Hugging Face Reviews

Name: Hugging Face
Brand: Hugging Face
Rating: 4.1 (10 reviews)

4.1 out of 5

10 reviews
100% willing to recommend

What is Hugging Face?

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.

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Hugging Face is the #5 ranked solution in top AI Development Platforms. PeerSpot users give Hugging Face an average rating of 8.2 out of 10. Hugging Face is most commonly compared to Microsoft Azure Machine Learning Studio: Hugging Face vs Microsoft Azure Machine Learning Studio. Hugging Face is popular among the large enterprise segment, accounting for 61% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a computer software company, accounting for 11% of all views.

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AI Development Platforms

January 2025

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Featured reviews

AshishKumar11

Associate Software Engineer at Linkfields Innovations (Pty) Ltd

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…

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Neeraj Maurya

Artificial Intelligence Consultant at GlobalLogic

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 desktop custom models after training and deployment The most valuable features of Hugging Face are the…

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Neeraj Pokala

Machine Learning Engineer at TechMinfy

We have two deployment options: cloud and on-premises. On-premises means it's on-demand, and we have to monitor it. With cloud deployment, there's no need to watch for availability because it's always handled in the cloud. There should be no problems with cloud deployment. If we deploy on-premises, we have to monitor it ourselves. That's the main difference. We have both options available. It's very easy to deploy an endpoint because there's already pre-built documentation. With just one click, you can directly load the knowledge handler. The challenging part is determining if the model suits our customized use case, which takes time. Once we're sure the model is right for our use case, it's straightforward.

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Hugging Face mindshare

As of January 2025, the mindshare of Hugging Face in the AI Development Platforms category stands at 13.2%, up from 6.3% compared to the previous year, according to calculations based on PeerSpot user engagement data.

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Key learnings from peers

Last updated Dec 5, 2024

Valuable Features

Hugging Face offers open-source models and abundant libraries, making it easy for teams to find effective and well-documented packages. Their trending and embedding models along with text conversion tools facilitate AI and machine learning work. The availability of an Inference API and useful documentation is highly valued. Users appreciate the cost efficiency and simplicity compared to frameworks like PyTorch and TensorFlow. Secure LMM and NLP capabilities are also significant advantages.

"There are numerous libraries available, and the documentation is rich and step-by-step, helping us understand which model to use in particular conditions."
"It is stable."
"The solution is easy to use compared to other frameworks like PyTorch and TensorFlow."

Room for Improvement

Hugging Face requires better material organization and a clearer layout with enhanced search capabilities. The documentation and security protocols need refining, and inference deployment should be more customizable. Incorporating a cloud system, improving integration with ecosystem libraries, and adding features like a ChatGPT-like search tool would be advantageous. More details on pre-trained models and instructions for using modules are necessary. Hugging Face also needs to focus on advancing AI capabilities to compete with entities like Facebook.

"Hugging Face could improve by implementing a search engine or chat bot feature similar to ChatGPT."
"It can incorporate AI into its services."
"Initially, I faced issues with the solution's configuration."

Pricing

Hugging Face offers various pricing models, including open-source solutions with no extra cost for downloading models locally. Enterprise options range from six to twenty dollars per month, depending on features. While setup costs can rise with specific implementations, such as using large language models with high machine and GPU requirements, many users find the pricing reasonable. Deployment can utilize any cloud service, allowing flexibility in setup costs.

"Hugging Face is an open-source solution."
"We do not have to pay for the product."
"The solution is open source."

Popular Use Cases

Users primarily engage with Hugging Face for machine learning and AI tasks, especially utilizing large language models like LLaMA for training and deployment. They fine-tune open-source models for custom use, such as creating SQL chatbots or handling PDF data extraction. Hugging Face provides open-source models like Llama 2 for businesses, enabling customization without the cost associated with other options. Users appreciate Hugging Face's ability to work with embedding models and perform data extraction efficiently.

Deployment

Hugging Face's initial setup is generally easy, often taking about ten minutes with clear instructions. Some find no setup necessary as they use web servers, while others mention deployment can be challenging depending on the architecture, like Kubernetes or Docker. Cloud deployment is straightforward, requiring no monitoring, unlike on-premises setups. Good hardware facilitates easy installation, but low RAM complicates it. Education or tutorials help manage deployment challenges. Tailoring models to specific use cases can require additional time.

Scalability

Users find Hugging Face scalable, with many relying on it for projects. Several companies rate scalability highly, though some note a need for managed offerings as they scale projects to production. While used by a small group in some organizations, others predict vast future adoption, underscoring its potential. Despite prototype-level capabilities at present, the tool effectively helps enlarge knowledge bases, garnering positive feedback from those embracing it.

Stability

Hugging Face is consistently praised for its stability, with users highlighting its reliable packages and models. Most users rate its stability between seven to nine out of ten. Some mention occasional server errors with the inference API but appreciate improvements for app development. Hugging Face is utilized by many companies for deploying large language models. Users rarely report bugs and generally find it a dependable and stable platform for generative AI tasks.

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Review data by company size

By reviewers

By visitors reading reviews

Top industries

By visitors reading reviews

Manufacturing Company

11%

Computer Software Company

11%

University

10%

Financial Services Firm

10%

Educational Organization

Comms Service Provider

Healthcare Company

Government

Retailer

Media Company

Energy/Utilities Company

Hospitality Company

Non Profit

Legal Firm

Real Estate/Law Firm

Insurance Company

Construction Company

Performing Arts

Wholesaler/Distributor

Outsourcing Company

Logistics Company

Consumer Goods Company

Pharma/Biotech Company

Compare Hugging Face with alternative products

Learn more about Hugging Face

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, access a library of open-source models for tailored applications, and utilize options like the Inference API. The platform impresses with its free usage, popularity of trending models, and effective program management, although improvements could be made in security and documentation for more customizable deployments. Collaboration with ecosystem library providers and better model description details could boost its utility.

What are the key features of Hugging Face?

Wide model hosting: Offers a vast array of models for diverse applications.
Inference API: Provides efficient and seamless deployment of models.
Open-source access: Encourages community collaboration and model sharing.
Embedding models: Supports various formats and packages.
Simple platform: User-friendly and cost-effective for businesses.

What benefits can users expect from Hugging Face?

Cost reduction: Access to free or lower-cost tools compared to proprietary software.
Customization: Easy adaptation of pre-trained models to fit specific needs.
Efficiency: Quick deployment and fine-tuning of models for business use.
Community support: Comprehensive documentation and open-source collaboration.

Hugging Face is widely used across industries requiring machine learning solutions, such as creating SQL chatbots or data extraction tools. Organizations focus on fine-tuning language models to enhance business processes and remove reliance on proprietary systems. The platform supports innovative applications, including business-specific AI solutions, demonstrating its flexibility and adaptability.