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하위 태스크

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평가표

1

보존된 평가 기록

89

코드

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

https://github.com/facebookresearch/fairseq2

Mistral 7B

https://github.com/facebookresearch/text_characterization_toolkit

HellaSwag: Can a Machine Really Finish Your Sentence?

https://github.com/facico/chinese-vicuna

LLaMA: Open and Efficient Foundation Language Models

https://github.com/fajri91/indommlu

LLaMA: Open and Efficient Foundation Language Models

https://github.com/few-shot-NER-benchmark/BaselineCode

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/flagalpha/llama2-chinese

LLaMA: Open and Efficient Foundation Language Models

https://github.com/flexible-fl/flex-nlp

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/foundation-model-stack/fms-fsdp

PaLM: Scaling Language Modeling with Pathways

https://github.com/freedomintelligence/huatuogpt

LLaMA: Open and Efficient Foundation Language Models

https://github.com/fsoft-ai4code/codecapybara

LLaMA: Open and Efficient Foundation Language Models

https://github.com/fywalter/label-bias

Language Models are Few-Shot Learners

https://github.com/ganjinzero/rrhf

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ggerganov/llama.cpp

Language Models are Few-Shot Learners

https://github.com/ggml-org/llama.cpp

Language Models are Few-Shot Learners

https://github.com/glb400/Toy-RecLM

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/gmum/dl-mo-2021

Language Models are Few-Shot Learners

https://github.com/google/paxml

PaLM: Scaling Language Modeling with Pathways

https://github.com/gpt4life/alpagasus

GPT-4 Technical Report

https://github.com/grantslatton/llama.cpp

Language Models are Few-Shot Learners

https://github.com/greenbitai/low_bit_llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/guinmoon/llmfarm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/haisongzhang/roberta-tiny-cased

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/haiyang-w/git

Language Models are Few-Shot Learners

https://github.com/haiyang-w/tokenformer

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/hamishivi/easylm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/havenhq/mamba-chat

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/hazyresearch/ama_prompting

Language Models are Few-Shot Learners

https://github.com/hexuandeng/drpruning

Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning

https://github.com/hilberthit/gpt-3

Language Models are Few-Shot Learners

https://github.com/hiyouga/llama-efficient-tuning

Finetuned Language Models Are Zero-Shot Learners

https://github.com/hkuds/easyrec

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/hojjat-mokhtarabadi/promptsource

Language Models are Few-Shot Learners

https://github.com/huberemanuel/DeBERTa

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/huggingface/transformers

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/human9000/nd-mamba2-torch

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/hustvl/vim

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

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