Text analysisRU2026
Liquid AI · USA
Small decision models: from text, an image or a voice clip they answer yes or no, pick an option or give a rating in one pass, without writing text. d1-3B sees images, d1-omni-600M also hears speech up to 30 seconds. Russian is listed. The open d1-2B (text, pictures and video, based on LFM3-VL-2B) and d1-Omni-2B (sound as well) decide in milliseconds, 5.7 ms on an H100 by the authors measurements; besides choosing an answer they pull out text, numbers and dates with their positions and find objects in a picture. It is not a chat model.
- routing requests and tickets to teams
- checking a photo for defects or violations against a list of questions
- routing voice commands
- Sizes
- 600M, 2B and 3B
- Hardware
- from: Laptop
Text analysisRU2026
vLLM Semantic Router project · international project
An open family of models for an AI gateway: they answer a request with probabilities without generating text. They detect topic and urgency, jailbreak attempts, harmful requests, personal data and whether a tool is needed. Small versions run on a CPU, Vela understands 17 languages including Russian. The new d3 (27B) also reads several images and videos, and d3-turbo is, by the authors' own estimate, 4 points more accurate on text. The newest in the line, d3-ultra, came out on October 24.
- routing requests between models and teams
- checking a request for a jailbreak attempt or harmful content before the main model
- finding personal data with positions in a text
- Sizes
- 0.3B – 27B
- Hardware
- from: Laptop
Code2026
Recursive · not stated
An open expert model (39.8B parameters, 2.5B active per token) for coding agents: it digs into someone else's repository, fixes bugs, writes functions and tests, and can reason and call tools over several steps. Trained from scratch, 262 thousand token context, text only. It weighs about 80 GB, and the authors run it on a single B300 or H200 card through SGLang. By the authors' own tests: Terminal-Bench 2.1 54.9%, SWE-bench Pro v2 89.7%, SWE-bench Verified 74.2%. Part of the comparisons come from other developers' own reports, and the test conditions differ between sources.
- an agent for fixing bugs and extending a repository in the terminal
- writing tests and new functions from a description
- a local developer assistant on your own server
- Sizes
- 39.8B-A2.5B
- Hardware
- from: 1 GPU
Text analysis2026
Contrastive-LM (per the announcement: Stanford together with NVIDIA) · USA
An open fast-choice model for agents: it takes a description of a situation and a set of options and returns the probability of each without generating text. It can answer yes or no, pick from a list and give a score on a scale. Actions and state are encoded separately, so action vectors can be computed in advance: by the authors' measurements, with a choice among about a thousand options it is up to 13 times faster than the Jev analogue. After fine-tuning it works as a verifier that picks the best of several solutions of a coding agent (DeepSWE 81.6%, Terminal-Bench 2.1 87.6% by their measurements). It needs Linux, an NVIDIA card and Qwen3-8B in vLLM, and the card language is English.
- fast choice of an agent's next action from given options
- checking and ranking several solutions of a coding agent
- routing requests: department, urgency, a score on a scale
- Sizes
- 8B (a frozen Qwen3-8B plus two small projection heads)
- Hardware
- from: 1 GPU
Text2026
Reflection AI · USA
An open model from the American company Reflection with 501B parameters (23B active, a 1 million token context), text only, mainly English. Stated as economical in reasoning: according to the authors it needs about three times fewer tokens for thinking at comparable quality. Strong in programming, tool calling and science. Weights in BF16, FP8 and NVFP4. The benchmark figures are the authors' own.
- an agent for solving tasks in a repository and a terminal
- long tasks with tools and search (via MCP)
- solving scientific and mathematical problems with reasoning
- Sizes
- 501B-A23B
- Hardware
- from: Cluster
Text2026
Shanghai Artificial Intelligence Laboratory · China
An agent model for long tasks: searching and gathering evidence, research, development, preparing reports and presentations, analyzing vulnerabilities in an authorized environment. A preview based on GLM-5.2, 256K-token context, text only. An FP8 version exists.
- deep search and research on a topic with a verifiable result
- developing programs and preparing reports and presentations
- checking and fixing vulnerabilities in an authorized environment
- Sizes
- 744B (MoE, based on GLM-5.2)
- Hardware
- from: Cluster
Text analysis2026
Shanghai Artificial Intelligence Laboratory · China
A decision model based on Qwen3.5-4B: from text and images it answers all questions about a situation with probabilities in one pass, without generating text. Suits routing requests and rating urgency.
- routing requests to teams
- rating priority and urgency of requests
- checking from a photo that everything is in order
- Sizes
- 4B
- Hardware
- from: Laptop
Text analysis2026
AutoTrust AI · Singapore
Open decision models: for yes/no, pick-one and rate-0-to-5 questions they answer with probabilities in one pass, and can also reason when needed. Not affiliated with the closed Jev from TypeSafe.
- routing requests to the right team
- checking an agent action before it runs
- rating urgency and tone of messages
- Sizes
- 9B – 35B
- Hardware
- from: 1 GPU
Text analysisOllama2026
Cloudflare · USA
Decision models: they do not write text but read a situation (text, JSON, an image, video frames, and for Clef-Omni also audio) and return in one pass the probability of each answer to typed questions: yes or no, pick one, rate on a scale. The agent gets ready numbers instead of parsing free text.
