AGI-Edgerunners/LLM-Adapters
Code for our EMNLP 2023 Paper: "LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models"
This project helps machine learning engineers efficiently customize large language models (LLMs) for specific tasks without needing massive computational resources. It takes an existing LLM (like LLaMa, OPT, or BLOOM) and a specific dataset, then outputs a specialized model ready for tasks like arithmetic reasoning or common sense understanding. The ideal user is an ML engineer or researcher working with LLMs who needs to fine-tune them for niche applications.
1,229 stars. No commits in the last 6 months.
Use this if you need to adapt a large language model to perform better on a specific task or dataset, but want to avoid the high computational cost and time of full fine-tuning.
Not ideal if you need to train a large language model completely from scratch, or if you prefer full fine-tuning over parameter-efficient methods.
Stars
1,229
Forks
120
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 10, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/AGI-Edgerunners/LLM-Adapters"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
scaleapi/llm-engine
Scale LLM Engine public repository
AGI-Arena/MARS
The official implementation of MARS: Unleashing the Power of Variance Reduction for Training Large Models
modelscope/easydistill
a toolkit on knowledge distillation for large language models
Wang-ML-Lab/bayesian-peft
Bayesian Low-Rank Adaptation of LLMs: BLoB [NeurIPS 2024] and TFB [NeurIPS 2025]
sangmichaelxie/doremi
Pytorch implementation of DoReMi, a method for optimizing the data mixture weights in language...