Using Amazon CodeWhisperer to write Hugging Face code
October 28, 2022
Following my test of Github Copilot (https://youtu.be/HxyG896swQU), I was curious how Amazon CodeWhisperer would perform on the same example. So I gave it a try!
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In a nutshell, things didn't go well :-/ I found the service slow, and most of the prompts it generated were irrelevant. It also struggled to keep generating additional lines of code. Failing to trigger on copy-pasted prompted was very annoying (probably an issue with the AWS extension for VS Code).
Unlike Copilot, I wouldn't use CodeWhisperer to get real work done. The service is still in preview at the time of recording, and one can only hope that it will get much better over time. I'll give another try when it becomes generally available.
Getting started :
- Hugging Face: https://huggingface.co/course
- Amazon CodeWhisperer: https://aws.amazon.com/codewhisperer/
And if you're interested in LLMs for code generation, check out the BigCode project at https://www.bigcode-project.org/
Transcript
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The End
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The next step is to make a difference between the two. Music
you
Julien Simon is the Chief Evangelist at Arcee AI
, specializing in Small Language Models and enterprise AI solutions. Recognized as the #1 AI Evangelist globally by AI Magazine in 2021, he brings over 30 years of technology leadership experience to his role.
With 650+ speaking engagements worldwide and 350+ technical blog posts, Julien is a leading voice in practical AI implementation, cost-effective AI solutions, and the democratization of artificial intelligence. His expertise spans open-source AI, Small Language Models, enterprise AI strategy, and edge computing optimization.
Previously serving as Principal Evangelist at Amazon Web Services and Chief Evangelist at Hugging Face, Julien has helped thousands of organizations implement AI solutions that deliver real business value. He is the author of "Learn Amazon SageMaker," the first book ever published on AWS's flagship machine learning service.
Julien's mission is to make AI accessible, understandable, and controllable for enterprises through transparent, open-weights models that organizations can deploy, customize, and trust.