TextOllama2023–2026
DeepSeek · China
DeepSeek's flagship line: from the first 7B/67B to V4-Pro with 1.6 trillion parameters. Closed-model quality under an open MIT license; V4-Flash-Vision-Exp and V4.1-Flash understand images, context up to 1M tokens.
- Employee assistant on your own server
- Analysis of long contracts and reports
- Agents that work with tools and APIs
- Sizes
- 7B – 1.6T-A49B
- Hardware
- from: Laptop
TextGGUF2025–2026
Xiaomi · China
Xiaomi models for reasoning and agents: from the compact MiMo-7B to MiMo-V2.6-Pro with 1.02 trillion parameters. The larger versions understand text, images, video and audio, with a 1M token context. Languages: English and Chinese.
- Logic and calculation tasks
- Agents with tools
- Help for developers
- Sizes
- 7B – 1,02T-A42B
- Hardware
- from: Laptop
TextRU2024–2026
Sber · Russia
Sber open models with strong Russian language support and local context, from 10B-A1.8B to 702B, all MIT. GigaChat3.1-Audio handles recordings up to two hours; GFusion is a fast diffusion text version.
- Russian-language employee assistant on your own server
- Customer replies and request handling in Russian
- Working with contracts and internal policies
- Sizes
- 10B-A1.8B – 702B-A36B
- Hardware
- from: Laptop
Tabular data2022–2026
Prior Labs (University of Freiburg) · Germany
A ready-made model for tables: it takes example rows and immediately predicts for new ones, without lengthy training or tuning. Only v2 is free for business; newer versions are non-commercial.
- Predicting customer churn from a CRM export
- Scoring applications and leads
- Classifying customers from 1C data
- Sizes
- from a few to hundreds of millions of parameters
- Hardware
- from: Laptop
Tabular data2025–2026
Amazon (AutoGluon team) · USA
Amazon's tabular model built into AutoGluon: classification and regression from examples with brief fine-tuning. Mitra-v2 handles more rows and columns.
- Predicting churn and repeat purchases
- Scoring applications
- Predicting deal or order value
- Sizes
- about 76M
- Hardware
- from: Laptop
Tabular data2025–2026
Layer 6 AI (TD Bank) · Canada
A tabular model from a Canadian bank's AI lab, trained on real tables rather than only synthetic ones. Version 1.2 Turbo made computation orders of magnitude faster.
- Scoring applications and customers
- Predicting churn
- Classifying transactions and customers
- Sizes
- about 60–80M
- Hardware
- from: Laptop
Tabular data2025–2026
Stable AI (Beijing, with Tsinghua University) · China
A table model that alone can classify, predict numbers and fill in missing data. The lightweight LimiX-2M runs on an ordinary computer.
- Filling gaps in 1C and CRM exports
- Churn prediction and scoring
- Classifying customers and products
- Sizes
- 2M – 16M and LimiX-2
- Hardware
- from: Laptop
Visual document searchGGUF2026
Tencent · China
Tencent models based on Qwen3.5 for searching scans and PDFs as images. According to the model card, among the top of the ViDoRe leaderboard at release.
- Search across scans and PDFs without OCR
- RAG over reports with tables and charts
- Search across document archives
- Sizes
- 4.5B – 8B
- Hardware
- from: 1 GPU
Documents and OCR2026
Jina AI · Germany
Document parsing in a single model: a whole page becomes Markdown - text in correct reading order, tables and formulas in LaTeX. Built on DeepSeek-OCR, with only 0.6B of its 3.4B parameters active.
- Converting scans and PDFs to Markdown
- Recognizing tables and formulas
- Parsing invoices, acts and reports
- Sizes
- 3.4B-A0.6B
- Hardware
- from: 1 GPU
Forecasting2024–2026
Google · USA
A ready-made Google forecasting model: forecasts any time series without training on your data.
- Sales and demand forecasting
- Purchase and inventory planning
- Load and traffic forecasting
- Sizes
- 200M – 500M
- Hardware
- from: Laptop
Speech to textGGUF2025–2026
IBM · USA
IBM speech models for recognizing and translating speech in English, several European languages and Japanese. Designed for enterprise use.