- sorting requests and tickets into categories with a probability
- checking a document or photo against a list of questions (is everything stated, is there a violation)
- choosing the next step of an agent without parsing a text answer
- Sizes
- 27B, 9B (Clef-flash) and 30B-A3B (Clef-Omni)
- Hardware
- from: Laptop
Plates and vehicles2020–2026
Mikel Brostrom (open project) · not disclosed
A set of trackers: it links the same vehicle across frames and cameras, which is how flow and time on site are counted. Filming vehicles and people and storing the records is personal data processing: agree the procedure with a lawyer.
- Counting vehicles crossing a line
- Following a vehicle across frames
- Finding the same vehicle on another camera
- Sizes
- trackers plus re-identification models from compact to large
- Hardware
- from: Laptop
Text analysisGGUF2026
FlowX.AI · USA and Romania
Small models from a banking software company that read the text of an SMS, email or chat message and explain which scam tactic it uses. A human makes the decision on a disputed operation; automatic blocking without review is not acceptable.
- Flagging incoming messages that show signs of deception
- Explaining which phrase triggered the flag
- Filtering emails and messages before they reach an operator
- Sizes
- 0.6B – 1.7B
- Hardware
- from: Laptop
Faces2021–2026
InsightFace (deepinsight) · China
The most widely used open toolkit for face detection and recognition. Many identity-preserving image generators are built on it. The pretrained weights are non-commercial.
- Detecting and comparing faces in photos
- Face-based access in prototypes
- Face processing as part of other AI systems
- Sizes
- packages from 16 MB to 407 MB
- Hardware
- from: Laptop
Plates and vehicles2026
Intel · USA
Intel models for vehicle video analytics: car, truck, bus, motorcycle, plus entry and exit logging and vehicles stopped too long. Filming vehicles and people is personal data processing: agree the procedure with a lawyer.
- Counting vehicles and splitting them by type
- Logging vehicle entry and exit
- Monitoring parking space occupancy
- Sizes
- YOLO26 variants from nano to x
- Hardware
- from: Laptop
Deepfake detection2023–2026
Adobe Research and University of Surrey · USA
An image watermark for arbitrary resolutions built for the Content Authenticity Initiative: it can both apply a mark and remove one. The detector errs in both directions - a human reviews the output.
- Marking images on the way out of your own pipeline
- Checking the provenance of a submitted image
- Linking with content provenance metadata
- Sizes
- model types Q and P with different mark capacity
- Hardware
- from: Laptop
Acoustic monitoring2019–2026
Google · USA
A tiny model covering 521 sound events, including alarms, breaking glass and screams: it fits on a microcontroller and runs without a GPU. A microphone can capture people voices, which is personal data, so check the procedure with a lawyer.
- Fast sound labelling right on the device
- Detecting alarm sounds on a site
- Picking interesting fragments out of a continuous recording
- Sizes
- 3.7M
- Hardware
- from: Laptop
Finance2024–2026
MaLA Lab, Singapore Management University (with University of Technology Sydney) · Singapore
A pretrained model that finds anomalous nodes in a relationship graph and marks suspicious vertices on a new graph without training on it. A human makes the decision on a disputed operation; automatic blocking without review is not acceptable.
- Finding suspicious accounts and counterparties linked to each other
- Highlighting atypical participants in a payment graph
- Testing hypotheses about ring schemes and chains of intermediaries
- Sizes
- weights file about 1.5 MB
- Hardware
- from: Laptop
Deepfake detection2025–2026
University of Michigan · USA
A lightweight detector of generated images, trained on 2.7M samples from nearly 5000 different generators. It errs in both directions: the result is a reason for a human to check, not proof.
- Checking submitted photos and illustrations
- Filtering AI images in a content flow
- Flagging suspicious images for manual review
- Sizes
- 22M
- Hardware
- from: Laptop
Acoustic monitoring2021–2026
MLCommons · USA
A reference autoencoder for finding anomalies in machine sound: it learns only from recordings of a healthy unit and flags deviations. The system does not make a diagnosis, it gives you a reason to check the unit before it fails.
- Listening to a machine tool, pump or conveyor
- Flagging deviations from a unit usual noise
- Training on your own recordings of healthy equipment
- Sizes
- a tiny fully connected autoencoder sized for microcontrollers
- Hardware
- from: Laptop
Deepfake detection2023–2026
University of Wisconsin-Madison · USA
An early and still used approach: a simple classifier trained on top of a frozen CLIP that transfers to unseen generators. It errs in both directions - the output needs a human check.
- Checking images from new, unfamiliar generators
- A baseline when comparing detectors
- Fast rollout of a check without training a large model
- Sizes
- a linear classifier on top of CLIP ViT-L/14
- Hardware
- from: Laptop
Image + text2026
OpenMOSS (Fudan University) · China
An image + video + text model focused on long videos and precise linking of events to timestamps. A Realtime version handles live video streams.
