We have seen improved productivity and time saved from using Hugging Face; for a task that would have taken six hours, it saved us five hours, and we completed it in one hour with the plug-and-play integration of inference and everything, using the few lines of code that Hugging Face provides. I would recommend Hugging Face to others looking into using it because it is easy to integrate and plug-and-play. My company does not have a business relationship with this vendor other than being a customer. Hugging Face is good overall, and I give this review a rating of eight out of ten.
Independent IT Security Consultant at Self-Employed
Consultant
Top 20
Apr 18, 2025
Overall, the platform is excellent. For any AI enthusiast, Hugging Face provides a broad array of open-source models and a solid foundation for building AI applications. Using an on-premises model helps manage errors in critical environments. I rate Hugging Face as an eight out of ten.
I've been trying to implement some chatbots, and having free access to Hugging Face helped me a lot. I use PyTorch and TensorFlow to implement other deep-learning models and access LLMs. Each one of these tools has its own purpose. Python is used for deep learning projects to train and fine-tune models at the deep learning level, while for Hugging Face, it's mainly for the transformers library and LLM APIs. I cannot compare them directly. For me, it's about access to datasets and models. I would rate this product nine out of ten.
Python/AI Engineer at Wokegenics Solutions Private Limited
Real User
Top 10
Sep 4, 2024
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.
You can start with it on a personal device. If you're planning to deploy, you might want to consider integrating Hugging Face with a cloud platform. This can help reduce charges, and the deployment will happen on the cloud platform. If you're joining our team and using this tool for the first time, you'll need some experience deploying models. Hugging Face is one platform where you can deploy open-source models. You should have six or seven months of experience handling large language models. After that, you can learn the basic documentation in two or three days. I rate it an eight out of ten.
Hugging Face is suitable if you are serious about your product and want to keep your data private instead of using peer services. It's good for learning and exploring AI models.
Lead RND Engineer, Data Scientist at IDMED, Beijing China
Real User
Sep 4, 2023
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.
Hugging Face offers a platform hosting a wide range of models with efficient natural language processing tools. Known for its open-source nature, comprehensive documentation, and a variety of embedding models, it reduces costs and facilitates easy adoption.Valued in the tech community for its ability to host diverse models, Hugging Face simplifies tasks in machine learning and artificial intelligence. Users find it easy to fine-tune large language models like LLaMA for custom data training,...
We have seen improved productivity and time saved from using Hugging Face; for a task that would have taken six hours, it saved us five hours, and we completed it in one hour with the plug-and-play integration of inference and everything, using the few lines of code that Hugging Face provides. I would recommend Hugging Face to others looking into using it because it is easy to integrate and plug-and-play. My company does not have a business relationship with this vendor other than being a customer. Hugging Face is good overall, and I give this review a rating of eight out of ten.
Overall, the platform is excellent. For any AI enthusiast, Hugging Face provides a broad array of open-source models and a solid foundation for building AI applications. Using an on-premises model helps manage errors in critical environments. I rate Hugging Face as an eight out of ten.
I've been trying to implement some chatbots, and having free access to Hugging Face helped me a lot. I use PyTorch and TensorFlow to implement other deep-learning models and access LLMs. Each one of these tools has its own purpose. Python is used for deep learning projects to train and fine-tune models at the deep learning level, while for Hugging Face, it's mainly for the transformers library and LLM APIs. I cannot compare them directly. For me, it's about access to datasets and models. I would rate this product nine out of ten.
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.
You can start with it on a personal device. If you're planning to deploy, you might want to consider integrating Hugging Face with a cloud platform. This can help reduce charges, and the deployment will happen on the cloud platform. If you're joining our team and using this tool for the first time, you'll need some experience deploying models. Hugging Face is one platform where you can deploy open-source models. You should have six or seven months of experience handling large language models. After that, you can learn the basic documentation in two or three days. I rate it an eight out of ten.
Hugging Face is suitable if you are serious about your product and want to keep your data private instead of using peer services. It's good for learning and exploring AI models.
Overall, I would rate it nine out of ten.
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.