Sanwoo Lee's starred repositories
human-eval
Code for the paper "Evaluating Large Language Models Trained on Code"
lm-evaluation-harness
A framework for few-shot evaluation of language models.
Awesome-Model-Merging-Methods-Theories-Applications
Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. arXiv:2408.07666.
dnn-mode-connectivity
Mode Connectivity and Fast Geometric Ensembles in PyTorch
iclr2024-model-merging
This is the repository for "Model Merging by Uncertainty-Based Gradient Matching", ICLR 2024.
AdaMerging
AdaMerging: Adaptive Model Merging for Multi-Task Learning. ICLR, 2024.
BayesianOptimization
A Python implementation of global optimization with gaussian processes.
loss-landscape
Code for visualizing the loss landscape of neural nets
TransformerCompression
For releasing code related to compression methods for transformers, accompanying our publications
model-soups
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
model-stock
Model Stock: All we need is just a few fine-tuned models
nlp-uncertainty-zoo
Model zoo for different kinds of uncertainty quantification methods used in Natural Language Processing, implemented in PyTorch.
task_vectors
Editing Models with Task Arithmetic
lm-human-preference-details
RLHF implementation details of OAI's 2019 codebase
alpaca_eval
An automatic evaluator for instruction-following language models. Human-validated, high-quality, cheap, and fast.
reward-bench
RewardBench: the first evaluation tool for reward models.
Transformers-Tutorials
This repository contains demos I made with the Transformers library by HuggingFace.
ms-swift
Use PEFT or Full-parameter to finetune 350+ LLMs or 90+ MLLMs. (LLM: Qwen2.5, Llama3.2, GLM4, Internlm2.5, Yi1.5, Mistral, Baichuan2, DeepSeek, Gemma2, ...; MLLM: Qwen2-VL, Qwen2-Audio, Llama3.2-Vision, Llava, InternVL2, MiniCPM-V-2.6, GLM4v, Xcomposer2.5, Yi-VL, DeepSeek-VL, Phi3.5-Vision, ...)
awesome-uncertainty-deeplearning
This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models.