BLOOM
bloom-87321ca3·5 events·first seen 28d agoAliases: BLOOM
Co-occurring entities
More like this (12)
Recent events (5)
The Technology Behind BLOOM Training
This Hugging Face blog post details the infrastructure and training methodology used to train BLOOM, a 176-billion parameter open-access multilingual language model. It covers the use of Megatron-DeepSpeed for distributed training across hundreds of GPUs, including tensor parallelism, pipeline parallelism, and data parallelism strategies. The post also discusses hardware setup, memory optimization techniques, and lessons learned during the large-scale training run.
Introducing BLOOM: The World's Largest Open Multilingual Language Model
Hugging Face and the BigScience workshop released BLOOM, a 176-billion parameter open-access multilingual language model trained on 46 natural languages and 13 programming languages. The model was developed collaboratively by over 1,000 researchers and represents a significant milestone in open-weights large language model development. BLOOM was designed to be freely accessible to researchers and practitioners, in contrast to proprietary models of similar scale.
Optimization story: Bloom inference
This Hugging Face blog post documents practical inference optimization techniques applied to the BLOOM large language model. It covers strategies for reducing latency and memory footprint during deployment, likely including quantization, tensor parallelism, and batching approaches. The post serves as a technical case study for serving very large open-weights models efficiently.
Incredibly Fast BLOOM Inference with DeepSpeed and Accelerate
This Hugging Face blog post details inference optimization techniques for the BLOOM 176B parameter model using DeepSpeed ZeRO and Hugging Face Accelerate. The post provides PyTorch scripts and benchmarks demonstrating significant throughput improvements through tensor parallelism and other optimizations. It serves as a practical guide for deploying large open-weight models efficiently across multiple GPUs.
Fast Inference on Large Language Models: BLOOMZ on Habana Gaudi2 Accelerator
This Hugging Face blog post covers deploying BLOOMZ, a large multilingual language model, on Intel's Habana Gaudi2 accelerator for inference. It benchmarks throughput and latency performance on Gaudi2 as an alternative to GPU-based inference. The post is part of ongoing work to demonstrate non-NVIDIA hardware options for large model deployment.