- Analyzing long videos and finding events by time
- Real-time streaming video analysis
- Understanding photos and documents
- Sizes
- about 11B
- Hardware
- from: 1 GPU
Plates and vehicles2024–2026
ankandrew (open project) · not disclosed
Ready-made ONNX detectors that find the license plate in a frame, plus general object detectors. Filming vehicles and people and storing the records is personal data processing: agree the procedure with a lawyer.
- Finding the plate in a camera frame
- Preparing a crop for character reading
- Picking frames with vehicles out of a video stream
- Sizes
- YOLO-v9 detectors at input sizes from 256 to 640 pixels
- Hardware
- from: Laptop
Voice: speakers and soundGGUF2022–2026
WeNet community · China
A set of ready-made voiceprint models: checks whether the same person speaks in two recordings and helps split a recording by speaker. One of the models is built into pyannote 3.x.
- Voice verification of a customer during a call
- Finding repeat calls from the same person
- Splitting a recording by speaker
- Sizes
- from a few to tens of millions of parameters
- Hardware
- from: Laptop
Moderation and safety2024–2026
GLiNER community (Fastino, Knowledgator, NVIDIA and others) · USA
Small GLiNER-based models for finding personal data: passports, phone numbers, accounts, addresses. Data types are set in words. Russian is not officially supported.
- Masking personal data before cloud AI
- Finding passport data and bank details in documents
- Checking data exports for leaks
- Sizes
- about 200M to 500M
- Hardware
- from: Laptop
Image + text2023–2026
Shanghai AI Lab (OpenGVLab) · China
A family of video models: encoders for search and classification of clips, and chat models that analyze long videos. InternVideo 3 is designed for multi-hour recordings.
- Searching a video archive with a text query
- Action recognition in video
- Answering questions about a long recording
- Sizes
- small encoders – 9B
- Hardware
- from: Laptop
Image + textOllama2024–2026
Moondream (M87 Labs) · USA
A small, fast vision model for product use cases: answering questions, finding and pointing to objects, captions. Moondream 3.1 is a 9B MoE with 2B active.
- Finding and counting objects in photos
- Checking photos from field reports
- Captions and tags for a catalogue
- Sizes
- 2B – 9B-A2B
- Hardware
- from: Laptop
Acoustic monitoring2019–2026
University of Surrey · UK
The classic set of convolutional networks that label sound across the 527 AudioSet categories, from machinery noise and alarms to breaking glass and screams. The system does not make a diagnosis, it gives you a reason to check the unit before it fails. A microphone can also capture people voices, which is personal data, so check the procedure with a lawyer.
- Labelling what is happening in a microphone recording
- Detecting alarms, breaking glass, screams
- A base for your own model tuned to one shop floor
- Sizes
- from 5.9M (CNN6) to 81.9M (CNN14)
- Hardware
- from: Laptop
Computer vision2025–2026
Roboflow · USA
Real-time object detector, an open alternative to YOLO without AGPL. Supports segmentation (object outlines) and, since 2026, keypoints.
- Object detection in video and photos
- Precise outlines of parts and defects
- Fine-tuning for your own object classes
- Sizes
- Nano – 2XL
- Hardware
- from: Laptop
Moderation and safety2024–2026
NVIDIA · USA
NVIDIA content filters for bots, with separate models for keeping the conversation on topic and detecting jailbreaks. Safety Guard v3 was trained on 9 languages; Russian was tested only without fine-tuning.
- Checking bot requests and replies
- Keeping the bot within its topic
- Detecting attempts to bypass rules
- Sizes
- 4B – 8B
- Hardware
- from: Laptop
Plates and vehicles2025–2026
MorseTechLab · not disclosed
The most downloaded plate detector on Hugging Face: it finds the plate in a frame, and a separate model then reads it. Filming vehicles and people and storing the records is personal data processing: agree the procedure with a lawyer.
- Finding the plate in camera video
- Preparing crops for plate reading
- Testing a plate recognition idea without training
- Sizes
- a fine-tuned YOLO11, with an ONNX build available
- Hardware
- from: Laptop
Moderation and safetyOllama2024–2026
IBM · USA
IBM judge models: they catch harm, profanity and jailbreak attempts, and in RAG and agents check whether an answer is grounded in the documents. You can state your own rule in words.
- Checking bot requests and replies
- Finding made-up facts in knowledge-base answers
- Checking your own rules written as text
- Sizes
- 38M – 8B
- Hardware
- from: Laptop
Moderation and safety2026
OpenAI · USA
Finds and hides personal data: names, addresses, phone numbers, emails, account numbers, passwords. Runs even in the browser. Trained mostly on English.
- Removing personal data from text before sending it to cloud AI
- Finding passwords and keys in texts
- Anonymizing correspondence for analytics
- Sizes
- 1.5B (50M active)
- Hardware
- from: Laptop
Acoustic monitoring2023–2026
Xiaomi · China
Compact sound-labelling models from 5.5M to 86M, with ONNX and INT8 builds that run on an ordinary CPU and on a board next to the equipment. The system does not make a diagnosis, it gives you a reason to check the unit before it fails.