- Transcribing business meetings
- Translating speech into text in another language
- Voice assistants
- Sizes
- 470M – 8B
- Hardware
- from: Laptop
Text2025–2026
Ant Group (inclusionAI) · China
An Ant Group family: Ling for standard models, Ring for reasoning ones. There are trillion-parameter flagships and the efficient Ling-3.0-tiny, which needs only 1.3 billion active parameters.
- Corporate assistant
- Agents for office processes
- Financial analytics (Fin version available)
- Sizes
- 7.9B-A1.3B – 1T
- Hardware
- from: Laptop
TextRUOllama2024–2026
Cohere · Canada
Business models: document search with source citations, tool calling, many languages. Command A+ (2026) was the first under Apache 2.0, followed by the North line: code, translation and compact vision.
- Knowledge-base answers with source citations
- Agents that work with internal systems
- Translation and correspondence in different languages
- Sizes
- 2.5B – 218B-A25B
- Hardware
- from: Laptop
TextOllama2024–2026
IBM · USA
IBM enterprise models with transparent training data and ISO 42001 certification. Granite 4 is a memory-efficient Mamba and Transformer hybrid.
- Answers based on internal documents (RAG)
- Tool calling and agent work
- Data extraction and classification
- Sizes
- 350M – 34B
- Hardware
- from: Laptop
Computer-use agentsGGUF2025–2026
Ant Group (inclusionAI) · China
An Ant Group family for finding elements on screen and completing tasks in phone and computer interfaces. UI-Venus-2 was specifically trained to refuse dangerous actions.
- Automating actions in mobile apps
- Filling in forms in web interfaces
- UI autotests
- Sizes
- 2B – 72B
- Hardware
- from: Laptop
Weather and climate2024–2026
Allen Institute for AI (Ai2) · USA
A fast climate model emulator: simulates the atmosphere years and decades ahead on a single GPU. Coupled with an ocean model (SamudrACE) for long-term scenarios.
- Decades-long climate scenarios to assess long-term asset risks
- Large-scale what-if runs on temperature and precipitation
- Preparing data for crop yield and energy demand models
- Sizes
- checkpoint of about 1.8 GB
- Hardware
- from: 1 GPU
Weather and climate2023–2026
Google DeepMind · UK
Google DeepMind's family of global weather models: GraphCast (10-day forecast), GenCast (probabilistic ensemble) and WeatherNext 2 with cyclone forecasting. Since August 2026 the weights are cleared for commercial use.
- Medium-range weather forecasts for planning shifts, voyages and deliveries
- Probabilistic assessment of extreme weather for insurance portfolios
- Tropical cyclone track forecasts for marine and port operations
- Sizes
- from lightweight 1° versions to full 0.25°
- Hardware
- from: 1 GPU
Speech to textRUGGUF2024–2026
Sber · Russia
Sber's models for Russian speech recognition, among the most accurate for Russian. Includes emotion recognition, v3 with punctuation, and a multilingual version (Russian, Kazakh, Kyrgyz, Uzbek).
- Transcribing calls in Russian
- Meeting minutes
- Voice control of services
- Sizes
- 220M – 600M
- Hardware
- from: Laptop
TextGGUF2025–2026
Moonshot AI · China
Very large Moonshot MoE models for agentic work. K3 (2.8 trillion parameters) was the largest open model at release, with up to 1M tokens of context and image understanding; K2.7-Code is built for programming.
- Multi-step agents: search, data collection, reports
- In-depth document analysis
- Help for developers
- Sizes
- 16B-A3B – 2.8T-A104B
- Hardware
- from: 1 GPU
TextRU2024–2026
T-Bank · Russia
T-Bank models fine-tuned from Qwen for Russian: they write and reason in Russian noticeably better than the original. T-Lite is 8B, T-Pro 32B on one GPU; T-Search is a multi-step search agent in Russian and English.
