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Training Large Language models can be resource-intensive and time-consuming. Even if you have resources, it could outright be difficult to implement. However, to make life simpler for smaller companies or enthusiasts, and innovators, several LLM training frameworks have emerged. These frameworks specifically address the challenge of democratizing the technical difficulties. Here are some of the most popular frameworks that help you to train and tune LLMs Models. I encourage you to explore these frameworks. They can significantly simplify and optimize the training process, allowing you to achieve better results efficiently.
An efficient deep learning optimization library that simplifies distributed training and inference, enabling easy and effective implementation.
DeepSpeed empowers ChatGPT-like model training with a single click, offering 15x speedup over SOTA RLHF systems with unprecedented cost reduction at all scales
DeepSpeed offers a confluence of system innovations, that has made large scale DL training effective, and efficient, greatly improved ease of use, and redefined the DL training landscape in terms of scale that is possible. These innovations such as ZeRO, 3D-Parallelism, DeepSpeed-MoE, ZeRO-Infinity, etc fall under the DeepSpeed-Training pillar. Learn more: DeepSpeed-Training
DeepSpeed brings together innovations in parallelism technology such as tensor, pipeline, expert and ZeRO-parallelism, and combines them with high performance custom inference kernels, communication optimizations and heterogeneous memory technologies to enable inference at an unprecedented scale, while achieving unparalleled latency, throughput and cost reduction. This systematic composition of system technologies for inference falls under the DeepSpeed-Inference. Learn more: DeepSpeed-Inference