- Labelling sound on modest hardware and on site
- Detecting alarm sounds and abnormal noise
- Fine-tuning for your own set of equipment sounds
- Sizes
- 5.5M - 86M
- Hardware
- from: Laptop
Acoustic monitoring2024–2026
Xiaomi · China
A general-purpose audio encoder trained on 272 thousand hours of speech, music and noise: it gives features on top of which you train your own classifier of abnormal sounds. The system does not make a diagnosis, it gives you a reason to check the unit before it fails.
- Audio features for your own anomaly model
- Finding similar fragments in a recording archive
- Fine-tuning for the sounds of one production line
- Sizes
- 86M - 1.2B
- Hardware
- from: Laptop
Plates and vehicles2024–2026
ankandrew (open project) · not disclosed
A ready pipeline for plate recognition: plate detector and character reading in one package, with your own models pluggable. Filming vehicles and people and storing the records is personal data processing: agree the procedure with a lawyer.
- Getting plate recognition running quickly on site
- Automatic vehicle access by list
- Logging vehicle entry and exit
- Sizes
- a lightweight plate detector paired with XS and S reading models
- Hardware
- from: Laptop
Plates and vehicles2024–2026
ankandrew (open project) · not disclosed
Lightweight models that read characters from an already cropped license plate and run on an ordinary CPU. Filming vehicles and people and storing such records is personal data processing: agree the procedure and retention with a lawyer.
- Reading a plate at entry and exit
- Matching a plate against a whitelist of passes
- Going through archived camera footage
- Sizes
- XS and S models on the CCT architecture
- Hardware
- from: Laptop
Plates and vehicles2022–2026
PaddlePaddle (Baidu) · China
A ready set of models for vehicle tasks: plate recognition, car color and type, counting entries and exits, spotting parking where it is not allowed. Filming vehicles and people is personal data processing: agree the procedure with a lawyer.
- Counting traffic flow
- Identifying car type and color
- Reading the plate at the entrance
- Sizes
- a set of models: vehicle detector, attributes, plate reading on PP-OCR
- Hardware
- from: Laptop
Plates and vehicles2026
Autolane · not disclosed
A recent set of plate recognition weights: two plate detectors and a reading model, trained on US plates, so the Russian format needs fine-tuning. Filming vehicles and people is personal data processing: agree the procedure with a lawyer.
- Finding the plate in a frame
- Reading the plate characters
- Running on cameras and single-board computers
- Sizes
- RF-DETR and YOLOX-S detectors, CCT-S for character reading
- Hardware
- from: Laptop
Cybersecurity2025–2026
Cisco (Foundation AI) · USA
Cisco models for information security based on Llama 3.1 8B: analysis of vulnerabilities, threats and incidents. Can be deployed inside your own perimeter.
- Analyzing vulnerability and threat reports
- Helping SOC analysts during incidents
- Mapping threats to MITRE ATT&CK
- Sizes
- 8B
- Hardware
- from: Laptop
Computer vision2023–2026
Ultralytics · USA
The most widely used real-time object detector: finds and marks items in video even on modest hardware. YOLOv5 came out back in 2020; the catalog starts from YOLOv8.
- Counting people, cars and goods on video
- Checking hard hats and workwear
- Spotting defects on the production line
- Sizes
- 2.4M – 68M
- Hardware
- from: Laptop
Moderation and safety2025
ServiceNow · USA
A guard model that catches both harmful content and attacks on AI (prompt injection, jailbreaks), including when agents use tools.
- Screening chatbot requests for attacks and jailbreaks
- Filtering harmful model answers
- Monitoring the actions of AI agents that use tools
- Sizes
- 8B
- Hardware
- from: Laptop
Computer vision2025
Meta · USA
Meta's family of encoders for images and video, and with PE-AV also for audio. PE-Core searches by text more accurately than SigLIP 2 (per Meta); small versions are available.
- Search photos and videos by description
- Catalog labeling and tagging
- Search across audio and video (PE-AV)
- Sizes
- size not stated on the model card
- Hardware
- from: Laptop
Deepfake detection2024–2025
Meta · USA
A watermark for video and images that survives re-encoding and cropping. The detector errs in both directions: a missing mark does not prove a forgery, and finding one is a reason for a human to check.
- Marking video created or processed by AI
- Finding your own mark in re-uploaded clips
- Protecting ad materials from being reused as someone else's
- Sizes
- a mark of 96 to 1024 bits
- Hardware
- from: Laptop
Deepfake detection2025
National Institute of Informatics, Yamagishi Lab · Japan
Seven speech encoders (wav2vec 2.0, XLS-R, MMS, HuBERT) post-trained to tell live speech from synthetic. The authors note themselves that quality depends heavily on the dataset; a human reviews the output.
- Checking audio recordings for synthesis
- Fine-tuning for your own language and recording channel
- Comparing several encoders on your own data
- Sizes
- 0,3B – 2B
- Hardware
- from: Laptop
Moderation and safetyOllama2025
OpenAI · USA
Moderation by your own rules: you write the policy in plain text, and the model reasons and gives a decision with an explanation. Built on gpt-oss.