- Russian-language support chatbot
- Analysis of requests and documents in Russian
- Answers based on the company knowledge base
- Sizes
- 7B – 36B-A3B
- Hardware
- from: Laptop
TextGGUF2026
Thinking Machines Lab · USA
Flagship open models from Mira Murati's lab: they take text, images and audio. Large MoE models that need several GPUs.
- Flagship-level corporate assistant
- Analysis of documents, images and audio
- Programming help
- Sizes
- 276B-A12B, 975B-A41B
- Hardware
- from: Cluster
Documents and OCRRUGGUF2025–2026
Tencent · China
A lightweight OCR model from Tencent: document parsing, finding text in photos, field extraction and translating text from images. Version 1.5 is faster and runs on an ordinary PC.
- Extracting fields from invoices and delivery notes
- Recognising tables and formulas
- Translating text in photos and scans
- Sizes
- 1B
- 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
Tabular data2026
LG AI Research · South Korea
LG's small tabular model: with 21M parameters it nearly matches the leaders in classification and regression accuracy. Weights are for non-commercial use only.
- Pilot churn forecasts
- Testing scoring hypotheses
- Exploring customer data
- Sizes
- about 21M
- Hardware
- from: Laptop
Documents and OCRRU2024–2026
Datalab · USA
A compact OCR toolkit from the makers of Marker and Chandra: text recognition, page layout, reading order and tables. Surya OCR 2 (650M) also runs on a CPU; Russian scored 88.8% in benchmarks.
- Recognizing scans and PDFs, including in Russian
- Page layout: headings, tables, images, reading order
- Recognizing tables by rows and columns
- Sizes
- up to 650M
- Hardware
- from: Laptop
Weather and climate2024–2026
Microsoft Research · USA
A foundation model of Earth's atmosphere: global weather forecasts, plus separate versions for air quality and ocean waves. Computes a forecast in seconds instead of hours on a supercomputer.
- Your own forecast of temperature, wind and precipitation for company locations
- Sea state estimates for planning voyages and port operations
- Air pollution forecasts for industrial sites
- Sizes
- about 1.3B (a small test version is available)
- Hardware
- from: 1 GPU
Weather and climate2022–2026
NVIDIA · USA
NVIDIA's set of weather and climate models: global FourCastNet forecasts, downscaling to kilometers (CorrDiff), regional storm forecasts (StormCast), climate generation (cBottle, Atlas). Run via Earth2Studio.
- Global forecasts followed by downscaling to the region you need
- Short-term forecasts of thunderstorms and heavy rain for dispatch services
- Generating many weather scenarios for stress tests
- Sizes
- 98M – 2.5B (FourCastNet 3 — about 711M)
- Hardware
- from: 1 GPU
Math and reasoningOllama2025–2026
Open Thoughts (Stanford, Berkeley and other universities) · USA
Fully open reasoning models: both weights and training data are published. Newer OpenThinkerAgent versions can carry out multi-step tasks.
- Calculations and formula checks
- Complex analytics with step-by-step breakdowns
- Checking the logic of internal policies
- Sizes
- 1.5B – 32B
- Hardware
- from: Laptop
Forecasting2025–2026
NXAI · Austria
A compact forecasting model on the xLSTM architecture, a leader in open benchmarks despite its small size. Runs fast on a regular CPU.
- Demand and sales forecasting
- Energy consumption forecasting
- Forecasts on modest hardware and on site
- Sizes
- about 35M to 82M
- Hardware
- from: Laptop
TextRUGGUF2025–2026
MiniMax · China
Large MoE models with very long context (up to 1M tokens for Text-01 and M3). M3 is multimodal and understands images. Licenses differ greatly from version to version.
- Analysis of large document archives in a single request
- Agents with tools
- Help for developers
- Sizes
- 230B-A10B – 456B-A46B
- Hardware
- from: Cluster
Tabular data2026
Google Research · USA
Google's large tabular model: classification and regression from examples without training, with numeric and categorical columns. Weights are for non-commercial use only.
