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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 

), 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.

 (January 2026), exploring their unique architectures and the specific business value they provide in today's AI-driven economy.


This LLM model acts as a digital translator that converts text into a compact list of 384 numbers called an 

. 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 

, it effectively finds the intent behind a query rather than just matching keywords.

) specialised for content moderation, specifically designed to distinguish between "normal" and "Not Safe For Work" (NSFW) images. Built upon the 

 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.

 is a highly efficient pre-training method for transformer networks that replaces the traditional "Masked Language Modeling" (

. 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.


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 

 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%.

 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 (

) 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.


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 

 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)

sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2


The is a great example of a versatile sentence-transformer model designed to map multilingual text into a 

 with a 128-token sequence limit, it utilises a 

 strategy to convert contextualised word embeddings into fixed-size sentence embeddings. This makes it highly effective for downstream 

 tasks such as semantic search, clustering, and paraphrase detection. While it can be implemented using the standard HuggingFace 

 library with manual pooling, it is optimised for seamless use with the 


This is also a sentence transformers model and was designed to produce 

 for tasks like semantic search and information retrieval. Fine-tuned from 

, 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 

 for its embeddings and is specifically optimised for high-quality English sentence representations. While it supports standard library implementations, it is also compatible with 

 for high-speed, production-grade deployment.


 if you need to support multiple languages or if you are running on limited hardware (CPU) and need the fastest possible inference.

 if you are working exclusively with English and need the highest possible accuracy for search results or clustering.


RoBERTa (Robustly Optimised BERT Approach) is a large-scale transformer model pretrained on 160GB of English text using a 

 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.


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 (

) 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 

) that incorporates speaker embedding 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 

 assigned to those LLM models. Different open-source and proprietary licenses impose varying legal requirements. Dataset licenses may restrict usage, distribution, or commercialisation.

Modern AI development is navigating a complex spectrum of licensing, primarily split between "

) 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 

 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.

 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.

Nigel Douglas

Head of Developer Relations

Secure LLM models

Top 10 most popular LLM models on Hugging Face

Exploring AI and the future of the software supply chain

AI assistants will shape the future of the software supply chain, and today, we’re sharing a glimpse of a powerful idea in motion: 

 server that connects large language models (LLMs) like ChatGPT and Claude directly to your software supply chain using the emerging 

Imagine managing policies, surfacing security insights, or orchestrating package workflows with a simple natural language prompt. No dashboards, no scripting, just a simple question or instruction that delivers the right end result. If this sounds interesting, you should 

This isn’t a launched product (yet), but it’s a clear signal of where we think Cloudsmith is headed: toward 

 that make it easier to control your software supply chain.

is a standard way to make information available to LLMs. Similar to an API, MCP provides a documented, standardized way for a client to integrate services from external sources.

MCP enables AI assistants to query and interact with software supply chain data, so it creates a foundation for smarter workflows and more accessible control over artifact management.

In essence, adding MCP capabilities to Cloudsmith offers up an entirely new interface to your software supply chain, with AI agents and LLMs working together to achieve your goals. We're exited to see MCP becoming a flexible new point of integration between our customers' third-party services and Cloudsmith.

MCP server demo

Our MCP prototype leverages Cloudsmith’s API-first architecture to provide programmatic access to developers through their AI assistant. Through standardized MCP requests, you can ask questions about your software supply chain or take safe, controlled actions - like managing policies or promoting packages - directly within Cloudsmith. This opens up conversational workflows with the Cloudsmith platform.

For example, you might ask your AI assistant to identify what package is trending up in usage month over month or see all of the versions of a particular package in use across your workspace. In the future, your AI assistant will be able to review your organization’s compliance and security requirements and recommend or create a set of policies in Cloudsmith’s Enterprise Policy Manager based on that input.

Building this prototype was a good reminder of a few fundamentals. First, an API-first foundation makes it easier to experiment, so it’s good that Cloudsmith is built truly API-first. Second, the data running through platforms like Cloudsmith can offer valuable insights when it’s made accessible to clients in lots of different ways. We’re giving developers and AI assistants simpler ways to ask questions, take actions, and get things done.

There’s still plenty to figure out, but MCP is already making artifact management and software supply chain management more intuitive and straightforward, especially at enterprise scale.

 for select customers and partners. If you're exploring the intersection of DevOps, AI, and supply chain security, we’d love to work with you. 

Alison Sickelka

VP of Product

Prototyping an MCP Server

As Generative AI (GenAI) reshapes the software development landscape, the risks and complexities around managing what gets built, where it comes from, and how it’s secured are growing just as fast. The 

Cloudsmith 2025 Artifact Management Report

 dives into this shift, offering critical insights into how teams are adapting their infrastructure and software supply chain security practices in response to the AI-generated code.

