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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/um-arm-lab/efficient-eng-2-ltl

Language Models are Few-Shot Learners

https://github.com/unispac/visual-adversarial-examples-jailbreak-large-language-models

GPT-4 Technical Report

https://github.com/usyd-fsalab/fp6_llm

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/utterworks/fast-bert

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/vcskaushik/LLMzip

LLaMA: Open and Efficient Foundation Language Models

https://github.com/viethoang1512/kpa

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/vilm-ai/viet-llm-eval

Language Models are Few-Shot Learners

https://github.com/volcengine/vegiantmodel

Language Models are Few-Shot Learners

https://github.com/vvvm23/mamba-jax

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/weitunglin/pixmamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/weiwang31/icemamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/wzzzd/LM_NER

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/x-lance/neusym-rag

Language Models are Few-Shot Learners

https://github.com/xi-j/mamba-asr

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/xiaoman-zhang/PMC-VQA

LLaMA: Open and Efficient Foundation Language Models

https://github.com/xiaoqian19940510/text-classification-

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/xiaoqian19940510/text-classification-surveys

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/xuetianci/pacit

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/xusenlinzy/api-for-open-llm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/xverse-ai/xverse-13b

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/xvyaward/owq

LLaMA: Open and Efficient Foundation Language Models

https://github.com/xzhang97666/alpacare

LLaMA: Open and Efficient Foundation Language Models

https://github.com/yangletliu/finlora

BloombergGPT: A Large Language Model for Finance

https://github.com/yangyucheng000/University/tree/main/model-3/roberta

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/young-geng/easylm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/yuanmu97/secure-transformer-inference

LLaMA: Open and Efficient Foundation Language Models

https://github.com/zach-zhiling-zheng/reticular_chemist

GPT-4 Technical Report

https://github.com/zaradana/Fast_BERT

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/zfj1998/CodeBert-Code2Text

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/zhangyezhuo/mamca

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/zhu-0108/mts-unmixers

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/zihanzhaosjtu/librisqa

LLaMA: Open and Efficient Foundation Language Models

https://github.com/znhy1024/protoco

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/zphang/lm_evaluation_harness

Language Models are Few-Shot Learners

https://github.com/zurichnlp/contradecode

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/zyphra/blackmamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.