A number of other libraries contain scripts for calling the eval harness through their library. These include [GPT-NeoX](https://github.com/EleutherAI/gpt-neox/blob/main/eval_tasks/eval_adapter.py), [Megatron-DeepSpeed](https://github.com/microsoft/Megatron-DeepSpeed/blob/main/examples/MoE/readme_evalharness.md), and [mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/blob/master/eval_harness.py).
A number of other libraries contain scripts for calling the eval harness through their library. These include [GPT-NeoX](https://github.com/EleutherAI/gpt-neox/blob/main/eval_tasks/eval_adapter.py), [Megatron-DeepSpeed](https://github.com/microsoft/Megatron-DeepSpeed/blob/main/examples/MoE/readme_evalharness.md), and [mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/blob/master/eval_harness.py).
### Additional Features
If you have a CUDA-compatible Mac GPU, you can run the eval harness using the MPS back-end by replaicng `--device cuda:0` with `--device mps:0`. PyTorch does not currently support automatic mixed precision (AMP) for MPS, so we forcibly cast all weights to fp32 regardless of how they're stored. This is slower and has a larger memory footprint than we can achieve on Linux systems, but as PyTorch continues to improve its MPS support we hope to continue to improve it.
💡 **Tip**: You can inspect what the LM inputs look like by running the following command:
💡 **Tip**: You can inspect what the LM inputs look like by running the following command:
