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Copy file name to clipboardExpand all lines: examples/speculative_decoding/README.md
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@@ -80,7 +80,7 @@ For small base models that fit in GPU memory, we can collocate them with draft m
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training.output_dir=ckpts/llama-3.2-1b-online
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```
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All default training settings live in `eagle3.yaml`; override any field via OmegaConf dotlist arguments on the command line.
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All default training settings are in `eagle3.yaml`. You can adjust them by editing the YAML file or by specifying command-line overrides with OmegaConf dotlist arguments.
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To enable context parallelism for long-context training, add `training.cp_size=<N>`.
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The saved modelopt checkpoint is similar in architecture to HF models. It can be further optimized through **ModelOpt**, e.g., PTQ and QAT.
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