Tools for merging pretrained large language models.
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Updated
Feb 26, 2025 - Python
Tools for merging pretrained large language models.
FusionBench: A Comprehensive Benchmark/Toolkit of Deep Model Fusion
Code release for Dataless Knowledge Fusion by Merging Weights of Language Models (https://openreview.net/forum?id=FCnohuR6AnM)
AdaMerging: Adaptive Model Merging for Multi-Task Learning. ICLR, 2024.
ZhiJian: A Unifying and Rapidly Deployable Toolbox for Pre-trained Model Reuse
Official repository of "Localizing Task Information for Improved Model Merging and Compression" [ICML 2024]
Representation Surgery for Multi-Task Model Merging. ICML, 2024.
DELLA-Merging: Reducing Interference in Model Merging through Magnitude-Based Sampling
All-in-one UI for merged LLMs in Hugging Face
Localize-and-Stitch: Efficient Model Merging via Sparse Task Arithmetic
[ECCV 2024] MagMax: Leveraging Model Merging for Seamless Continual Learning (official repository)
flow-merge is a powerful Python library that enables seamless merging of multiple transformer-based language models using the most popular merge methods such as model soups, SLERP, ties-MERGING or DARE.
Merge transformers without using like a bajillion GB of RAM
An easy-to-use Python library for merging PyTorch models.
SurgeryV2: Bridging the Gap Between Model Merging and Multi-Task Learning with Deep Representation Surgery. Arxiv, 2024.
The code used in the paper "DogeRM: Equipping Reward Models with Domain Knowledge through Model Merging"
This repository serves as a template for creating new projects based on FusionBench. It includes all the necessary configurations and boilerplate code to get started quickly.
Official repository of our work "Efficient Model Editing with Task-Localized Sparse Fine-tuning" accepted at ICLR 2025
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