SuperAdapters

SuperAdapters

SuperAdapters is a comprehensive tool designed to finetune large language models (LLMs) using various adapters across multiple platforms. Key features include:

• Wide Compatibility: Supports all LLMs and adapters, allowing flexibility across different machine learning tasks.
• Customizable Training: Users can fine-tune models with specific parameters tailored to their dataset and task requirements.
• Multiple Integration Options: Supports integration with databases for efficient data management and retrieval during the finetuning process.
• Performance Optimizations: Includes options for using Lora, Qlora, and other adapter types to enhance model performance without extensive resource demands.

Benefits

• Enables seamless finetuning for various applications including classification and instruction following.
• Reduces time and resources required for model training through efficient adapters.
• Enhances the capabilities of existing LLMs, making them adaptable to specific tasks or datasets.

Highlights

• Supports popular models like ChatGLM, LLaMa, and Bloom.
• Provides detailed instructions and examples for finetuning and inference.
• Open-source with an active community for support and collaboration.