MiniMind-V's Efficient Training Capabilities
MiniMind-V is an open source visual language model (VLM) training framework based on PyTorch implementation, with its core strength being the ability to complete model training in a very short period of time. The tool is capable of completing a training session for a 26 million parameter model on a single NVIDIA 3090 GPU in only about an hour.
- Hardware efficiency:Optimized for single-card GPUs with only 24GB of video memory requirement
- Training speed:Each training cycle (epoch) takes about 1 hour
- Cost Control:Complete training costs only about 1.3 RMB
- Code Streamlining:No more than 50 lines of core implementation code
This high efficiency is achieved through a well-designed model architecture that includes strategies for freezing the CLIP visual coder, training only the projection layer and the last layer of the language model. The project provides a complete closed loop from data cleaning to model inference, and is particularly suitable for researchers and developers who need to quickly validate VLM prototypes.
This answer comes from the articleMiniMind-V: 1 hour training of a 26M parameter visual language modelThe
