AGI-Edgerunners/LLM-Adapters

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1233 stars · 115 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

LLM-Adapters is a Python framework extending HuggingFace's PEFT library that integrates various adapter types (LoRA, series/parallel adapters, prefix/prompt tuning) into open-access LLMs like LLaMA, BLOOM, and GPT-J for parameter-efficient fine-tuning. It accompanies an EMNLP 2023 paper and includes benchmark datasets such as math10k and commonsense170k.

Use cases

• fine-tune llama with lora adapters
• parameter-efficient fine-tuning of large language models
• compare adapter types like bottleneck vs parallel adapters on llms
• reproduce emnlp 2023 llm-adapters paper experiments
• train adapters on commonsense170k dataset
• apply prefix tuning or p-tuning to open-access llms

When to choose

• you want a unified framework to experiment with multiple PEFT adapter methods on LLaMA, BLOOM, GPT-J, or OPT
• you need the paper's benchmark datasets and adapter checkpoints for research reproduction
• you want to study adapter placement and hyperparameter effects on fine-tuning performance

When to avoid

• you need actively maintained tooling with support for the latest LLMs - the repo has seen limited updates since 2024
• you want full fine-tuning rather than parameter-efficient methods
• you need production-grade training infrastructure rather than research code

Facets

library · maturity maintenance

llm-training machine-learning sdk large-language-models machine-learning deep-learning python peft lora adapters fine-tuning prompt-tuning huggingface research-code llama natural-language-processing gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="AGI-Edgerunners/LLM-Adapters")