AGI-Edgerunners/LLM-Adapters
An adapter-based framework for parameter-efficient fine-tuning of Large Language Models including LLaMA, OPT, BLOOM, and GPT-J.
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LLM-Adapters is an easy-to-use framework that integrates various adapter methods into Large Language Models to execute parameter-efficient fine-tuning for different tasks. It supports multiple adapter families including LoRA, Bottleneck adapters, Parallel adapters, Prefix Tuning, and Adapter variants. The framework extends HuggingFace’s PEFT library and enables training on open-access LLMs like LLaMA, OPT, BLOOM, and GPT-J with minimal parameter updates.
Frequently asked
- What is AGI-Edgerunners/LLM-Adapters?
- An adapter-based framework for parameter-efficient fine-tuning of Large Language Models including LLaMA, OPT, BLOOM, and GPT-J.
- Is LLM-Adapters open source?
- Yes — AGI-Edgerunners/LLM-Adapters is open source, released under the Apache-2.0 license.
- What language is LLM-Adapters written in?
- AGI-Edgerunners/LLM-Adapters is primarily written in Python.
- How popular is LLM-Adapters?
- AGI-Edgerunners/LLM-Adapters has 1.2k stars on GitHub.
- Where can I find LLM-Adapters?
- AGI-Edgerunners/LLM-Adapters is on GitHub at https://github.com/AGI-Edgerunners/LLM-Adapters.