Hugging Face has evolved from a repository for niche research into the definitive backbone of the global AI ecosystem. Often described as the "GitHub of Machine Learning", it serves as a central hub where the boundary between research and industry vanishes. By providing a unified platform for open-source models, datasets, and interactive demos (Spaces), Hugging Face democratises access to state-of-the-art Natural Language Processing (NLP), Computer Vision, and Multimodal AI.

Determining the "top" models in such a vast ecosystem is complex, as "likes", trending status, or sheer download volume can measure popularity. However, as we start February 2026, download counts remain the most reliable metric for real-world utility. They represent models being actively integrated into production pipelines, automated workflows, and enterprise applications.

Below, we analyse the top 10 models by download volume (January 2026), exploring their unique architectures and the specific business value they provide in today's AI-driven economy.

1. All MiniLM L6 (v2)

• Downloads Last Month: 142 million
• Source: sentence-transformers/all-MiniLM-L6-v2
• Classification: Sentence Similarity
• License: apache-2.0
• Likes: 4.41K

This LLM model acts as a digital translator that converts text into a compact list of 384 numbers called an embedding. By representing sentences as coordinates in a multi-dimensional space, it allows computers to "calculate" meaning. Sentences with similar topics are placed close together, while unrelated ones are far apart. Trained on over a billion sentence pairs to excel at semantic search and clustering, it effectively finds the intent behind a query rather than just matching keywords.

2. NSFW Image Detection

• Downloads Last Month: 66.8 million
• Source: Falconsai/nsfw_image_detection
• Classification: Image Classification
• License: apache-2.0
• Likes: 973

The model is a Vision Transformer (ViT) specialised for content moderation, specifically designed to distinguish between "normal" and "Not Safe For Work" (NSFW) images. Built upon the google/vit-base-patch16-224-in21k architecture, it leverages a transformer encoder to process images as sequences of patches. It was fine-tuned on a proprietary dataset of 80,000 diverse images. The final model achieves a high evaluation accuracy of 98%, making it a robust tool for automated safety filtering and explicit content detection in digital applications.

3. Google’s ELECTRA Base Discriminator

• Downloads Last Month: 48.4 million
• Source: google/electra-base-discriminator
• Classification: Unclassified
• License: apache-2.0
• Likes: 79

ELECTRA is a highly efficient pre-training method for transformer networks that replaces the traditional "Masked Language Modeling" (MLM) approach with Replaced Token Detection. Instead of masking words and asking the model to predict the missing pieces, ELECTRA uses a small "generator" network to swap certain words with plausible alternatives. The main "discriminator" model then learns to identify which tokens are original and which are "fake." This approach is significantly more compute-efficient because the model learns from every single token in the input, rather than just the small percentage that are typically masked, allowing for state-of-the-art performance even on limited hardware.

4. Fairface Age Image Detection

• Downloads Last Month: 43.5 million
• Source: dima806/fairface_age_image_detection
• Classification: Image Classification
• License: apache-2.0
• Likes: 59

The Fairface age image detection model is another example of a vision transformer designed to classify individuals into nine distinct age groups from images. Built using the nateraw/fairface dataset and licensed under Apache-2.0, the model achieves an overall accuracy of approximately 59%. Performance varies significantly across demographics. It is most effective at identifying young children (0–9 years), where F1-scores reach nearly 80%, but struggles with older age brackets, particularly the "more than 70" category, which shows a low recall of 18%.

5. Google’s BERT Base Uncased

• Downloads Last Month: 40.1 million
• Source: google-bert/bert-base-uncased
• Classification: Fill-Mask
• License: apache-2.0
• Likes: 2.56K

BERT base uncased is a bidirectional transformer model pre-trained on a massive corpus of English books and Wikipedia articles using self-supervised learning. Unlike models that read text left-to-right, BERT also uses MLM to predict hidden words and Next Sentence Prediction (NSP) to understand relationships between sentences, allowing it to capture deep contextual meaning. While it can be used for basic tasks like filling in the blanks, it is primarily designed to be fine-tuned for specific downstream applications such as text classification, sentiment analysis, and question answering. As an "uncased" model, it treats uppercase and lowercase letters identically and ignores accent marks, though it may reflect social biases present in its original training data.

6. MobileNet (v3)

• Downloads Last Month: 22.6 million
• Source: timm/mobilenetv3_small_100.lamb_in1k
• Classification: Image Classification
• License: apache-2.0
• Likes: 43

MobileNet is an efficient image classification model and feature backbone designed for mobile and edge devices. This specific version was trained on the ImageNet-1k dataset using the timm library with a LAMB optimiser and an exponential decay learning rate schedule. With only 2.5 million parameters and 0.1 GMACs, it offers a highly compact architecture optimised for low-latency performance while maintaining accuracy through advanced training techniques like EMA weight averaging and a 224x224 input resolution.