- Pilot comparison with current scoring models
- Exploring customer data
- Testing churn hypotheses
- Sizes
- about 1.6B
- Hardware
- from: 1 GPU
Documents and OCR2026
Baidu · China
Baidu's OCR model building on DeepSeek-OCR ideas: processes multi-page documents and PDFs in a single pass and outputs structured text. Claimed to be multilingual, but the language list is not published.
- Converting multi-page PDFs and scans to text and Markdown
- Recognizing contracts, invoices and reports
- Preparing document archives for search and RAG
- Sizes
- 3.3B
- Hardware
- from: Laptop
Documents and OCRRU2022–2026
Baidu (PaddlePaddle) · China
Classic lightweight PaddleOCR models: detecting and recognizing lines of text plus page layout. They run on CPUs and phones; there is a separate model for East Slavic languages, including Russian.
- Recognizing text on scans, photos and screens
- Reading labels, displays and markings in production and warehouses
- Page layout: tables, formulas, stamps, headings
- Sizes
- from 1.5M to tens of millions of parameters
- Hardware
- from: Laptop
Satellite and geo2025–2026
Allen Institute for AI (Ai2) · USA
Ai2's family of models for Sentinel-1, Sentinel-2 and Landsat imagery, with ready-made fine-tunes for mangroves, deforestation and ecosystem types. The license excludes the extractive industries.
- Monitoring deforestation and forest condition across the supply chain
- Classifying land and crops from image series
- Image embeddings for finding similar plots
- Sizes
- Nano – Large (Base about 114M)
- Hardware
- from: Laptop
Documents and OCRRUGGUF2025–2026
Baidu (PaddlePaddle) · China
A compact document parsing model from the popular PaddleOCR toolkit. Per the model card it supports 109 languages, including Russian; version 1.6 leads the OmniDocBench benchmark.
- Recognising invoices, contracts and delivery notes, including in Russian
- Recognising tables, formulas and stamps
- Converting scans to Markdown and JSON
- Sizes
- 0.9B
- Hardware
- from: Laptop
Forecasting2024–2026
THUML, Tsinghua University · China
A compact forecasting foundation model from the Tsinghua lab: trained on a large set of diverse series and fine-tunable on your own data.
- Forecasting demand and load
- Forecasting sensor readings on the shop floor
- Fine-tuning forecasts on your own history
- Sizes
- 84M (timer-base)
- Hardware
- from: Laptop
ForecastingGGUF2025–2026
Datadog · USA
A Datadog forecasting model trained on server and application metrics. Especially strong for IT monitoring: load, latency, errors.
- Server load forecasting
- Anomaly detection in metrics
- Capacity planning
- Sizes
- 4M – 2.5B
- Hardware
- from: Laptop
TextRUGGUF2025–2026
Arcee AI · USA
An American family of MoE models trained from scratch: Nano, Mini and Large. Trinity-Large-Thinking (398B) reasons before answering.
- Agents with tool calling
- Reasoning tasks
- Corporate assistant on your own servers
- Sizes
- 6B – 398B-A13B
- 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
Image + textOllama2025–2026
IBM · USA
Compact IBM models for business documents: tables, charts, forms, field-value pairs. The model card openly warns that it works best with English.
- Extracting fields from forms and invoices
- Turning charts and tables into data
- Answering questions about documents
- Sizes
- 2B – 4B
- Hardware
- from: Laptop
Text analysisOllama2024–2026
NuMind · France
Models for template-based data extraction: give it a document or scan and a JSON field template, get a filled-in JSON back. NuExtract3 (4B) also converts scans to Markdown.
- Extracting company details, amounts and dates from invoices and contracts into JSON
- Parsing receipts, waybills and forms against a set template
- Converting scans to Markdown for search
- Sizes
- 0.5B – 8B
- Hardware
- from: Laptop
Search and RAGGGUF2025–2026
Octen · USA / Singapore
Qwen3-Embedding models fine-tuned by the startup Octen for search in legal, financial and medical texts. As of January 2026 the 8B version topped the RTEB leaderboard.
