QLoRA: Efficient Finetuning of Quantized LLMs
QLoRA investigates fine-tuning quantized models with low-rank adapters to reduce memory requirements.
Paper & contextLearning language systems that can be adapted efficiently.
Zettlemoyer studies language understanding and how to train, adapt, and evaluate language models. These papers connect contextual word representations with efficient adaptation, tool use, and openly documented language-model development.
10 papers
QLoRA investigates fine-tuning quantized models with low-rank adapters to reduce memory requirements.
Paper & contextSelected research in language models. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in language models. Read the full paper, including the methods, experiments, and reported results.
Paper & contextToolformer explores teaching language models to invoke external tools with a self-supervised training approach.
Paper & contextSelected research in ai evaluation, language models. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in language models. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in machine learning, ai evaluation. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in language models. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in language models. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in language models. Read the full paper, including the methods, experiments, and reported results.
Paper & contextAn independent editorial profile. Inclusion does not imply Council membership or endorsement. Research is collaborative; coauthorship does not imply sole credit.
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