v0.23.0: Model Memory Estimation tool, Breakpoint API, Multi-Node Notebook Launcher Support, and more!
A new model estimation tool to help calculate how much memory is needed for inference has been added. This does not download the pretrained weights, and utilizes init_empty_weights to stay memory efficient during the calculation.
Usage directions:
accelerate estimate-memory {model_name} --library {library_name} --dtypes fp16 int8
Or:
from accelerate.commands.estimate import estimate_command_parser, estimate_command, gather_data
parser = estimate_command_parser()
args = parser.parse_args(["bert-base-cased", "--dtypes", "float32"])
output = gather_data(args)
We've made the huggingface_hub library a first-class citizen of the framework! While this is mainly for the model estimation tool, this opens the doors for further integrations should they be wanted
Accelerator Enhancements:gather_for_metrics will now also de-dupe for non-tensor objects. See #1937mixed_precision="bf16" support on NPU devices. See #1949breakpoint API to help when dealing with trying to break from a condition on a single process. See #1940torch.compile support was fixed. See #1919gradient_accumulation_steps to "auto" in your deepspeed config, and Accelerate will use the one passed to Accelerator instead (#1901)accelerate config on npu by @statelesshz in https://github.com/huggingface/accelerate/pull/1895Tests] Finish all todos by @younesbelkada in https://github.com/huggingface/accelerate/pull/1957force_hooks to dispatch_model by @austinapatel in https://github.com/huggingface/accelerate/pull/1969Full Changelog: https://github.com/huggingface/accelerate/compare/v0.22.0...v0.23.0
Fetched April 7, 2026