- Search across contracts and case law
- Search across financial reports
- Search across long documents up to 32K tokens
- Sizes
- 0.6B – 8B
- Hardware
- from: Laptop
TextRU2024–2026
Ivan Bondarenko (bond005), Novosibirsk State University · Russia
Russian-language models for working with documents rather than chatting: knowledge-base answers, extraction of entities and facts from Russian text, long context.
- Answers to questions based on internal documents
- Extracting names, dates and amounts from contracts
- Short summaries of long Russian texts
- Sizes
- 1.5B – 7.6B
- Hardware
- from: Laptop
Documents and OCRRUGGUF2025–2026
rednote hilab (Xiaohongshu) · China
A multilingual document parsing model: text, tables, formulas and reading order in one pass. dots.mocr also turns charts and diagrams into vector SVG.
- Recognising invoices, contracts and delivery notes
- Converting tables into an editable format
- Converting charts and diagrams into vector format
- Sizes
- about 3B
- Hardware
- from: Laptop
Documents and OCRRUGGUF2025–2026
Datalab · USA
A strong OCR model from the authors of Marker and Surya: handwriting, forms, tables. Per the model card it supports 90+ languages, with Russian among the examples.
- Recognising invoices, contracts and delivery notes, including in Russian
- Recognising handwritten forms and questionnaires
- Recognising complex tables
- Sizes
- 5B – 9B
- Hardware
- from: Laptop
Documents and OCRRUGGUF2026
Baidu (Qianfan) · China
A Baidu model that not only recognises a document but also answers questions about it. Per the model card it supports 192 languages, including Cyrillic.
- Recognising invoices, contracts and delivery notes, including in Russian
- Page layout analysis and table recognition
- Answering questions about a document
- Sizes
- 4B
- Hardware
- from: Laptop
Tabular data2025–2026
Lexsi Labs · India
Recent open models for tabular data: they predict from a few examples given in the prompt, with no task-specific training.
- Classification and forecasting on tables with no separate training
- Quickly testing models on new datasets
- Assessing features in large tables
- Sizes
- size not stated on the model card
- Hardware
- from: Laptop
Tabular data2025–2026
Inria (Soda team) · France
An open tabular model from the creators of scikit-learn: classifies and predicts from examples without training and handles tables of up to hundreds of thousands of rows. The license allows business use.
- Predicting customer churn
- Scoring applications and deals
- Classifying customers from 1C and CRM data
- Sizes
- about 25–30M
- Hardware
- from: Laptop
Avatars2024–2026
Ant Group · China
Ant Group's talking avatars: the face and, from V2, hand gestures. V3-Flash produces video in 8 steps and fits into 12 GB of GPU memory.
- Presenter video from a photo and voice
- Avatar with gestures for presentations
- Voiced characters
- Sizes
- up to 1.3B
- Hardware
- from: 1 GPU
TextRUOllama2023–2026
Microsoft · USA
Small Microsoft models trained on carefully selected data: strong at logic and math for their modest size. Versions with images and speech are available.
- Assistant on a laptop or your own server
- Reasoning and calculation tasks
- Analysis of images and diagrams (vision versions)
- Sizes
- 1.3B – 42B-A6.6B
- Hardware
- from: Laptop
TextGGUF2024–2026
AI21 Labs · Israel
A hybrid of Transformer and Mamba with a window of up to 256K tokens: handles long documents faster than conventional models. Jamba2 focuses on accurate, source-based answers.
- Answers based on long policies and contracts
- Knowledge-base search (RAG)
- Summaries of large documents
- Sizes
- 3B – 398B-A94B
- Hardware
- from: Laptop
Documents and OCROllama2025–2026
DeepSeek · China
An OCR model that compresses a page into a small number of visual tokens, so it processes large volumes quickly. Version 2 better understands reading order.
