Hugging Face Reviews

Name: Hugging Face
Brand: Hugging Face
Rating: 4.1 (12 reviews)

4.1 out of 5

12 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 university, accounting for 10% of all views.

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AI Development Platforms

January 2025

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Helped 838,533 peers since 2012

Featured reviews

SwaminathanSubramanian

Director/Enterprise Solutions Architect, Technology Advisor at Kyndryl

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.

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Melek Ghouma

Student at Renater

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.

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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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Hugging Face mindshare

As of February 2025, the mindshare of Hugging Face in the AI Development Platforms category stands at 13.2%, up from 6.6% compared to the previous year, according to calculations based on PeerSpot user engagement data.

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Key learnings from peers

Last updated Feb 9, 2025

Valuable Features

Hugging Face offers valuable features such as access to numerous models for natural language processing, open-source availability, an extensive range of libraries, trending and well-documented models, and versatile functionality. Users highlight the efficiency in exploring AI capabilities, ease of use compared to other frameworks, and cost reduction through free resources. The platform's advanced tools, like embedding models and inference APIs, facilitate rapid prototyping, making it ideal for AI and machine learning projects.

"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 would rate this product nine out of ten."

Room for Improvement

Hugging Face needs better organization and a clear layout. Users desire easier search functionality and improved documentation. There's frustration over some ecosystem libraries lagging behind, and a suggestion for collaboration to update technologies. Security concerns exist due to missing API token indications. Implementing a chatbot feature could aid developers. Scalability issues, access restrictions, and inadequate instructions for using modules pose challenges. Enhancements in deployment customization, model details, and model accessibility for students are needed.

"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."
"Regarding scalability, I'm finding the multi-GPU aspect of it challenging."
"Access to the models and datasets could be improved. Many interesting ones are restricted."

Pricing

Hugging Face offers varied pricing models with enterprise-level features, including options such as six or nine dollars per month and an enterprise option at twenty dollars monthly. Users find the tool open-source, with no extra costs for downloading models locally. However, deploying product services can be costly, especially when utilizing open LLM models, due to machine and GPU requirements. Setup costs depend significantly on task requirements and model parameters.

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

Popular Use Cases

Organizations primarily use Hugging Face for machine learning and AI, especially with large language models like LLaMA. They utilize open-source models for tasks such as fine-tuning, embedding, and data extraction. These models help train custom data, develop chatbots, and conduct proof of concepts. By leveraging open and pre-trained models, teams fine-tune solutions for specific business needs, including creating SQL chatbots and integrating functionalities similar to ChatGPT without incurring API fees.

Service and Support

Users express satisfaction with Hugging Face customer service, particularly when dealing with specific queries like model fine-tuning. Engagement typically occurs through email communication. Some have yet to contact Hugging Face's support team, indicating a lack of necessity or encounters with issues requiring assistance.

Deployment

Users find Hugging Face's initial setup mostly straightforward, often taking only minutes with proper guides or pre-built documentation. Challenges can arise based on system specifications, architecture like Kubernetes or Docker, and the desired deployment method, whether cloud or on-premises. Strong hardware and familiarity with LLMs can ease the process. While installation is often easy, it may require education or tutorials to handle complexities, taking around two to three hours for some.

Scalability

Hugging Face is seen as scalable, rated mostly an eight out of ten by several users, though some rate it less. Organizations using Hugging Face are exploring its potential but face challenges in transitioning to production, with concerns about multi-GPU setups and prototype-level capabilities. Users range from small groups to predicting thousands. Hugging Face supports scaling of knowledge rather than the application, causing some entities to consider managed offerings or self-building for further expansion.

Stability

Hugging Face is generally perceived as stable, with many users rating its stability high. Users appreciate its consistent provision of stable packages and models. Some mention occasional rate-limiting and internal server errors, yet they find it reliable, especially in production environments. Many companies utilize it for AI model deployment, noting minimal bugs or breakdowns. While some users have not faced major stability issues, they remain unsure about performance at larger scales.

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Top industries

By visitors reading reviews

Computer Software Company

10%

Manufacturing Company

10%

Financial Services Firm

10%

University

10%

Comms Service Provider

Educational Organization

Healthcare Company

Government

Retailer

Media Company

Non Profit

Real Estate/Law Firm

Energy/Utilities Company

Hospitality Company

Legal Firm

Insurance Company

Construction Company

Outsourcing Company

Performing Arts

Wholesaler/Distributor

Logistics Company

Consumer Goods Company

Pharma/Biotech Company

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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.