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Hugging Face
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Bloom Inference Optimization

6 posts · Mar 2023 – Aug 2026

What this trail covers

Hugging Face now supports BLOOM model inference optimization, leveraging DeepSpeed and Accelerate for incredibly fast inference. This builds upon existing efforts in large language model development and optimization, enabling efficient training and deployment of very large models. This post specifically details how to train a language model using Megatron-LM, a framework for distributed training, and introduces techniques for optimizing inference speed. The focus is on achieving high throughput and efficient resource utilization for BLOOM.

All Hugging Face trails
  1. Accelerating Transformers Fine-Tuning with NVIDIA NeMo AutoModelThis post introduces the integration of NVIDIA NeMo AutoModel for accelerating the fine-tuning of Transformers models. It details how AutoModel simplifies the process of selecting and configuring optimal training parameters and infrastructure, leading to faster and more efficient fine-tuning of large language models.
  2. How 🤗 Accelerate runs very large models thanks to PyTorchThis post details how 🤗 Accelerate enables running very large models thanks to PyTorch, by managing memory and distribution across multiple GPUs. It explains how Accelerate handles the complexities of distributed training and inference, allowing users to leverage PyTorch's capabilities for models that would otherwise exceed single-device memory.
  3. Incredibly Fast BLOOM Inference with DeepSpeed and AccelerateThis post details the technology behind BLOOM's training, including the distributed training infrastructure, hardware, and software stack required for such a massive model. It introduces optimizations using DeepSpeed and Accelerate to achieve incredibly fast inference for the BLOOM model, building upon existing efforts in large language model development and optimization.
  4. The Technology Behind BLOOM TrainingThis post details the technology behind training the BLOOM model, focusing on the infrastructure, distributed training strategies, and optimization techniques employed. It highlights the use of Megatron-LM for distributed training and discusses the challenges and solutions in scaling such a large model. The post provides technical insights into the engineering efforts required to train a 176B parameter model.
  5. Optimization story: Bloom inferenceThis post details the technology behind BLOOM's training, including the distributed training infrastructure, hardware, and software stack required for such a massive model.