- Bulk recognition of scanned invoices and contracts
- Table recognition
- Converting PDFs to Markdown for search and RAG
- Sizes
- about 3B
- Hardware
- from: Laptop
Documents and OCRRUOllama2026
Zhipu AI (Z.ai) · China
A lightweight OCR model from Zhipu for document parsing. The model card lists Russian among supported languages; built for high load and low-end hardware.
- Recognising invoices, contracts and delivery notes, including in Russian
- Recognising tables and formulas
- Extracting fields to JSON
- Sizes
- 0.9B
- Hardware
- from: Laptop
Weather and climate2024–2026
European Centre for Medium-Range Weather Forecasts (ECMWF) · Europe (intergovernmental organization)
ECMWF's weather neural network running operationally: a 15-day forecast four times a day, an ensemble version with 51 scenarios, and since version 2, ocean waves.
- Running your own forecast from open initial data
- Ensemble forecasts to estimate the probability of frost, downpours and storms
- Wave forecasts for marine operations
- Sizes
- checkpoint of about 1 GB
- Hardware
- from: 1 GPU
Tabular dataGGUF2024–2025
Zhejiang University · China
A family for working with tables and databases: it understands data structure, writes parsing code and answers questions about exports.
- Answering questions about tables and data exports
- Automated data analysis with generated code
- A helper for BI and internal reporting
- Sizes
- 7B – 72B
- Hardware
- from: 1 GPU
Math and reasoningGGUF2024–2025
DeepSeek · China
DeepSeek's maths models. The first 7B version introduced the GRPO training method; the 685B V2 writes and checks its own olympiad-level proofs.
- Calculations and formula checks
- Checking mathematical workings in reports
- Working through problems step by step
- Sizes
- 7B – 685B
- Hardware
- from: Laptop
Satellite and geo2025
IBM and the European Space Agency (ESA) · USA / Europe
A multimodal Earth model: understands optical and radar imagery, terrain, vegetation index and land use maps, and can generate a missing data type (for example, a "see-through-clouds" image from radar).
- Analyzing fields and forests even in cloudy weather using radar imagery
- Land use maps for assessing plots
- Flood and wildfire assessment (ready-made fine-tunes available)
- Sizes
- tiny – large (checkpoints from ~200 MB to ~3.8 GB)
- Hardware
- from: Laptop
Rerankers2025
ZeroEntropy · USA
Rerankers built on Qwen3. The model card lists the target domains — finance, law, code, medicine, science; the stated language is English.
- Refining results before an AI assistant answers
- Sorting search results across contracts and reports
- Search across technical and scientific documentation
- Sizes
- zerank-2 — 4B (based on Qwen3-4B), plus a smaller "small" version
- Hardware
- from: Laptop
ForecastingGGUF2024–2025
Amazon · USA
Amazon forecasting models, among the most downloaded. Chronos-2 takes external factors into account: prices, promotions, weather.
- Demand forecasting with promotions and prices
- Inventory planning
- Forecasting revenue and customer flow
- Sizes
- 8M – 710M
- Hardware
- from: Laptop
Documents and OCRRUGGUF2025
Nanonets · USA / India
A model that converts documents to Markdown with tables, stamps, signatures, checkboxes and watermarks. The OCR2 model card lists Russian among its languages.
- Recognising invoices, contracts and delivery notes, including in Russian
- Recognising stamps, signatures and marks
- Handwriting recognition
- Sizes
- 1.5B – 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
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
Satellite and geo2023–2025
IBM and NASA · USA
Foundation models for Landsat and Sentinel-2 satellite imagery that account for image time series. Ready-made fine-tunes for floods, burn scars and crop types, plus a separate weather model, WxC.
- Mapping crops and field condition over the season
- Assessing flood zones and burn scars after natural disasters
- Monitoring changes in buildings and land use
- Sizes
- tiny – 600M (imagery), 2.3B (Prithvi WxC weather)
- Hardware
- from: Laptop
Text to SQLGGUF2024–2025
Prem AI · UK
A text-to-SQL model of just 1B parameters, designed to run locally so the database never leaves for external services.
