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코드

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

https://github.com/lucidrains/PaLM-pytorch

PaLM: Scaling Language Modeling with Pathways

https://github.com/lvyufeng/bert4ms

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/mbzuai-paris/lm-evaluation-harness-atlas-chat

Language Models are Few-Shot Learners

https://github.com/meetyou-ai-lab/can-mc-evaluate-llms

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/meta-llama/llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/mgmalek/efficient_cross_entropy

Mistral 7B

https://github.com/milanlproc/honest

https://github.com/milmor/GPT

Language Models are Few-Shot Learners

https://github.com/mindspore-ai/models/tree/master/official/nlp/gpt

Language Models are Few-Shot Learners

https://github.com/mthcom/hscore-dataset-pruning

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/musixmatchresearch/umberto

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/mzeromiko/vmamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/national-center-for-ai-saudi-arabia/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/ncoop57/gpt-code-clippy

Language Models are Few-Shot Learners

https://github.com/nebuly-ai/nebullvm/tree/main/apps/accelerate/chatllama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/neuralmagic/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/neuralmind-ai/coliee

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/nguyenvulebinh/vietnamese-roberta

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/ninglab/ecellm

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/niyunsheng/ems-sd

Factuality Enhanced Language Models for Open-Ended Text Generation

https://github.com/nkluge-correa/teenytinyllama

Training Compute-Optimal Large Language Models

https://github.com/nlx-group/overlapy

Language Models are Few-Shot Learners

https://github.com/ntunlplab/traditional-chinese-alpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/nus-hpc-ai-lab/dyvm

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/obi-ml-public/ehr_deidentification

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/octanove/shiba

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/ofa-sys/expertllama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ohadrubin/rpt

LLaMA: Open and Efficient Foundation Language Models

https://github.com/oneflow-inc/libai

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/open-finance-lab/finlora

BloombergGPT: A Large Language Model for Finance

https://github.com/openbiolink/promptsource

Language Models are Few-Shot Learners

https://github.com/opengptx/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/osu-nlp-group/mind2web

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/phoebussi/alpaca-cot

LLaMA: Open and Efficient Foundation Language Models

https://github.com/pisalore/roberta_results

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/postech-ami/smile-dataset

Language Models are Few-Shot Learners

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