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With the burgeoning development in the realm of large language models (LLMs),the demand for efficient incremental training tailored to specific industriesand domains continues to increase. Currently, the predominantly employedframeworks lack modular design, it often takes a lot of coding work tokickstart the training of LLM. To address this, we present "LMTuner", a highlyusable, integrable, and scalable system for training LLMs expeditiously andwith minimal user-input. LMTuner comprises three main modules - theInteraction, Training, and Inference Modules. We advocate that LMTuner'susability and integrality alleviate the complexities in training large languagemodels. Remarkably, even a novice user could commence training large languagemodels within five minutes. Furthermore, it integrates DeepSpeed frameworks andsupports Efficient Fine-Tuning methodologies like Low Rank Adaptation (LoRA),Quantized LoRA (QLoRA), etc., enabling the training of language models scalingfrom 300M to a whopping 130B parameters using a single server. The LMTuner'shomepage (https://wengsyx.github.io/LMTuner/)and screencast video(https://youtu.be/nsXmWOmN3rE) are now publicly available.

LMTuner: An user-friendly and highly-integrable Training Framework for fine-tuning Large Language Models