7. Paraphrase Multilingual MiniLM L12 (v2)

• Downloads Last Month: 21.8 million
• Source: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
• Classification: Sentence Similarity
• License: apache-2.0
• Likes: 1.09K

The is a great example of a versatile sentence-transformer model designed to map multilingual text into a 384-dimensional dense vector space. Built on a BERT-based architecture with a 128-token sequence limit, it utilises a mean pooling strategy to convert contextualised word embeddings into fixed-size sentence embeddings. This makes it highly effective for downstream NLP tasks such as semantic search, clustering, and paraphrase detection. While it can be implemented using the standard HuggingFace transformers library with manual pooling, it is optimised for seamless use with the sentence-transformers library for efficient text encoding.

8. All MPNET Base (v2)

• Downloads Last Month: 21.6 million
• Source: sentence-transformers/all-mpnet-base-v2
• Classification: Sentence Similarity
• License: apache-2.0
• Likes: 1.21K

This is also a sentence transformers model and was designed to produce 768-dimensional dense vectors for tasks like semantic search and information retrieval. Fine-tuned from microsoft/mpnet-base on a massive dataset of over 1 billion sentence pairs, it uses a self-supervised contrastive learning objective to maximise semantic accuracy. Unlike the previously mentioned Multilingual MiniLM L12 model, this version includes an additional normalisation step for its embeddings and is specifically optimised for high-quality English sentence representations. While it supports standard library implementations, it is also compatible with Text Embeddings Inference (TEI) for high-speed, production-grade deployment.

Which one should you choose?

1. Choose MiniLM-L12-v2 if you need to support multiple languages or if you are running on limited hardware (CPU) and need the fastest possible inference.
2. Choose all-mpnet-base-v2 if you are working exclusively with English and need the highest possible accuracy for search results or clustering.

9. Facebook’s RoBERTa Large

• Downloads Last Month: 21.1 million
• Source: FacebookAI/roberta-large
• Classification: Fill-Mask
• License: MIT
• Likes: 257

RoBERTa (Robustly Optimised BERT Approach) is a large-scale transformer model pretrained on 160GB of English text using a self-supervised MLM objective. By masking 15% of tokens and challenging the model to predict them, RoBERTa develops a deep, bidirectional understanding of language that is highly effective for downstream tasks like text classification and question answering. While it inherits societal biases present in its internet-based training data, its "large" architecture and case-sensitive nature make it a powerful tool for extracting complex linguistic features.

10. "Powerset" speaker segmentation

• Downloads Last Month: 17.4 million
• Source: pyannote/segmentation-3.0
• Classification: Voice Activity Classification
• License: MIT
• Likes: 729

This is an example of a speaker segmentation model designed to process 10-second mono audio chunks sampled at 16kHz. Utilising a "powerset" multi-class encoding, it identifies up to three distinct speakers and specifically detects overlapped speech by classifying frames into seven categories (including non-speech and specific speaker combinations). While it is highly effective for tasks like Voice Activity Detection (VAD) and overlapped speech detection, it is intended as a building block rather than a standalone solution. For full-length recording diarisation, it must be integrated into a larger pipeline (such as pyannote/speaker-diarization-3.0) that incorporates speaker embedding models.

Top 10 most liked models on Hugging Face

Filtering instead by “most liked models” on Hugging Face, we get a very different picture of the projects that excite AI software engineers. Aside from the obvious lack of correlation between the superfluous “likes” metrics and actual downloads, there’s another interesting difference in repository licenses assigned to those LLM models. Different open-source and proprietary licenses impose varying legal requirements. Dataset licenses may restrict usage, distribution, or commercialisation.

How licensing affects AI development

Modern AI development is navigating a complex spectrum of licensing, primarily split between "Open Weights" models and truly "Open Source" (OSI-approved) software. Models like Meta’s Llama 3.1 and Google’s Gemma utilise custom licenses that, while accessible, impose significant restrictions, such as user-capacity caps, prohibitions on training competing models, or specific redistribution requirements. Similarly, the OpenRAIL++ license used by Stable Diffusion XL permits commercial use but legally binds developers to strict "Responsible AI" terms, prohibiting use cases like medical advice or deceptive content. These models offer transparency and accessibility without granting the full legal freedom found in traditional open-source ecosystems.

In contrast, models like DeepSeek’s R1 and OpenAI’s Whisper employ highly permissive licenses such as MIT and apache-2.0 respectively, which are the gold standard for commercial flexibility. The MIT license is concise and allows for modification and monetisation with almost no strings attached, while the Apache 2.0 license provides similar freedoms but adds robust legal protections regarding patents and trademarks.

To manage this legal maze, Cloudsmith ML Registry enables enterprises to govern ML models and datasets by enforcing strict policies. This ensures that only models with approved, compliant licenses enter the production pipeline, safeguarding your operations teams from the legal risks associated with restrictive or non-commercial terms. While the ease of downloading models from Hugging Face fuels rapid innovation, building a resilient enterprise on these foundations requires rigorous governance. Cloudsmith provides the control and oversight necessary to turn experimental AI into a secure, commercially viable reality.

Don't let AI licensing slow you down. Book a personalised demo to see how Cloudsmith can help you manage your ML models in your software development lifecycle.