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Building trust from source to ship

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

 hosted on Hugging Face. While this democratisation of AI is changing how all work and develop with AI, it also introduces a massive supply chain risk. Every time a developer runs 

, they are essentially pulling an opaque blob of data from a public registry.

The core of the issue? We often don't know the provenance of these models, their true licensing constraints, or (most importantly) what code is tucked inside their weight files.

What is a Pickle and why is it dangerous?

 is the standard way to serialise object structures into binary. In machine learning, it is the default format for 

However, the Pickle protocol is not just a data format; it is a stack-based virtual machine. When you 

 a file, you aren't just reading data, you’re also executing a sequence of instructions (

Pickle was never designed to be secure against erroneous or maliciously constructed data. A malicious actor can craft a pickle file that, when opened, executes arbitrary code on your machine, like opening a reverse shell or exfiltrating environment variables.

You can see this in action using open-source tools like 

. By scanning a known dummy malicious model (

), we can see exactly where the trap was set.

Again, this is a dummy model. It’s not actually malicious. To my knowledge it doesn’t do anything. This is purely to demonstrate what a picklescan is looking for:

 during the load process, which executes the arbitrary code. This is the smoking gun of a serialisation attack. 

The scanner can also load Pickles from local files, directories, URLs, and zip archives.

For the purpose of demonstration, let’s scan the 

sshleifer/tiny-distilbert-base-cased-distilled-squad

Preventing model serialisation attacks with Modelscan

As discussed above, ML models are shared publicly over the internet, within and across teams. The rise of Foundation Models resulted in increasing consumption and further training/fine tuning of public ML models. Because developers use ML models to make critical decisions and power mission-critical applications, it makes sense that there would be more than one open-source scanning option, especially when vulnerabilities can appear in other scanners such as 

 that scans models to determine if they contain unsafe code. It is the first model scanning tool to support multiple model formats. ModelScan currently supports: 

Scanning the entire local Hugging Face cache on my Macbook with Modelscan showed nothing suspicious when I scanned the original 

’s Totally Harmless Model we downloaded earlier.

Then run the below commands to create the model and scan it with Modelscan:

The above Python script provides a classic example of why pickling is dangerous and why tools like 

, you tell Python exactly how to reconstruct the object when it's loaded. By putting 

 inside that method, you turn a simple data file into an executable script.

Once you point Modelscan at the correct filename, it should successfully flag 

When you’re done, you can clean up all the newly-created files with the below command:

To secure your AI pipeline, you should adopt a defence-in-depth strategy:

 format, which is designed to be zero-copy and, crucially, non-executable. This is the ideal starting point.

: Because some models force us to use file formats considered “less safe” than safetensors, we should only load models from trusted organisations, where possible. For example, I trust Nvidia or Google more than random publishers on the internet.

Automated protection with Cloudsmith’s Rego policies

Manually scanning every model isn't scalable. This is where Cloudsmith comes in. In a 

, we explained how to use Cloudsmith as your private proxy for AI models, and how to enforce Hugging Face policies in Enterprise Policy Manager (EPM) to automatically block potentially infected or unsafe models before they ever reach a developer's machine.

Example 1: Blocking based on scan results

The nice thing for Hugging Face users is that Hugging Face Hub already performs 

 on all LLM Models. It then flags a model as safe or not. This Cloudsmith 

 policy rejects ingesting any model Hugging Face’s internal security scan did not mark as "SAFE".

But as discussed earlier, we can run standalone picklescan almost anywhere to scan for files deemed insecure and verify those results against the existing Cloudsmith scans. However, it might be worth considering a more restrictive approach to the risky file formats that we accept into production.

Example 2: Reducing the potential attack surface by extension

If your LLMOps and security teams have standardised on the 

 format, you can explicitly block legacy or high-risk formats. This reduces the overall attack surface by preventing the entry of formats known to support execution (like 

Like a funhouse mirror, securing AL models isn’t always what it seems. Securing generative AI is about more than just protecting the code – it’s about protecting the data that behaves like code. By utilising flexible Rego policies within Cloudsmith, you move from a reactive posture (scanning after the fact) to a proactive one (blocking by policy).

Check out our upcoming webinar with great insights about Hugging Face and evolving AI threats:

Want a broader framework for protecting AI artifacts and model supply chains? Explore our 

securing non-deterministic systems in LLMOps

Cloudsmith Blog

Top 10 most popular LLM models on Hugging Face

Securing LLM dependencies against serialisation attacks