusion, WuDao GLM, EVA-CLIP, OPT, BERT, RoBERTa, GPT2, T5) and supports prompt-learning for few-shot tasks. The toolkit is built to simplify complex model operations, abstracting away much of the underlying distributed training complexity.

Quick Start & Requirements

• Install via pip: pip install -U flagai
• Requires Python >= 3.8, PyTorch >= 1.8.0.
• Optional dependencies for enhanced performance include CUDA, NCCL, Apex, DeepSpeed, BMTrain, BMInf, and Flash Attention.
• Official quickstart examples are available at FlagAI/quickstart.

Highlighted Details

• Supports over 30 mainstream models, including large-scale Chinese models like WuDao GLM.
• Facilitates parallel training with integrations for Deepspeed, Megatron-LM, and BMTrain.
• Offers prompt-learning toolkits for few-shot learning scenarios.
• Provides specific optimizations and examples for Chinese language tasks.
• Includes models for text generation, image-text matching, and text classification.

Maintenance & Community

FlagAI is actively maintained with regular releases (e.g., v1.7.0 in June 2023). Community engagement is encouraged via GitHub Issues and Discussions. Contact is available via open.platform@baai.ac.cn.

Licensing & Compatibility

The majority of FlagAI is licensed under Apache 2.0. However, components like Megatron-LM (Megatron-LM license), GLM (MIT license), and AltDiffusion (CreativeML Open RAIL-M license) have separate terms. This mix requires careful review for commercial or closed-source integration.

Limitations & Caveats

The project's licensing is a mix of permissive and potentially more restrictive licenses, requiring careful consideration for commercial use. While extensive, the README does not detail specific hardware requirements for training the largest models, which are likely substantial.