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.

I had to perform training on a model when I worked as a data scientist. There is already a pre-trained model, and we train our model on our custom data. We can accept things from this pre-trained model that has already been trained on a huge amount of data.

Hugging Face provides open-source models, making it the best open-source and reliable solution. Currently, Hugging Face is the best solution for exploring many models. There are several models that we can use in real life. There are several words, and we can use a Hugging Face model like NER to accept only limited words from a text.

Most people upload their pre-trained models on Hugging Face, but more details should be added about the models.

I have been using Hugging Face for six months.

The solution provides good stability.

Five people from our team totally depend on the Hugging Face model whenever the company gets a new project.

Hugging Face is an open-source solution.

The solution is deployed on the cloud in our organization. Hugging Face provides many open-source models like Meta and Gemma that are performing very well. When someone puts their model on Hugging Face, they provide us with all the steps. We can follow those steps and train our model. This is the best thing I have seen by Hugging Face.

Several IT industries in India are unable to purchase models like ChatGPT. Hugging Face provides open-source models, making it the best open-source and reliable solution. I would recommend the solution to other users. Users can easily use Hugging Face after watching YouTube videos on how to use it. It is easy to learn to use Hugging Face.

Overall, I rate the solution an eight out of ten.