LLM-Adapters
Discover a framework designed for efficient fine-tuning of advanced large language models via a variety of adapter techniques. This framework, supporting models such as LLaMa, OPT, BLOOM, and GPT-J, enables parameter-efficient learning across multiple tasks. It offers compatibility with adapter methods like LoRA, AdapterH, and Parallel adapters, enhancing the performance of NLP applications. Keep informed about the latest achievements, including surpassing benchmarks such as ChatGPT in commonsense and mathematics evaluations.
