The script can take a pre-properly trained model checkpoint and converts it into a format that may be loaded and optimised by TensorRT-LLM.
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The examples address situations which include changing a checkpoint making use of only one GPU with FP16 or BF16 precision, and converting a checkpoint with specific product hyperparameters.
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As our Model Zoo implementations evolve with time, the modifications may perhaps from time to time crack out-of-the-box compatibility when shifting to a whole new launch. To ensure that it is possible to go on using your outdated checkpoints, we offer converters that permit you to “update” configs and checkpoints when important.
This will let you figure out Should the configuration you are applying inside of Model Zoo may be transformed prior to deciding to practice the model. In addition, You need to use the information in the mistake concept to switch the config file to create a configuration that can be converted.
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“Update” an older Cerebras Product Zoo config and checkpoint to so that they can proceed to be used in a newer launch (e.
The script supports tensor parallelism and pipeline parallelism to distribute the model across various GPUs.
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