Hugging Face Hub

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 However, they could focus on cleaning up outdated models if they seem unnecessary and continue organizing more LLMs.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.

I have been using Hugging Face  for proof of concepts (POC) and a generative AI project. Currently, I'm trying to use it with Tala and Olaama, along with some other AI tools as I build up my knowledge of AI and generative AI.

I like that Hugging Face  is versatile in the way it has been developed. I appreciate the versatility and the fact that it has generalized many models. I'm exploring other solutions as well, however, I find Hugging Face very user-friendly. 

I am still building my knowledge of it. From my perspective, it's very easy to use, and as you ramp up, you discover new aspects about it.

Regarding scalability, I'm finding the multi-GPU aspect of it challenging. Training the model is another hurdle, although I'm only getting into that aspect currently. Organizations are apprehensive about investing in multi-GPU setups. 

Additionally, data cleanup is a challenge that needs to be resolved, as data must be mature and pristine.

I have been using it for a total of around six months.

I have not really faced any stability issues, however, the scale has been small. I'm unsure how it would perform on a larger scale.

I have not had production-type deployments for a client yet. Organizations are not mature enough to invest significantly in multi-GPU setups, which presents a scalability challenge. Also, organizations are apprehensive about the multi-GPU route.

I have not contacted their support team yet.

Neutral

I am just a user at this point and do not have information about their pricing.

I'm exploring Langchain  and Agentic AI as part of my current learning and development.

Joining the Hugging Face community can provide additional support. It allows for collaboration on models and datasets, offering quick insights on how the community is using it. 

I rate the solution a seven out of ten.

This is a simple personal project, non-commercial. As a student, that's all I do.

The most valuable features are the inference APIs as it takes me a long time to run inferences on my local machine.

Access to the models and datasets could be improved. Many interesting ones are restricted. It would be great if they provided access for students or non-professionals who just want to test things.

I have been using this solution for about the last three or four months.

I have used just TensorFlow  and PyTorch . Nothing else.

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. 

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.

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.