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.. important:: | ||
Before starting this tutorial, be sure to review the :ref:`introduction <nemo-aligner-getting-started>` for tips on setting up your NeMo-Aligner environment. | ||
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If you run into any problems, refer to NeMo's `Known Issues page <https://docs.nvidia.com/nemo-framework/user-guide/latest/knownissues.html>`__. The page enumerates known issues and provides suggested workarounds where appropriate. | ||
If you run into any problems, refer to NeMo's `Known Issues page <https://docs.nvidia.com/nemo-framework/user-guide/latest/knownissues.html>`__. The page enumerates known issues and provides suggested workarounds where appropriate. | ||
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After completing this tutorial, refer to the :ref:`evaluation documentation <nemo-aligner-eval>` for tips on evaluating a trained model. |
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.. include:: /content/nemo.rsts | ||
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.. _nemo-aligner-eval: | ||
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Evaluate a Trained Model | ||
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After training a model, you may want to run evaluation to understand how the model performs on unseen tasks. You can use Eleuther AI's `Language Model Evaluation Harness <https://github.com/EleutherAI/lm-evaluation-harness>`_ | ||
to quickly run a variety of popular benchmarks, including MMLU, SuperGLUE, HellaSwag, and WinoGrande. | ||
A full list of supported tasks can be found `here <https://github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/README.md>`_. | ||
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Install the LM Evaluation Harness | ||
################################# | ||
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Run the following commands inside of a NeMo container to install the LM Evaluation Harness: | ||
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.. code-block:: bash | ||
git clone --depth 1 https://github.com/EleutherAI/lm-evaluation-harness | ||
cd lm-evaluation-harness | ||
pip install -e . | ||
Run Evaluations | ||
############### | ||
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A detailed description of running evaluation with ``.nemo`` models can be found in Eleuther AI's `documentation <https://github.com/EleutherAI/lm-evaluation-harness?tab=readme-ov-file#nvidia-nemo-models>`_. | ||
Single- and multi-GPU evaluation is supported. The following is an example of running evaluation using 8 GPUs on the ``hellaswag``, ``super_glue``, and ``winogrande`` tasks using a ``.nemo`` file from NeMo-Aligner. | ||
Please note that while it is recommended, you are not required to unzip your .nemo file before running evaluations. | ||
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.. code-block:: bash | ||
mkdir unzipped_checkpoint | ||
tar -xvf /path/to/model.nemo -c unzipped_checkpoint | ||
torchrun --nproc-per-node=8 --no-python lm_eval --model nemo_lm \ | ||
--model_args path='unzipped_checkpoint',devices=8,tensor_model_parallel_size=8 \ | ||
--tasks lambada_openai,super-glue-lm-eval-v1,winogrande \ | ||
--batch_size 8 |
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