- Local translation of questions into SQL with no internet access
- Query hints on modest hardware
- Embedding into internal analytics tools
- Sizes
- 1B
- Hardware
- from: Laptop
ForecastingGGUF2024–2025
Salesforce · USA
Salesforce's universal forecasting model for series with different frequencies and many variables. Weights are open for research only.
- Research forecasting pilots
- Comparison with other forecasting models
- Forecasts across many related series
- Sizes
- 11M – 311M
- Hardware
- from: Laptop
TextGGUF2025
ByteDance · China
An open ByteDance 36B model with up to 512K tokens of context and an adjustable thinking budget. Fits on a single powerful GPU.
- Analysis of long documents
- Agents with tools
- Corporate assistant
- Sizes
- 36B
- Hardware
- from: 1 GPU
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
Speech to textRU2025
T-Bank · Russia
A compact T-Bank streaming model for recognizing Russian speech in phone calls. Works in real time even without a GPU.
- Transcribing phone calls
- Voice robots on the line
- Call quality control
- Sizes
- 72M
- Hardware
- from: Laptop
Finance2025
Tsinghua University (NeoQuasar) · China
A foundation model for market candlestick data: trained on data from more than 45 exchanges, it forecasts prices and volumes. The largest version, large, is not open.
- Forecasting candlesticks and trading volumes
- Volatility estimation
- A base for fine-tuning on your own series
- Sizes
- 4.1M – 102M
- Hardware
- from: Laptop
Visual document search2024–2025
Illuin Technology (ViDoRe team) · France
Searches PDFs and scans as images: pages do not need to be OCR'd first, the model finds the right one for a question directly, including tables and charts. Trained on English.
- Search across scans, presentations and PDFs
- RAG over documents with tables and charts
- Search across technical documentation
- Sizes
- 256M – 3B
- Hardware
- from: Laptop
Math and reasoning2025
NVIDIA · USA
NVIDIA models for maths and reasoning based on Qwen. AceReason was fine-tuned with reinforcement learning first on maths, then on code.
- Calculations and formula checks
- Complex analytics with step-by-step breakdowns
- Working through programming problems
- Sizes
- 1.5B – 72B
- Hardware
- from: Laptop
Text to SQLGGUF2025
IDEA Research · China
A text-to-SQL model trained with reinforcement learning: it works through the schema and the conditions step by step before producing a query.
- Database queries for questions with several conditions
- Reviewing and fixing other people SQL queries
- An analyst helper inside a BI system
- Sizes
- 3B – 14B
- Hardware
- from: Laptop
TextOllama2025
DeepSeek · China
A reasoning model that thinks step by step before answering. Strong at calculations, logic and code; compact distilled versions are available.
- Complex calculations and logic checks
- Analysis of contracts and internal policies
- Help for developers
- Sizes
- 1,5B – 671B
- Hardware
- from: Laptop
Text to SQL2025
Snowflake · USA
A Snowflake model for turning questions into SQL, trained with reinforcement learning by checking query results. The open 7B version is based on Qwen2.5-Coder.
- Plain-language questions to a data warehouse
- Generating SQL for reports and dashboards
- Checking and fixing analysts' queries
- Sizes
- 7B
- Hardware
- from: Laptop
Finance2025
The Fin AI · international project
Models that spell out their reasoning before answering financial questions with numbers and tables. Not investment advice: decisions are made by a specialist.
- Calculation questions on statements with the working shown
- Working through tasks with tables and numbers from documents
- Checking calculations made by hand
- Sizes
- 8B и 14B
- Hardware
- from: 1 GPU
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
Forecasting2025
THUML, Tsinghua University · China
A forecasting model that returns a set of possible scenarios rather than a single line — useful when you need a range for demand or load, not one number.
- Forecasting demand with a range of values
- Planning stock while accounting for spread
- Forecasting load on services and staff
- Sizes
- 128M (sundial-base)
- Hardware
- from: Laptop
Text to SQL2025
Alibaba · China
Alibaba models for turning questions into SQL, based on Qwen2.5-Coder. They work with different SQL dialects; a small 3B version suits modest hardware.
