Open the config.yaml file and specify the specified configurations. The file ought to have the following composition:
In prior releases, conversion expected both of those the input & output checkpoints to generally be saved in memory for the duration of conversion. This intended that giant types demanded a prohibitively big amount of memory to be able to complete conversion. In launch two.0.0, we introduce streaming conversion, which substantially cuts down the peak memory use by executing conversion incrementally.
Make sure you browse the output notes of the checklist command prior to utilizing the converter. This portion points out the exact product lessons that are being converted from/to.
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Alter the values within the config.yaml file according to your necessities. You'll be able to refer to the reviews inside the file for suggestions and offered options.
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That is carried out by loading/preserving one particular shard at a time for pickled checkpoints and loading/preserving a single tensor at any given time for Cerebras H5 checkpoints. Streaming conversion is enabled by default; you don’t want to make any improvements for the command line arguments. As a result of this characteristic, you will now have the capacity to convert substantial checkpoints (e.g.: LLaMA 70B) on a little machine (~10GB of RAM).
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The Mapping class is used to outline the mapping of the model across GPUs based on the parallelism configurations.
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