- Moderation by internal company rules
- Labeling disputed messages with an explanation
- Checking reviews and listings before publishing
- Sizes
- 20B – 120B
- Hardware
- from: 1 GPU
Computer vision2023–2025
IDEA Research · China
Finds any objects in an image from a text description, without training on your data: "red box", "person without a hard hat". Rex-Omni is the new VLM-based generation.
- Finding objects by description without labeling
- Automatic data labeling for training
- Checking photos against requirements
- Sizes
- 172M – 3B
- Hardware
- from: Laptop
Voice: speakers and sound2022–2025
NVIDIA · USA
NVIDIA models for "who is speaking": TitaNet recognizes a specific person's voice, Sortformer splits a recording into up to 4 speakers, including live during a call.
- Real-time speaker tagging in conversations
- Checking that the same person is calling (voiceprint)
- Preparing meeting transcripts
- Sizes
- 23M (TitaNet) – 117M (Sortformer)
- Hardware
- from: Laptop
Moderation and safetyRUGGUF2025
Alibaba (Qwen) · China
Safety filters for 119 languages, Russian among them. The Stream version checks a bot's reply while it is being generated and can cut it off on the fly.
- Filtering bot requests in Russian
- Stopping a dangerous reply during generation
- Labeling messages by risk category
- Sizes
- 0.6B – 8B
- Hardware
- from: Laptop
Deepfake detection2023–2025
IBM Research and The Chinese University of Hong Kong · USA
An AI-text detector trained together with a paraphraser: it was deliberately taught not to give up when the text has been rewritten. It errs in both directions; a human reviews the output.
- Checking texts that may have been rewritten after generation
- First-pass filtering in a newsroom or admissions office
- Comparison against simpler detectors
- Sizes
- about 355M (RoBERTa-large)
- Hardware
- from: Laptop
Deepfake detection2023–2025
The Chinese University of Hong Kong, Shenzhen (SCLBD) · China
Dozens of open face-swap detectors for video and photo under one codebase with ready weights. A detector errs in both directions: its output is a reason for a human to check, not proof of a forgery.
- First-pass check of a submitted video or selfie
- Comparing several detectors on your own data
- Fine-tuning a detector for your own flow of applications
- Sizes
- Xception- and EfficientNet-class detectors, tens of millions of parameters
- Hardware
- from: Laptop
Faces2025
ByteDance · China
Transformer-based face recognition from ByteDance, one of the most accurate open models on benchmarks. Weights are published in ONNX format but are non-commercial.
- Comparing faces and searching a photo database
- Research on recognition accuracy
- Access control prototypes
- Sizes
- ViT-T – ViT-L
- Hardware
- from: Laptop
Text analysis2023–2025
University of Texas at Arlington · USA
A classifier from an IEEE S&P study: it separates ordinary requests to a language model from requests that order text and a page meant to deceive people. A human makes the decision on a disputed operation; automatic blocking without review is not acceptable.
- Flagging assistant prompts that look like preparation for deception
- Checking campaign texts before they are sent
- Filling internal filtering rules with examples
- Sizes
- about 125M (based on RoBERTa-base)
- Hardware
- from: Laptop
Plates and vehicles2024–2025
NYU Abu Dhabi (ComNets) · UAE
A model that looks at a photo and answers in words what the plate says: fine-tuned on Malaysian plates, so the Russian format needs fine-tuning. Filming vehicles and people is personal data processing: agree the procedure with a lawyer.
- Reading plates in difficult frames
- Answering questions about a vehicle photo
- Going through an archive of camera photos
- Sizes
- 3B
- Hardware
- from: 1 GPU
Deepfake detection2024–2025
Meta · USA
An image watermark that can be applied to individual regions: the model shows which part of the image is marked. It errs in both directions - a human reviews the result.
- Marking generated and edited images
- Finding a marked fragment inside a collage
- Tracking which parts of a picture were made by AI
- Sizes
- a mark encoder and decoder for images
- Hardware
- from: Laptop
Deepfake detection2024–2025
Xiaohongshu, USTC and Shanghai Jiao Tong University · China
An AI-image detector made of several experts: some look at visual artifacts, others at noise. The hard Chameleon benchmark was released with it. It errs in both directions - a human reviews the result.
- Checking realistic AI images without obvious artifacts
- Comparing detectors on hard examples
- Fine-tuning for your own type of content
- Sizes
- several experts based on ConvNeXt and CLIP
- Hardware
- from: 1 GPU
Plates and vehicles2023–2025
Hezar AI · Iran
Character reading for Iranian license plates; the model is small and runs without a GPU. It was trained on another country plates, so the Russian format needs fine-tuning. Filming vehicles and people is personal data processing: agree the procedure with a lawyer.
- Reading a plate from a cropped image
- Using the architecture as an example for your own plate format
- Fine-tuning reading on your own footage
- Sizes
- a compact CRNN model
- Hardware
- from: Laptop
Cybersecurity2022–2025
NVIDIA · USA
An open NVIDIA bundle: a card-transaction graph plus a classifier, a profile of normal user behaviour, log parsing and root-cause search. A human makes the decision on a disputed operation; automatic blocking without review is not acceptable.
