Train transformer language models with reinforcement learning.

LIMO: Less is More for Reasoning

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

Paper Reproduction Google SCoRE(Training Language Models to Self-Correct via Reinforcement Learning)

Clinical text summarization by adapting large language models

The official Meta Llama 3 GitHub site

A curated list of reinforcement learning with human feedback resources (continually updated)

Federated Optimization in Heterogeneous Networks (MLSys '20)

자체 구축한 한국어 평가 데이터셋을 이용한 한국어 모델 평가

A framework for few-shot evaluation of language models.

Flower: A Friendly Federated AI Framework

This repository contains two datasets with multi-turn adversarial conversations generated by human agents interacting with a dialog model and rated for safety by two corresponding diverse rater pools.

Implementation of the training framework proposed in Self-Rewarding Language Model, from MetaAI

[NAACL 2024 Findings] Evaluation suite for the systematic evaluation of instruction selection methods.

🐙 OctoPack: Instruction Tuning Code Large Language Models

✏️ 기술 면접 스터디 Cheat Sheet

☁️ 구름(KULLM): 고려대학교에서 개발한, 한국어에 특화된 LLM

Transformer related optimization, including BERT, GPT

Korean BART

Code for T-Few from "Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning"

Assignments for CS294-112.

For experiments involving instruct gpt. Currently used for documenting open research questions.

🙋 핵심을 질문하다. 그리고 용감하게 대답하다. 국내 IT기업부터 실리콘밸리까지 "현직자가 해설해주는 기술면접"

🤗 Pretrained BERT model & WordPiece tokenizer trained on Korean Comments 한국어 댓글로 프리트레이닝한 BERT 모델과 데이터셋

파이썬 알고리즘 / 코딩테스트 스터디