Hugging Face Reviews

I mainly use it for machine learning and AI. It's for a large language model, like LLaMA.

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 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.

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.

I've been using Hugging Face for a little over a year.

When it comes to stability, I would give it a nine out of ten.

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.

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.

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.

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.

We use the tool to extract data from a PDF file, give the text data to any Hugging Face model like Meta or Llama, and get the results from those models according to the prompt. It's basically like having a chat with the PDF file.

The solution is easy to use compared to other frameworks like PyTorch and TensorFlow.

Initially, I faced issues with the solution's configuration.

I have been using Hugging Face for almost two years.

Hugging Face is a stable solution.

Hugging Face is a scalable solution.

To use Hugging Face, you need to have basic knowledge of how to feed the data, how to speed data, how to train the model, and how to evaluate the model. Compared to other frameworks like PyTorch and TensorFlow, I'm more comfortable with using Hugging Face. I would recommend the solution to other users.

Overall, I rate the solution seven and a half out of ten.