LMTuner: An user-friendly and highly-integrable Training Framework for fine-tuning Large Language Models
This addresses the need for efficient and accessible fine-tuning tools for LLM practitioners, though it is incremental as it builds on existing methods like LoRA and DeepSpeed.
The authors tackled the problem of complex and code-intensive fine-tuning of large language models by introducing LMTuner, a user-friendly framework that enables novice users to start training within five minutes and supports models from 300M to 130B parameters on a single server.
With the burgeoning development in the realm of large language models (LLMs), the demand for efficient incremental training tailored to specific industries and domains continues to increase. Currently, the predominantly employed frameworks lack modular design, it often takes a lot of coding work to kickstart the training of LLM. To address this, we present "LMTuner", a highly usable, integrable, and scalable system for training LLMs expeditiously and with minimal user-input. LMTuner comprises three main modules - the Interaction, Training, and Inference Modules. We advocate that LMTuner's usability and integrality alleviate the complexities in training large language models. Remarkably, even a novice user could commence training large language models within five minutes. Furthermore, it integrates DeepSpeed frameworks and supports Efficient Fine-Tuning methodologies like Low Rank Adaptation (LoRA), Quantized LoRA (QLoRA), etc., enabling the training of language models scaling from 300M to a whopping 130B parameters using a single server. The LMTuner's homepage (https://wengsyx.github.io/LMTuner/)and screencast video (https://youtu.be/nsXmWOmN3rE) are now publicly available.