Hugging Face
The AI community building the future.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Reviews for Hugging Face
Hear what real users highlight about this tool.
Makers widely credit Hugging Face as essential infrastructure for building and shipping AI. The makers of Cartesia Sonic rely on it for open models and datasets, while the makers of Sagehood AI call it the leading place to host and collaborate. The makers of Meilisearch praise its OSS ecosystem for developers. User feedback echoes this: rich model selection, active community, easy inference endpoints, and strong tooling. Minor caveats note heavier models and a learning curve, but overall sentiment is highly positive.
This AI-generated snapshot distills top reviewer sentiments.
Utilized the Hugging Face Model Card
The easiest way to play with open LLMs and embeddings.
For wonderful ML resources on so many fronts!
For open-source baselines used in early LLaMA/Zyhler model optimization. Hugging Face helped us benchmark performance before building fully private weights.
The community building the future of AI
Hugging Face – The AI community building the future of open-source machine learning. I use their models and datasets for experimenting with voice synthesis and fine-tuning. Why I like it: Hugging Face makes cutting-edge AI accessible — their open ecosystem and community are amazing for learning, prototyping, and collaboration.
We’ve built a lot of our NLP foundation on top of Hugging Face’s ecosystem. Their datasets, like embedding-data/Amazon-QA, gave us great insights into how people naturally ask product-related questions and do research online. For text similarity and competitor mapping, we use Sentence Transformers models such as paraphrase-multilingual-MiniLM-L12-v2 and all-MiniLM-L6-v2, which handle both English and German really well. Our NER pipeline runs on spaCy transformer models (en_core_web_trf and de_core_news_md), both powered by Hugging Face under the hood, to extract company and product names across languages. Overall, Hugging Face made it surprisingly smooth to combine multilingual understanding, embeddings, and entity recognition into one cohesive system.
The ever so dependable Hugging Face community. Their models and open ecosystem gave us the foundation to experiment, fine-tune, and deliver accurate script analysis at scale. Grateful for their incredible community-driven innovation.
Shoutout to Hugging Face — we rely on their embeddings and libraries to understand code context at scale.
Hugging Face is the hub for open-source AI. The community, model zoo, and APIs are way more accessible than any alternative.
Hugging Face is the backbone of our open-source AI strategy. It's an essential platform for finding the best models, and more importantly, it provides the infrastructure for us to securely host our own recommended models for our users to download.
Hugging Face provides state-of-the-art AI models and tools that allow us to deliver instant, accurate feedback on IELTS writing. Its ecosystem is developer-friendly, scalable, and keeps us at the cutting edge of AI, unlike building custom models from scratch.
Hugging Face is more than a library; it's the backbone of the open-source AI community. Their transformers library and model hub allowed us to experiment and build on state-of-the-art NLP models without starting from scratch. It drastically accelerated our development.