Table of contents
Hugging Face
The AI community building the future. The platform where the machine learning community collaborates on models, datasets, and applications.
A startup from the United States.
A startup from the United States.
Model Availability
Hugging Face offers a wide variety of pre-trained models for different NLP tasks such as text classification, translation, summarization, and question-answering, which can be easily accessed and implemented in projects.
Ease of Use
The platform provides user-friendly APIs and transformers library that simplifies the integration and use of complex models, even for users with limited expertise in machine learning.
Community and Collaboration
Hugging Face has a robust community of developers and researchers who contribute to the continuous improvement of models and tools. Users can share their models and collaborate with others within the community.
Documentation and Tutorials
Extensive documentation and a variety of tutorials are available, making it easier for users to understand how to apply models to their specific needs and learn best practices.
Inference API
Offers an inference API that allows users to deploy models without needing to worry about the backend infrastructure, making it easier and quicker to put models into production.
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Hugging Face is generally considered an excellent resource for both learning and implementing NLP technologies. Its robust and comprehensive range of tools and models support various applications, making it highly recommended in the field.
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Check the traffic stats of Hugging Face on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of Hugging Face on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of Hugging Face's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of Hugging Face on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about Hugging Face on Reddit. This can help you find out how popualr the product is and what people think about it.
Hugging Face hosts thousands of open models for NLP, vision, and other tasks. The Inference API (via Inference Providers) lets you call those models over HTTP. The @huggingface/inference package from huggingface.js is the Node.js client. - Source: dev.to / about 2 months ago
Right now, I don't. If model foo is deleted from HuggingFace but its compare rows are still in the DB, those compare pages will still be served at build time. They'll have the old data until the model's row in models.json is removed โ which only happens if the model falls out of the top-500 in the nightly fetch. It's a known gap. For now, the risk is low; popular models don't disappear. A more robust system would... - Source: dev.to / about 2 months ago
Apify turned out to be an excellent platform for building multi-agent systems(MAS). It allows seamless integration with modern agentic frameworks like LangGraph, CrewAI, TogetherAI, and Hugging Face. - Source: dev.to / 2 months ago
The garage is not the network. ComfyUI is a workbench. It does not describe how a workflow assembled in it travels to another workbench, what license attaches to the intermediate frames, or who in a multi-tool pipeline counts as the author of the result. Hugging Face is the closest thing the field has to a shared hub for models and datasets, and is a remarkable piece of community infrastructure, and is also a... - Source: dev.to / 2 months ago
All numbers below are reproducible from public APIs and public repository files: citation metadata, GitHub Code Search, the Hugging Face Hub, and root-level packaging files (requirements.txt, pyproject.toml, etc.) in each OSS repo. The org-scoped grep is org: "import albumentations". - Source: dev.to / 3 months ago
Many applications, like Ollama or LM Studio, wrap some of these and then have their own repositories to pull models from. For best speed and the fastest updates for model support, you generally want to avoid that. You can find all models here: https://huggingface.co. - Source: dev.to / 3 months ago
At the time of this writing, artificial intelligence models have a plethora of modalities, yet typically classify as either Generative or Agentic. For our purposes, let's head over to HuggingFace to find a Text Generation model. - Source: dev.to / 3 months ago
`wget https://huggingface.co/[USER]/[REPO]/resolve/main/[FILE_NAME]` `rm [FILE_NAME]` With Ollama, the initial one-time setup is a little easier, and the CLI is useful, but is it worth dysfunctional templates, worse performance, and the other issues? Not to me. Jinja templates are very common, and Jinja is not always losslessly convertible to the Go template syntax expected by Ollama. This means that some models... - Source: Hacker News / 3 months ago
Seems like so much more work than "just" paying for https://huggingface.co or whichever other neocloud who already did all the setup for you and just waits for your credit card per minute/seconds/token. - Source: Hacker News / 3 months ago
Download your model in GGUF format. Hugging Face has quantized versions of most popular models. - Source: dev.to / 3 months ago
The open-source movement offers hope here. Projects like Hugging Face are democratizing access to state-of-the-art models, while initiatives like Google's TensorFlow provide powerful frameworks without licensing costs. But even open-source solutions require technical expertise that many lack. - Source: dev.to / 4 months ago
Clone the Reka Edge repository from Hugging Face. This includes both the model weights and the inference code:. - Source: dev.to / 4 months ago
Now to answer your question - "Why I would not just load from huggingface?". The answer is - speed. Based on personal experience, and whatever I heard from other devs, when downloading a dataset hosted on kaggle for the first time, it is hell slow. 2nd time, 3rd time, also slow. Next time it will be cached. And so, subsequent downloads will be faster, like a lot faster. Faster than faster. And I run notebooks non... - Source: dev.to / 4 months ago
Training language models from scratch requires enormous computational resources and training data. A single model can cost millions of dollars in infrastructure. Hugging Face solves this problem by providing a repository of ready-to-use models and datasets. - Source: dev.to / 5 months ago
Both models are available on Hugging Face and GitHub, with the 1.7B model occupying 4.54GB and the 0.6B model requiring 2.52GB of storage. - Source: dev.to / 6 months ago
The next best thing is to use the leading open source/open weights models for free or for pennies on OpenRouter [1] or Huggingface [2]. An article about the best open weight models, including Qwen and Kimi K2 [3]. [1]: https://openrouter.ai/models [2]: https://huggingface.co [3]: https://simonwillison.net/2025/Jul/30/. - Source: Hacker News / 7 months ago
The underlying training data goes up to early 2023, and the model was trained in the months leading up to that release. If youโre asking about the ChatGPT product that ships the model to users, it went live in November 2022 and has since received updates (GPTโ3.5, GPTโ4, etc.) that keep it current. ==== But when supposedly running it from https://huggingface.co/chat/models/openai/gpt-oss-20b: ==== when have you... - Source: Hacker News / 8 months ago
Git clone https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev. - Source: dev.to / 8 months ago
Add custom niche models from https://huggingface.co. - Source: dev.to / 8 months ago
Https://github.com/LostRuins/koboldcpp Download models at HuggingFace and run them locally. No logins, no spying, no hidden data harvesting. - Source: dev.to / 9 months ago
We also need a model to talk to. You can run one in the cloud, use Hugging Face, Microsoft Foundry Local or something else but I choose* to use the qwen3 model through Ollama:. - Source: dev.to / 10 months ago
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Is Hugging Face good? This is an informative page that will help you find out. Moreover, you can review and discuss Hugging Face here. The primary details have not been verified within the last quarter, and they might be outdated. If you think we are missing something, please use the means on this page to comment or suggest changes. All reviews and comments are highly encouranged and appreciated as they help everyone in the community to make an informed choice. Please always be kind and objective when evaluating a product and sharing your opinion.
Great resource and community for machine learning and AI.
Excellent platform for AI developers.