AI Briefing#deepspeed
The latest AI and developer news about #deepspeed, with the original source and a short summary.
The latest AI and developer news about #deepspeed, with the original source and a short summary.
In PCIe-based VLA training, FP8 transmission reduced bytes by 48.4%, but step time increased because post-merger serialization placed it on the critical path.
AutoSP is a DeepCompile-based compiler that automates Sequence Parallelism for long-context training.
This summarizes the major technical issues and solutions that arise during fine-tuning and deployment of the Gemma-4 model.
This analyzes how differences in the internal precision handling methods of DeepSpeed and PyTorch FSDP affect model training outcomes.
Introduces how to fine-tune StarCoder with ChatML and DeepSpeed to build the conversational coding assistant StarChat.
Introduces how to efficiently deploy and run inference on the 176B-scale BLOOMZ model using 🤗 Optimum Habana on Habana Gaudi2 accelerators.
By integrating Hugging Face's Optimum library with ONNX Runtime, training speed for transformer models has been improved by up to 130%.
Compares the optimal hardware configuration and benchmarks for maximizing inference performance of the 176B-parameter BLOOM model using DeepSpeed and Accelerate.
It introduces how to apply DeepSpeed ZeRO technology through Hugging Face's Accelerate library to solve memory issues in large-scale model training.
It explains how to use the ZeRO features of DeepSpeed and FairScale in the Hugging Face Trainer to improve large-scale model training efficiency.