Hugging Face Reviews

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.

Hugging Face is an open-source desktop solution.

The solution is open-source. There are so many models available for usage, especially for prototyping. You can play around with text-to-text, text-to-image, and text-to-video. They have also provided the Inference API as part of the WebUI for a smaller model. You can play around with their website.

You could use Hugging Face for libraries like Lambda. Hugging Face has upgraded from using Inference API for their free developer offering. Some ecosystem libraries are lagging. Both of them are still using Inference API. Perhaps Hugging Face could collaborate with these ecosystem library providers to ensure they update their offerings and provide users access to the latest technology.

I have been using Hugging Face for four to five months. We are using the latest version of the solution.

The inference API and other stuff are rate-limited. It returns internal server errors or does not return any results a lot of times. There were no such crashes. Secondly, Hugging Face has made things easier for apps in production. They offer libraries, but much other work is left to the developers.

I rate the solution’s stability a seven out of ten.

Hugging Face has not been built out for taking the app to production. They are offering prototype-level capabilities. We'll have to start consuming some managed offerings or build everything ourselves.

I rate the solution's scalability a six out of ten.

I started using Hugging Face because I'm still prototyping. Other vendors are pretty managed offerings with many costs in getting code built out, whereas Hugging Face is free.

Alternatives like Vertex OpenAI and Azure OpenAI offer access to large language models, but most platforms are closed and restrict fine-tuning. This is where Hugging Face shines. Its open nature allows for fine-tuning of models, providing a significant advantage. Additionally, if data security is a concern, enterprises can deploy their own Hugging Face model as an endpoint or local instance, avoiding the need to send data to externally managed offerings. This flexibility and control over data makes Hugging Face a compelling choice for producing large language models.

The initial setup is very easy and takes a few seconds to complete.

There is no extra cost.

Many advanced models are available on Hugging Face. The managed providers are working towards adding the usage of AI models and getting them to a ready stage for usage.

They're trying to give offerings for people to be able to use it. They are also coming up with options to productionize it, but some areas need work.

Overall, I rate the solution a nine out of ten.

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.

I am working on AI with various large language models for different purposes such as medicine and law, where they are fine-tuned with specific requirements. I download LLMs from Hugging Face for these environments. I use it to support AI-driven projects and deploy AI applications for local use, focusing on local LLMs with real-world applications.

Hugging Face is valuable because it provides a single, comprehensive repository with thorough documentation and extensive datasets. It hosts nearly 400,000 open-source LLMs that cover a wide variety of tasks, including text classification, token classification, text generation, and more. It serves as a foundational platform offering updated resources, making it essential in the AI community.

It is challenging to suggest specific improvements for Hugging Face, as their platform is already very well-organized and efficient. However, they could focus on cleaning up outdated models if they seem unnecessary and continue organizing more LLMs.

I have been working with Hugging Face for about one and a half years.

Hugging Face is stable, provided the environment is controlled, and the user base is limited. The stability relies on the specific models and the data they're fed, which minimizes issues like hallucination.

Hugging Face is quite scalable, especially in terms of upgrading models for better performance. There is flexibility in using models of varying sizes while keeping the application environment consistent.

I have not needed to communicate with Hugging Face's technical support because they have extensive documentation available.

Neutral

Before Hugging Face, I used Ollama due to its ease of use, but Hugging Face offers a wider range of models.

The initial setup can be rated as a seven out of ten due to occasional issues during model deployment, which might require adjustments. Recent developments have made the process easier though.

The pricing is reasonable. I use a pro account, which costs about $9 a month. This positions it in the middle of the cost scale.

Before choosing Hugging Face, I used Ollama for its ease of use, but it lacked the variety offered by Hugging Face.

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.