- Highlighting suspicious card operations taking participant links into account
- Tracking deviations of an account from its usual behaviour
- Parsing logs and finding the root cause of an incident
- Sizes
- from a few megabytes (tree-based models) up to BERT-based classifiers
- Hardware
- from: 1 GPU
CybersecurityGGUF2023–2025
Clouditera · China
A Chinese open family for cybersecurity: reviewing vulnerabilities, analysing logs and traffic, explaining commands and scripts.
- Reviewing vulnerabilities and drafting fix recommendations
- Analysing logs and reconstructing an attack chain
- Explaining suspicious commands and scripts
- Sizes
- 1.5B – 14B
- Hardware
- from: Laptop
CybersecurityGGUF2025
Trendyol · Turkey
Security models from a large Turkish marketplace, published in GGUF format: reviewing alerts and incidents, English and Turkish.
- Reviewing alerts and first-pass incident assessment
- Explaining suspicious activity in reports
- Helping the on-duty shift of a monitoring centre
- Sizes
- 32B и 70B
- Hardware
- from: 1 GPU
Acoustic monitoring2024–2025
Shanghai Jiao Tong University and Peng Cheng Laboratory · China
A self-supervised sound understanding model that is markedly cheaper to train on your own data than its predecessors. The system does not make a diagnosis, it gives you a reason to check the unit before it fails.
- Training your own audio model on modest hardware
- Audio features for spotting abnormal operating modes
- Labelling sound events on a site
- Sizes
- 90M (base) and 309M (large)
- Hardware
- from: Laptop
Deepfake detection2022–2025
GRIP, University Federico II of Naples · Italy
Finds traces of editing and shows on a map which regions of an image look altered: suitable for scans of contracts, certificates and photos of documents. It errs in both directions - a person decides.
- Checking scans of certificates and contracts for edits
- Highlighting altered photo regions for an expert
- Filtering out obviously redrawn documents before manual review
- Sizes
- a transformer model producing a map of suspicious regions
- Hardware
- from: 1 GPU
Acoustic monitoring2024–2025
MIT CSAIL and MIT-IBM Watson AI Lab · USA
A sound understanding model built on state spaces instead of a transformer: it handles recordings hours long, which suits continuous listening to a production line. The system does not make a diagnosis, it gives you a reason to check the unit before it fails.
- Processing long continuous recordings
- Finding a rare event across a multi-hour shift
- Labelling sound events on a site
- Sizes
- 30M (small) and 49M (medium)
- Hardware
- from: Laptop
Moderation and safetyOllama2023–2025
Meta · USA
Filter models that check chatbot requests and replies for dangerous topics against a list of categories. Version 4 also checks images. Russian is not officially supported.
- Checking user questions to the bot
- Checking bot replies before sending
- Reporting which rule category was violated
- Sizes
- 1B – 12B
- Hardware
- from: Laptop
Moderation and safety2023–2025
Falconsai and Freepik · USA and Spain
Small models that tell explicit images from regular ones. The Freepik model distinguishes four levels of explicitness. They run on a CPU.
- Filtering user photos and avatars
- Checking generated images before publishing
- Labeling a media library
- Sizes
- 86M
- Hardware
- from: Laptop
Moderation and safety2024–2025
Meta · USA
Tiny classifiers that catch attempts to hack a bot: prompt injections and rule bypassing. The 86M version is multilingual, 22M is English only.
- Protecting a bot from prompt injections
- Checking emails and documents that reach an AI agent
- Fast filter in front of a large model
- Sizes
- 22M – 86M
- Hardware
- from: Laptop
Voice: speakers and sound2024–2025
Songting Liu (Plachtaa) · China
Voice conversion without training: transfers timbre from a 1–30 second sample, can sing and work in real time; V2 also changes accent. Use only with the voice owner's consent.
- Re-voicing a video with a different voice
- Voice anonymization in recordings
- Real-time voice for streams
- Sizes
- about 70M – 200M
- Hardware
- from: Laptop
CybersecurityGGUF2023–2025
Kindo · USA
One of the best-known open families for security and DevSecOps work: reviewing code for weaknesses, test scenarios, explaining attacks.
- Finding weak spots in code and configurations
- Reviewing incidents and explaining attack techniques
- Drafting scripts and procedures for the security team
- Sizes
- 7B – 70B
- Hardware
- from: Laptop
Deepfake detection2025
Desklib · India
A recent open AI-text detector on DeBERTa-v3-large, trained on the RAID dataset, with a separate version for academic work. It errs in both directions - a human always reviews the result.
- Checking submitted articles and reports
- Filtering templated reviews and applications
- First-pass check of student work
- Sizes
- 0.4B (DeBERTa-v3-large)
- Hardware
- from: Laptop
Moderation and safetyOllama2024–2025
Google · USA
Gemma-based filters: they check text for dangerous and offensive content, and ShieldGemma 2 checks images. Focused on English.
- Moderating user messages
- Checking bot replies
- Checking generated images before publishing
- Sizes
- 2B – 27B
- Hardware
- from: Laptop
Plates and vehicles2023–2025
szad670401 (open project) · China
A well-known open system for Chinese plates: single-row blue and yellow plates, electric vehicles, driving-school cars. It has to be fine-tuned for the Russian plate format. Filming vehicles and people is personal data processing: agree the procedure with a lawyer.
