I mainly use it for machine learning and AI. It's for a large language model, like LLaMA.
Hugging Face Hub
Hugging FaceExternal reviews
8 reviews
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Stable, easy to set up, and useful
What is our primary use case?
How has it helped my organization?
Hugging Face has helped me in many ways. For example, I can check the leading board and see which model gives the best performance. Another thing I can do is use an exact Q code to deploy and test the model. It has a lot of articles and papers where I can find out what I need.
What is most valuable?
What I find the most valuable about Hugging Face is that I can check all the models on it and see which ones have the best performance without using another platform.
What needs improvement?
The area that needs improvement would be the organization of the materials. It could be clearer and more systematic. It would be good if the layout was clear and we could search the models easily.
For how long have I used the solution?
I've been using Hugging Face for a little over a year.
What do I think about the stability of the solution?
When it comes to stability, I would give it a nine out of ten.
What do I think about the scalability of the solution?
It's a scalable solution. I would rate the scalability an eight out of ten. Approximately ten to twenty people use Hugging Face at our company. I try to use the solution as much as possible.
Which solution did I use previously and why did I switch?
I have previously used GitHub for codes and models. I still use it from time to time when I want to double-check something, but I use Hugging Face regularly.
How was the initial setup?
The ease of the initial setup is a nine out of ten. It only takes about ten minutes if you follow the instructions you find on Google.
What other advice do I have?
Hugging Face is the main hub for large language models and AIs. I would recommend it to anyone who's considering using it. Overall, I rate it a nine out of ten.
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