42% of developers using AI report that at least half of their codebase is now generated by AI tools

. Where in 2024 this might have been considered a theoretical shift, in 2025 we need to accept that this is not a practical reality reshaping how software is written. AI isn't just a helper on the sidelines; it’s actively contributing to the core of modern applications and transforming the day-to-day workflow of developers. While the rapid rise of GenAI is often seen as a leap forward in productivity and innovation, it also introduces new security risks and software supply chain vulnerabilities that can’t be ignored.

Attackers Are Targeting the Software Supply Chain

Adversaries are increasingly shifting their focus from traditional infrastructure attacks to targeting the software supply chain itself. Rather than breaching networks directly, they’re infiltrating the ecosystem through techniques like 

 (the creation of malicious packages with names deliberately designed to mimic legitimate ones).

These deceptive packages are easy to overlook, especially in fast-paced development environments. Developers, often under tight deadlines and relying heavily on AI-assisted tooling to manage dependencies, may unknowingly pull in these malicious components without thorough vetting. The result is a stealthy but highly effective method for attackers to introduce vulnerabilities directly into production code.

AI and automation: a double-edged innovation

Survey respondents anticipate that GenAI-driven automation will bring both benefits and complications, which should accelerate development workflows while also straining infrastructure with unpredictable behaviors and dependency complexities. Tools like 

 promise greater speed and efficiency in coding, but they also raise new red flags for security teams. As automation becomes more deeply embedded in the development process, the potential for AI-generated code to introduce unknown risks or unstable dependencies grows, which challenges the long-held assumptions about trust and reliability in the software pipeline.

These concerns are further amplified by the widespread adoption of Large Language Model (LLM)-based technologies. While LLMs can significantly boost developer productivity, they also pose new threats (such as recommending non-existent or harmful packages). When asked whether AI would worsen open-source 

 risks like typosquatting or dependency confusion, nearly 

a third warning of a significant rise in exposure

These evolving development patterns are expanding an already vulnerable software supply chain. Without rigorous artifact validation and integrity checks, developers face growing risks - not just from external attackers, but also from the very tools designed to help software development teams move faster.

To address these emerging threats, modern artifact management solutions must embed intelligent access controls and offer end-to-end visibility into artifact provenance. With dynamic access control policies, organizations can ensure only authorized users and processes can interact with sensitive assets, reducing the likelihood of unauthorized changes or malware insertion. Coupled with robust policy-as-code frameworks, enterprises can establish and enforce security protocols that adapt as threats evolve, especially in environments where AI-generated code is becoming commonplace.

Where in the SDLC are AI-generated risks coming from?

While the speed and efficiency benefits of using AI in software development are clear, the oversight is not.

Only 67% of developers surveyed review AI-generated code before every deployment

, leaving large portions of production code potentially unvetted. This is quickly becoming a growing vulnerability in the software supply chain. This behaviour, driven by a desire to move faster, is dramatically expanding the attack surface. AI isn’t just introducing new code, it’s also introducing new risks, often at scale. Traditional concerns like artifact integrity, dependency management, and 

 (Software Bill of Materials) are being compounded by AI’s ability to rapidly consume and reuse unknown or untrusted code.

41% of respondents identified code generation as the riskiest point of AI influence

, yet practices around review and trust remain inconsistent:

66% said they only trust AI-generated code after manual review.

Just 20% fully trust AI output without extra scrutiny.

59% apply additional reviews to AI-generated packages, but only 16% treat them like any other package, without further checks.

The Need for Secure Artifact Management in the AI Era

This uneven approach is happening against a backdrop of expanding AI usage: alarmingly, 

86% of organizations have seen more AI-influenced dependencies in the last year, with 40% seeing a significant increase

29% of teams feel very confident in their ability to detect malicious code in open-source libraries

, which is the very ecosystem where AI tooling tends to source its suggestions. The threats, according to those surveyed, range from sensitive data leakage to the inclusion of compromised dependencies. These risks are amplified when developers blindly trust AI-generated code or package suggestions without security oversight.

These findings point to a critical inflection point. AI is becoming a core contributor to the software stack, but we haven’t fully adapted our trust models, tooling, or policies to match this new reality. Relying on developers to manually spot every risk (especially under time pressure) is not sustainable.

What’s needed is a secure checkpoint for AI-assisted development:

Automatically-enforced policies to catch unreviewed or untrusted AI-generated artifacts.

Artifact provenance tracking to distinguish between human-authored and AI-authored code.

Integration of trust signals directly into the development pipeline, so that reviews become automatic rather than an optional decision.

Download the 2025 Cloudsmith Artifact Management report

Explore the latest trends, insights, and best practices for securing your software supply chain and managing AI-generated code responsibly.

2025 Cloudsmith Artifact Management Report

From AI to Scalability: 2025 Trends in Artifact Management

Cloudsmith Blog

Top 10 most popular LLM models on Hugging Face

Prototyping an MCP Server

AI is now writing code at scale - but who’s checking it?