- Plate recognition at entry and exit
- Running on modest hardware without a GPU
- Embedding recognition into your own application
- Sizes
- compact detection and reading models designed to run without a GPU
- Hardware
- from: Laptop
Cybersecurity2025
Trend Micro · Japan
Trend Micro cybersecurity models based on Llama 3.1 8B, fine-tuned on a corpus of security texts. A reasoning version is available.
- Answering questions about threats and vulnerabilities
- Analyzing cyberattack reports
- A base for fine-tuning for SOC tasks
- Sizes
- 8B
- Hardware
- from: Laptop
Image + text2023–2025
Alibaba DAMO Academy · China
Models that watch a video and answer questions about it: what happens, when, who does what. VideoLLaMA 3 at 2B and 7B is among the strongest in its size class.
- Video description and short summary
- Finding a moment in a recording by question
- Tagging a video archive
- Sizes
- 2B – 72B
- Hardware
- from: Laptop
Deepfake detection2024
Meta · USA
An imperceptible mark in synthetic speech plus a fast detector that finds it even inside a fragment of a long recording. The detector errs in both directions: a hit is a reason for a human to check, not proof.
- Marking speech synthesized by your service
- Finding your own mark in third-party publications
- Checking whether synthesis was mixed into a call recording
- Sizes
- a watermark generator and detector, 16-bit message
- Hardware
- from: Laptop
Plates and vehiclesRU2022–2024
RIA.com · Ukraine
A plate recognition system with separate reading modules for Russia, Kazakhstan, Belarus, Ukraine, Georgia, Armenia and Kyrgyzstan. Filming vehicles and people and storing the records is personal data processing: agree the procedure with a lawyer.
- Reading a Russian plate from a camera
- Identifying the country and plate type
- Logging vehicles at entry and exit
- Sizes
- a YOLOv8 detector plus separate character-reading modules
- Hardware
- from: Laptop
Cybersecurity2024
cybersectony · not disclosed
A very light classifier for emails and links showing signs of phishing. It errs in both directions, so borderline emails are still reviewed by a person.
- Flagging suspicious incoming emails
- Checking links from correspondence before opening them
- A first-level filter in a mail gateway
- Sizes
- about 66M
- Hardware
- from: Laptop
Finance2024
MIT and MIT-IBM Watson AI Lab · USA
Models that label suspicious chains of transfers by looking at who sent the money at the start of the chain and who received it at the end. A human makes the decision on a disputed operation; automatic blocking without review is not acceptable.
- Classifying transfer chains as ordinary or needing review
- Finding new suspicious chains in a large flow of operations
- Preparing material for the compliance team
- Sizes
- checkpoints from 0.1 to 45 MB
- Hardware
- from: Laptop
Plates and vehicles2023–2024
dima806 (open project) · not disclosed
Two simple photo classifiers: vehicle type and car brand. Trained on public datasets, so check them on your own footage before use. Filming vehicles and people is personal data processing: agree the procedure with a lawyer.
- Identifying the vehicle type from a photo
- Pre-labelling the car brand
- Going through photos in listings and applications
- Sizes
- based on ViT-base (google/vit-base-patch16-224-in21k)
- Hardware
- from: Laptop
Cybersecurity2023–2024
Huawei and University of Science and Technology of China (LogAIBox project) · China
A model pretrained on a large volume of logs from different systems: it turns a log line into a vector on top of which failure and outlier detection is fine-tuned. A human makes the decision on a disputed operation; automatic blocking without review is not acceptable.
- Finding unusual entries in operation logs
- Grouping similar failure messages
- Preparing features for an in-house monitoring model
- Sizes
- about 110M (BERT-base architecture)
- Hardware
- from: Laptop
Moderation and safety2024
iiiorg · not disclosed
A popular detector of 17 types of personal data in six European languages. No Russian and a non-commercial license: suitable for trials and research.
- Finding personal data in texts
- Comparing the quality of PII detectors
- Sizes
- 278M
- Hardware
- from: Laptop
Moderation and safetyGGUFNot maintained2024
Ai2 (Allen Institute for AI) · USA
An open Ai2 filter: in a single pass it determines whether a request is harmful, whether a reply is harmful, and whether the bot refused needlessly. Works in English.
- Checking requests to the bot
- Checking bot replies
- Finding unnecessary bot refusals on harmless questions
- Sizes
- 7B
- Hardware
- from: 1 GPU
Deepfake detectionNot maintained2024
UC Santa Barbara and co-authors · USA
A Longformer-based AI-text detector: it holds a long document whole and was trained on texts from many different language models. It errs in both directions; its output is a reason for a human to check.
- Checking long articles and reports as a whole
- Filtering machine text in a publication flow
- Comparing detectors on your own data
- Sizes
- about 150M (Longformer-base)
- Hardware
- from: Laptop
Deepfake detectionNot maintained2023–2024
Sichuan University and co-authors · China
An open model for finding forgeries in images: it outputs a pixel-level mask of altered regions. It errs in both directions; its map is a hint for an expert, not proof of a forgery.
