mit-han-lab/hardware-aware-transformers
[ACL'20] HAT: Hardware-Aware Transformers for Efficient Natural Language Processing
This project helps machine translation practitioners deliver faster, more efficient language models on various devices. You input a machine translation task and the target hardware (like a Raspberry Pi or a powerful server CPU/GPU), and it outputs a specialized, pre-trained translation model optimized for speed and size on that hardware, without sacrificing translation quality. This is ideal for researchers and engineers deploying language models in diverse environments.
336 stars. No commits in the last 6 months.
Use this if you need to deploy high-quality machine translation models that run quickly and efficiently on specific hardware, especially devices with limited resources.
Not ideal if you are looking for a general-purpose, off-the-shelf translation service without specific hardware optimization needs.
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Python
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Last pushed
Jul 14, 2024
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