{"task":"parameter-efficient fine-tuning","dataset":"HellaSwag","metric_names":["Accuracy (% )"],"rows":[{"id":116211,"task":"parameter-efficient fine-tuning","parent_task":null,"dataset":"HellaSwag","model_name":"LLaMA2-7b","metrics":{"Accuracy (% )":"76.68"},"paper_url":"https://arxiv.org/abs/2408.15300v1","paper_title":"GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMs","paper_date":"2024-08-27","code_links":[{"title":"On-Point-RND/GIFT_SW","url":"https://github.com/On-Point-RND/GIFT_SW"}],"metrics_order":"[\"Accuracy (% )\"]","area":"Methodology","uses_additional_data":1,"source":"archive","tags":[]},{"id":116212,"task":"parameter-efficient fine-tuning","parent_task":null,"dataset":"HellaSwag","model_name":"LLaMA2-7b","metrics":{"Accuracy (% )":"76.67"},"paper_url":"https://arxiv.org/abs/2106.09685v2","paper_title":"LoRA: Low-Rank Adaptation of Large Language Models","paper_date":"2021-06-17","code_links":[{"title":"labmlai/annotated_deep_learning_paper_implementations","url":"https://github.com/labmlai/annotated_deep_learning_paper_implementations"},{"title":"hiyouga/llama-efficient-tuning","url":"https://github.com/hiyouga/llama-efficient-tuning"},{"title":"tatsu-lab/stanford_alpaca","url":"https://github.com/tatsu-lab/stanford_alpaca"},{"title":"tloen/alpaca-lora","url":"https://github.com/tloen/alpaca-lora"},{"title":"qwenlm/qwen","url":"https://github.com/qwenlm/qwen"},{"title":"QwenLM/Qwen-7B","url":"https://github.com/QwenLM/Qwen-7B"},{"title":"flagalpha/llama2-chinese","url":"https://github.com/flagalpha/llama2-chinese"},{"title":"llamafamily/llama-chinese","url":"https://github.com/llamafamily/llama-chinese"},{"title":"microsoft/LoRA","url":"https://github.com/microsoft/LoRA"},{"title":"mistralai/mistral-inference","url":"https://github.com/mistralai/mistral-inference"},{"title":"mistralai/mistral-src","url":"https://github.com/mistralai/mistral-src"},{"title":"qwenlm/qwen-vl","url":"https://github.com/qwenlm/qwen-vl"},{"title":"Lightning-AI/lit-llama","url":"https://github.com/Lightning-AI/lit-llama"},{"title":"videocrafter/videocrafter","url":"https://github.com/videocrafter/videocrafter"},{"title":"internlm/xtuner","url":"https://github.com/internlm/xtuner"},{"title":"h2oai/h2o-llmstudio","url":"https://github.com/h2oai/h2o-llmstudio"},{"title":"facico/chinese-vicuna","url":"https://github.com/facico/chinese-vicuna"},{"title":"mistralai/mistral-finetune","url":"https://github.com/mistralai/mistral-finetune"},{"title":"phoebussi/alpaca-cot","url":"https://github.com/phoebussi/alpaca-cot"},{"title":"liucongg/chatglm-finetuning","url":"https://github.com/liucongg/chatglm-finetuning"},{"title":"lxe/simple-llama-finetuner","url":"https://github.com/lxe/simple-llama-finetuner"},{"title":"pytorch/opacus","url":"https://github.com/pytorch/opacus"},{"title":"facebookresearch/pytorch-dp","url":"https://github.com/facebookresearch/pytorch-dp"},{"title":"punica-ai/punica","url":"https://github.com/punica-ai/punica"},{"title":"vivo-ai-lab/bluelm","url":"https://github.com/vivo-ai-lab/bluelm"},{"title":"arielnlee/Platypus","url":"https://github.com/arielnlee/Platypus"},{"title":"longyuewangdcu/chinese-llama-2","url":"https://github.com/longyuewangdcu/chinese-llama-2"},{"title":"flagai-open/aquila2","url":"https://github.com/flagai-open/aquila2"},{"title":"flowersteam/lamorel","url":"https://github.com/flowersteam/lamorel"},{"title":"clementromac/lamorel","url":"https://github.com/clementromac/lamorel"},{"title":"ZhangYuanhan-AI/NOAH","url":"https://github.com/ZhangYuanhan-AI/NOAH"},{"title":"Davidzhangyuanhan/NOAH","url":"https://github.com/Davidzhangyuanhan/NOAH"},{"title":"thuml/AutoTimes","url":"https://github.com/thuml/AutoTimes"},{"title":"yjiangcm/lion","url":"https://github.com/yjiangcm/lion"},{"title":"davisyoshida/lorax","url":"https://github.com/davisyoshida/lorax"},{"title":"teelinsan/camoscio","url":"https://github.com/teelinsan/camoscio"},{"title":"heekhero/DTL","url":"https://github.com/heekhero/DTL"},{"title":"pilancilab/caldera","url":"https://github.com/pilancilab/caldera"},{"title":"aleemsidra/convlora","url":"https://github.com/aleemsidra/convlora"},{"title":"sunildkumar/lora_from_scratch","url":"https://github.com/sunildkumar/lora_from_scratch"},{"title":"ykwon0407/datainf","url":"https://github.com/ykwon0407/datainf"},{"title":"debjitpaul/refiner","url":"https://github.com/debjitpaul/refiner"},{"title":"jackaduma/Alpaca-LoRA-RLHF-PyTorch","url":"https://github.com/jackaduma/Alpaca-LoRA-RLHF-PyTorch"},{"title":"wwxu21/cut","url":"https://github.com/wwxu21/cut"},{"title":"Owen-Oertell/rlcm","url":"https://github.com/Owen-Oertell/rlcm"},{"title":"zjunlp/trice","url":"https://github.com/zjunlp/trice"},{"title":"lighterswang/biomedclip-lora","url":"https://github.com/lighterswang/biomedclip-lora"},{"title":"yjiangcm/lte","url":"https://github.com/yjiangcm/lte"},{"title":"uds-lsv/llmft","url":"https://github.com/uds-lsv/llmft"},{"title":"durham/rwkv-finetune-script","url":"https://github.com/durham/rwkv-finetune-script"},{"title":"pilancilab/Riemannian_Preconditioned_LoRA","url":"https://github.com/pilancilab/Riemannian_Preconditioned_LoRA"},{"title":"KU-CVLAB/MoA","url":"https://github.com/KU-CVLAB/MoA"},{"title":"song-wx/sift","url":"https://github.com/song-wx/sift"},{"title":"brandon3964/multimodal-task-vector","url":"https://github.com/brandon3964/multimodal-task-vector"},{"title":"mchl-labs/stambecco","url":"https://github.com/mchl-labs/stambecco"},{"title":"ikergarcia1996/sequence-labeling-llms","url":"https://github.com/ikergarcia1996/sequence-labeling-llms"},{"title":"hellokevin07/elastictrainer","url":"https://github.com/hellokevin07/elastictrainer"},{"title":"hitz-zentroa/this-is-not-a-dataset","url":"https://github.com/hitz-zentroa/this-is-not-a-dataset"},{"title":"oseyosey/ccds","url":"https://github.com/oseyosey/ccds"},{"title":"zhanglab-aim/lingo","url":"https://github.com/zhanglab-aim/lingo"},{"title":"gt-ripl/selective-projection-decay","url":"https://github.com/gt-ripl/selective-projection-decay"},{"title":"zhaolingao/langptune","url":"https://github.com/zhaolingao/langptune"},{"title":"12kimih/hicupid","url":"https://github.com/12kimih/hicupid"},{"title":"Luohh5/Chain-of-Exemplar","url":"https://github.com/Luohh5/Chain-of-Exemplar"},{"title":"chenlicodebank/lora_on_analog_hardware","url":"https://github.com/chenlicodebank/lora_on_analog_hardware"},{"title":"google-deepmind/thunnini","url":"https://github.com/google-deepmind/thunnini"},{"title":"sultan-hassan/CosmoGemma","url":"https://github.com/sultan-hassan/CosmoGemma"},{"title":"theodb/asr-whisper-finetuning","url":"https://github.com/theodb/asr-whisper-finetuning"},{"title":"if001/rwkv-lm-lora-ja","url":"https://github.com/if001/rwkv-lm-lora-ja"},{"title":"simula/pointdetectcount","url":"https://github.com/simula/pointdetectcount"},{"title":"dreamboat26/fuzzy-logic","url":"https://github.com/dreamboat26/fuzzy-logic"},{"title":"lif31up/extended-BERT-for-low-rank-adaption","url":"https://github.com/lif31up/extended-BERT-for-low-rank-adaption"},{"title":"lif31up/attention-is-all-you-need","url":"https://github.com/lif31up/attention-is-all-you-need"},{"title":"h2oai/alpaca-lora","url":"https://github.com/h2oai/alpaca-lora"}],"metrics_order":"[\"Accuracy (% )\"]","area":"Methodology","uses_additional_data":1,"source":"archive","tags":[]},{"id":116213,"task":"parameter-efficient fine-tuning","parent_task":null,"dataset":"HellaSwag","model_name":"LLaMA2-7b","metrics":{"Accuracy (% )":"76.27"},"paper_url":"https://arxiv.org/abs/2402.09353v6","paper_title":"DoRA: Weight-Decomposed Low-Rank Adaptation","paper_date":"2024-02-14","code_links":[{"title":"NVlabs/DoRA","url":"https://github.com/NVlabs/DoRA"},{"title":"catid/dora","url":"https://github.com/catid/dora"},{"title":"seanzhang-zhichen/llama3-chinese","url":"https://github.com/seanzhang-zhichen/llama3-chinese"},{"title":"nbasyl/DoRA","url":"https://github.com/nbasyl/DoRA"},{"title":"ayyucedemirbas/DoRA","url":"https://github.com/ayyucedemirbas/DoRA"}],"metrics_order":"[\"Accuracy (% )\"]","area":"Methodology","uses_additional_data":1,"source":"archive","tags":[]}]}