AnswerDotAI/fsdp_qlora
Fsdp Qlora
This framework provides a toolkit for fine-tuning large language models by combining distributed data parallelism with parameter sharding and quantization techniques. It is designed to scale the training of massive neural networks across multiple graphics processors, enabling the execution of models that exceed the memory capacity of individual hardware units.
The library distinguishes itself by integrating low-rank adaptation with memory-efficient weight loading and quantization-aware parameter sharding. By initializing model weights directly on the graphics processor and applying granular layer-wise wrapping, the framework minimizes memory spikes and reduces communication overhead during the distributed setup and training phases.
The system supports the training of custom transformer architectures through flexible wrapping policies for attention and multilayer perceptron layers. It further optimizes resource usage by dynamically adjusting numerical precision during computation, balancing training stability against available hardware memory. The project is distributed as a collection of utilities and scripts intended for use in distributed computing environments.
Features
- Quantized Fine-Tuning Frameworks - Provides a toolkit for fine-tuning large language models using memory-efficient quantization and sharded data parallelism.
- Large-Scale Model Training - Scales the training process across multiple graphics processors by sharding model parameters to handle massive architectures.
- Large Language Model Fine-Tuning Frameworks - Combines memory-efficient quantization with distributed data parallelism to train massive models while reducing hardware resource requirements.
- Fully Sharded Data Parallelism - Distributes model parameters, gradients, and optimizer states across multiple graphics processors to minimize memory consumption per device.
- QLoRA Adapters - Applies low-rank adaptation and quantization to reduce the memory footprint of large model training.
- Distributed Memory Optimizers - Partitions memory-intensive optimizer states across multiple devices to enable training of models exceeding single-GPU capacity.
- Distributed Training Frameworks - Scales model training across multiple nodes by sharding parameters, gradients, and optimizer states efficiently.
- Automatic Precision Casting - Dynamically switches between numerical precision formats during computation to balance training stability and hardware throughput.
- Distributed Mixed-Precision Training - Adjusts numerical precision levels between high-accuracy and high-speed modes to balance training stability against available hardware memory.
- Memory-Efficient Deep Learning - Reduces the hardware footprint of training large neural networks by applying quantization techniques to lower memory usage.
- Quantized Model Loading - Initializes model weights directly within graphics processor memory to prevent large memory spikes during distributed setup.
- Weight Sharding - Compresses model weights into lower precision formats before distributing them across the cluster to reduce communication overhead.
Star history
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