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Hugging Face vs PyTorch comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Hugging Face
Ranking in AI Development Platforms
5th
Average Rating
8.2
Number of Reviews
10
Ranking in other categories
No ranking in other categories
PyTorch
Ranking in AI Development Platforms
8th
Average Rating
8.6
Number of Reviews
10
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of November 2024, in the AI Development Platforms category, the mindshare of Hugging Face is 7.8%, up from 5.6% compared to the previous year. The mindshare of PyTorch is 1.5%, down from 2.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms
 

Featured Reviews

AshishKumar11 - PeerSpot reviewer
Jul 25, 2024
Open-sourced, reliable, and enables organizations to finetune data for business requirements
Hugging Face is a website that provides various open-source models. We use them to finetune models for our business. It is just like ChatGPT, but ChatGPT has paid sources. If we have to call an API, we must pay for it. However, Hugging Face has various open-source models like Llama 2 and Llama 3…
Arucy Lionel - PeerSpot reviewer
Nov 27, 2023
Offers good backward compatible and simple to use
We work a lot with text processing, vectorization, and other NLP tasks. Sometimes, we need to process websites, presentations, or optics quickly because they're used in user engines and other applications. We use PyTorch to test our implementations as well One of the things I really like about…

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"The product is reliable."
"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 tool's most valuable feature is that it shows trending models. All the new models, even Google's demo models, appear at the top. You can find all the open-source models in one place. You can use them directly and easily find their documentation. It's very simple to find documentation and write code. If you want to work with AI and machine learning, Hugging Face is a perfect place to start."
"Hugging Face provides open-source models, making it the best open-source and reliable solution."
"My preferred aspects are natural language processing and question-answering."
"There are numerous libraries available, and the documentation is rich and step-by-step, helping us understand which model to use in particular conditions."
"The solution is easy to use compared to other frameworks like PyTorch and TensorFlow."
"It is stable."
"Its interface is the most valuable. The ability to have an interface to train machine learning models and construct them with the high-level interface, without excess busting and reconstructing the same technical elements, is very useful."
"The product's initial setup phase is easy."
"The tool is very user-friendly."
"I like that PyTorch actually follows the pythonic way, and I feel that it's quite easy. It's easy to find compared to others who require us to type a long paragraph of code."
"It's been pretty scalable in terms of using multiple GPUs."
"For me, the product's initial setup phase is easy...For beginners, it is fairly easy to learn."
"We use PyTorch libraries, which are working well. It's very easy."
"The framework of the solution is valuable."
 

Cons

"Implementing a cloud system to showcase historical data would be beneficial."
"I believe Hugging Face has some room for improvement. There are some security issues. They provide code, but API tokens aren't indicated. Also, the documentation for particular models could use more explanation. But I think these things are improving daily. The main change I'd like to see is making the deployment of inference endpoints more customizable for users."
"Initially, I faced issues with the solution's configuration."
"Most people upload their pre-trained models on Hugging Face, but more details should be added about the models."
"Hugging Face could improve by implementing a search engine or chat bot feature similar to ChatGPT."
"I've worked on three projects using Hugging Face, and only once did we encounter a problem with the code. We had to use another open-source embedding from OpenAI to resolve it. Our team has three members: me, my colleague, and a team leader. We looked at the problem and resolved it."
"The solution must provide an efficient LLM."
"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."
"There is not enough documentation about some methods and parameters. It is sometimes difficult to find information."
"The product has breakdowns when we change the versions a lot."
"On the production side of things, having more frameworks would be helpful."
"PyTorch could make certain things more obvious. Even though it does make things like defining loss functions and calculating gradients in backward propagation clear, these concepts may confuse beginners. We find that it's kind of problematic. Despite having methods called on loss functions during backward passes, the oral documentation for beginners is quite complex."
"I've had issues with stability when I use a lot of data and try out different combinations of modeling techniques."
"We faced an issue with PyTorch due to version incompatibility. PyTorch has no latest version after v12.3."
"I would like to see better learning documents."
"I would like a model to be available. I think Google recently released a new version of EfficientNet. It's a really good classifier, and a PyTorch implementation would be nice."
 

Pricing and Cost Advice

"We do not have to pay for the product."
"The tool is open-source. The cost depends on what task you're doing. If you're using a large language model with around 12 million parameters, it will cost more. On average, Hugging Face is open source so you can download models to your local machine for free. For deployment, you can use any cloud service."
"So, it's requires expensive machines to open services or open LLM models."
"Hugging Face is an open-source solution."
"The solution is open source."
"I recall seeing a fee of nine dollars, and there's also an enterprise option priced at twenty dollars per month."
"It is free."
"PyTorch is an open-source solution."
"The solution is affordable."
"It is free."
"PyTorch is open-sourced."
"PyTorch is open source."
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Top Industries

By visitors reading reviews
Computer Software Company
11%
Manufacturing Company
11%
Financial Services Firm
10%
University
10%
Manufacturing Company
29%
Computer Software Company
11%
Healthcare Company
8%
University
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Hugging Face?
My preferred aspects are natural language processing and question-answering.
What needs improvement with Hugging Face?
Hugging Face could improve by implementing a search engine or chat bot feature similar to ChatGPT. This would aid developers in easily finding how to fine-tune models with specific data or get mode...
What is your primary use case for Hugging Face?
I use Hugging Face primarily to work with open LLM models. I recently started using the open LOM models and also use embedding models. I use these models to train custom data and monitor our deskto...
What needs improvement with PyTorch?
We faced an issue with PyTorch due to version incompatibility. PyTorch has no latest version after v12.3. We also faced a few version compatibility issues with CUDA drivers.
 

Comparisons

 

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Overview

Find out what your peers are saying about Hugging Face vs. PyTorch and other solutions. Updated: October 2024.
815,854 professionals have used our research since 2012.