LMFlow : Train your ChatGPT LLM using Nvidia GPU at home in 5 Hours
LMFlow Simplifies Personalized Training of Large Models for Specialized Tasks. A Versatile Toolkit for Enhancing Large Foundation Models through Fine Tuning and Inference
Large foundation models have revolutionized the field of artificial intelligence by showcasing impressive capabilities in achieving human-level intelligence across various domains. However, when it comes to specialized-task applications, these models often require finetuning to achieve optimal performance. As the number of available models and specialized tasks continues to grow, the process of general finetuning becomes increasingly complex. Addressing this challenge, a groundbreaking solution called LMFlow has been introduced—an extensible and lightweight toolkit designed to simplify the finetuning and inference of large foundation models.
LMFlow provides a comprehensive finetuning workflow for large foundation models, enabling personalized training with limited computing resources. It supports continuous pretraining, instruction tuning, parameter-efficient finetuning, alignment tuning, and large model inference. With carefully designed and extensible APIs, LMFlow empowers developers and researchers to enhance the performance of large models efficiently.
The toolkit offers several key features, including:
Continuous Pre Training:
LMFlow supports continuous pre training, allowing large language models to adapt to specific domains. By collecting unlabeled data and preparing it in the appropriate format, users can effectively improve the model’s performance in a particular domain.
Instruction Tuning:
Instruction tuning, also known as supervised finetuning, enhances language models by training them to follow natural language instructions. LMFlow enables training on a small set of task-specific data, improving the model’s proficiency in various tasks and enabling more accurate responses to user queries.
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Reinforcement Learning with Human Feedback (RLHF):
LMFlow incorporates reinforcement learning with human feedback to align the model’s text generation with human preferences. The novel RLHF algorithm called RAFT (Reward rAnked FineTuning) ranks the model’s output based on a reward model, allowing for efficient training with selected high-quality samples.
Efficient Tuning:
LMFlow supports low-rank adaptation (LoRA), an efficient tuning method that reduces the number of trainable parameters by freezing the weights of the pretrained model and incorporating trainable rank decomposition matrices into each layer of the Transformer architecture.
Inference:
LMFlow provides an easy-to-use inference interface for large language models. Leveraging Deepspeed, LMFlow supports parameter partitioning and zero-offload strategies, ensuring efficient and real-time word-by-word output.
LMFlow has been thoroughly tested and is readily available for use. It offers a simplified and lightweight solution for training personalized models based on large foundation models. For instance, using a 7-billion-parameter LLaMA model, it only takes one Nvidia 3090 GPU and five hours to train a personalized model. The trained models can be employed in question-and-answer services, such as lmflow.com, for immediate use.
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With LMFlow, individuals can train their own personalized models, selecting the appropriate model based on available resources and specific tasks, such as question answering, companionship, writing, translation, and expert consultations across various domains. The larger the model and dataset, the longer the training time, resulting in improved results. Notably, a 33-billion-parameter model trained using LMFlow has achieved comparable or even better performance than ChatGPT.
LMFlow’s system design encompasses four stages: domain adaptation, task adaptation, instruction finetuning, and reinforcement learning with human feedback. By offering a complete fine-tuning workflow for these stages, LMFlow enables personalized training of large language models, even with limited computing resources.
To sum up, while large foundation models have shown tremendous potential in general applications, LMFlow plays a crucial role in unlocking their full capabilities for specialized domains and tasks. By providing an extensible, lightweight, and user-friendly toolkit, LMFlow empowers developers and researchers to efficiently perform finetuning and inference tasks, revolutionizing the application of large foundation models in various specialized fields.
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Reference : https://doi.org/10.48550/arXiv.2306.12420