- Finding pasted and erased fragments in photos
- Checking document scans for edits
- A baseline when comparing manipulation-localization models
- Sizes
- a Vision Transformer based model
- Hardware
- from: 1 GPU
CybersecurityNot maintained2023–2024
SmartData@PoliTO, Politecnico di Torino · Italy
A pair of models from an Italian university: they read a recorded command session and mark which parts belong to reconnaissance, persistence and other attacker steps. A human makes the decision on a disputed operation; automatic blocking without review is not acceptable.
- Labelling command logs by stage of activity
- Grouping similar sessions for incident review
- Preparing features for server monitoring
- Sizes
- about 125M (based on CodeBERT)
- Hardware
- from: Laptop
CybersecurityGGUFNot maintained2023–2024
ZySec AI · India
A small open assistant for security professionals: questions about standards, reviewing threats and vulnerabilities, drafting internal documents.
- Answering questions about security policies and standards
- First-pass review of threat reports
- Drafting internal protection guidelines
- Sizes
- 2.8B и 7B
- Hardware
- from: Laptop
Deepfake detectionNot maintained2023
Hello-SimpleAI · China
One of the first open AI-text classifiers, trained on the HC3 corpus of paired human and ChatGPT answers. It errs in both directions: its output is a reason to talk to the author, not proof.
- First-pass check of student work
- Filtering templated applications and reviews
- Flagging suspicious texts for manual review
- Sizes
- about 125M (RoBERTa-base)
- Hardware
- from: Laptop
Music and soundNot maintained2023
LAION · Germany
CLIP for audio: maps audio and text into a shared space. Lets you search sounds and music by description and classify them without training. Text must be in English.
- Search sounds and music by description
- Automatic tags for an audio library
- Recognizing sound types (siren, breaking glass, voice)
- Sizes
- size not stated on the model card
- Hardware
- from: Laptop
Deepfake detectionNot maintained2022–2023
EURECOM · France
A step beyond AASIST: instead of raw audio it uses the wav2vec 2.0 speech encoder, which helps it hold up on unfamiliar synthesis methods. It errs in both directions - a human reviews the result.
- Spotting synthetic speech in calls
- Checking voice messages and recordings
- Fine-tuning for your own data and codecs
- Sizes
- about 0.3B (wav2vec 2.0 XLS-R encoder)
- Hardware
- from: Laptop
CybersecurityNot maintained2022–2023
Ehsan Aghaei and co-authors · USA
A compact language encoder trained on cybersecurity texts: tagging threat reports, finding entities and classification.
- Tagging threat reports and vulnerability bulletins
- Extracting entities from security texts
- Classifying and searching an internal incident base
- Sizes
- около 125M
- Hardware
- from: Laptop
Text analysisRUNot maintained2022–2023
David Dale (cointegrated) and the community · Russia
Ready-made tiny rubert-tiny models for Russian text: detect rudeness and insults, sentiment and emotions. They run on a CPU in milliseconds.
- Filtering insults in Russian chats and comments
- Labeling reviews as positive, neutral or negative
- Spotting irritated customers in requests
- Sizes
- 12M – 29M
- Hardware
- from: Laptop
Acoustic monitoringNot maintained2022
Microsoft · USA
A self-supervised sound understanding model that became the base for many current systems: teams take it as a starting point and fine-tune it on their own equipment sounds. The system does not make a diagnosis, it gives you a reason to check the unit before it fails.
- A base for your own sound understanding model
- Audio features for spotting abnormal operating modes
- Labelling sound events on a site
- Sizes
- a ViT-base class model, around 90M parameters
- Hardware
- from: Laptop
Music and soundNot maintained2022
MIT · USA
A classic 2021 sound recognition model: detects 527 AudioSet event classes (siren, barking, breaking glass, music). Lightweight, runs without a GPU, in Transformers since 2022.
- Sound event recognition
- Tagging an audio archive
- Detecting alarm sounds
- Sizes
- about 87M
- Hardware
- from: Laptop
Computer visionNot maintained2021–2022
OpenAI · USA
The 2021 model that first linked images and text: search photos by words and classify them without training. English only; SigLIP 2 or PE are usually chosen today.
- Image search by text query
- Automatic tags for a catalog
- Finding similar images
- Sizes
- about 0.15B – 0.6B
- Hardware
- from: Laptop
Deepfake detectionNot maintained2021
NAVER Clova AI Research and EURECOM · South Korea
The baseline open model against voice spoofing: it listens to the raw recording and tells a live person from synthesis or a replay. It errs in both directions - its output is a reason for a human to check, not proof.
- Voice check during phone authentication
- Filtering replays and synthesis in a voice menu
- A baseline when comparing voice detectors
- Sizes
- weight files of 0.4 and 1.3 MB
- Hardware
- from: Laptop
Deepfake detectionNot maintained2020
MiniVision Technology · China
Practically the only fully open weight set for single-frame face liveness: it tells a live person from a photo, a screen or a mask. It errs in both directions - a person must be able to appeal a rejection.
- Liveness check when signing in by selfie
- Protecting an access system from a photo on a phone
- A check during remote customer identification
- Sizes
- two models of about 1.8 MB each
- Hardware
- from: Laptop