- Plain-language database questions
- Queries for different databases (PostgreSQL, MySQL, SQLite)
- Automating routine reports
- Sizes
- 3B – 32B
- Hardware
- from: Laptop
Math and reasoningOllama2024–2025
Qwen (Alibaba) · China
Qwen's first open reasoning model: it thinks step by step before answering and comes close to DeepSeek-R1 on maths tasks with only 32B parameters.
- Calculations and formula checks
- Complex analytics with step-by-step breakdowns
- Checking the logic of contracts and internal policies
- Sizes
- 32B
- Hardware
- from: 1 GPU
Math and reasoningGGUF2025
Stanford University · USA
A reasoning model trained on just a thousand problems. It can be told to think longer to answer a hard question more accurately.
- Calculations and formula checks
- Working through complex problems step by step
- Training
- Sizes
- 1.5B – 32B
- Hardware
- from: Laptop
Math and reasoningGGUF2025
Qihoo 360 · China
Reasoning models from Qihoo 360: a standard Qwen2.5 was fine-tuned for long reasoning using an open recipe; data and code are published.
- Calculations and formula checks
- Working through problems step by step
- A base for your own reasoning fine-tuning
- Sizes
- 7B – 32B
- Hardware
- from: Laptop
Text to SQL2025
Renmin University of China (RUC) · China
Models for turning questions into SQL, trained on millions of synthetic query examples across different databases. Three sizes for different hardware.
- Database questions without knowing SQL
- Generating queries for reports
- A base for fine-tuning on your own database schema
- Sizes
- 7B – 32B
- Hardware
- from: Laptop
Finance2025
Shanghai University of Finance and Economics (SUFE) · China
A reasoning model for financial tasks based on Qwen2.5-7B: calculations, report analysis, regulatory questions. Trained on Chinese and English data.
- Financial calculations with step-by-step explanations
- Answering questions about financial statements
- Analyzing tables of financial data
- Sizes
- 7B
- Hardware
- from: Laptop
Visual document search2025
Nomic AI · USA
Search across PDF pages and scans as images. The cards list English, Italian, French, German and Spanish — Russian is not among them.
- Search across an archive of scans and PDFs
- Search across tables and diagrams inside documents
- Picking pages for an AI assistant answer
- Sizes
- 3B and 7B
- Hardware
- from: 1 GPU
Math and reasoningOllama2024–2025
Qwen (Alibaba) · China
Maths versions of Qwen: they solve problems step by step and can calculate via code. Includes reward models that check each step of a solution.
- Calculations and formula checks
- Checking calculations in estimates and reports
- Working through problems step by step
- Sizes
- 1.5B – 72B
- Hardware
- from: Laptop
Visual document search2025
LlamaIndex · USA
A small model for searching document pages as images, from the team behind a popular RAG framework. The card lists English, Italian, French, German and Spanish.
- Search across scans and PDFs without OCR
- Search across invoices, acts and contracts
- Picking pages for an AI assistant answer
- Sizes
- 2B (based on Qwen2-VL)
- Hardware
- from: 1 GPU
Search and RAGRUOllama2024
Snowflake · USA
Snowflake embeddings built specifically for search. Version 2.0 is multilingual (Russian is on the language list), handles long texts up to 8K tokens and can compress vectors.
- Search across documents and knowledge bases
- Picking passages for RAG
- Search across reports and internal data
- Sizes
- 22M – 568M
- Hardware
- from: Laptop
Finance2023–2024
Du Xiaoman (Duxiaoman-DI) · China
A large Chinese model family for the financial industry: advice, document reading and long texts up to 8k-16k. Not investment advice: decisions are made by a specialist.
- Answering customer questions about banking products
- Working through long financial documents
- An internal assistant for a finance company regulations
- Sizes
- 6B – 176B
- Hardware
- from: 1 GPU
Finance2023–2024
The Fin AI / ChanceFocus · international project
One of the first open model families for financial text: reading statements, news and questions about numbers. Not investment advice: decisions are made by a specialist.
- Reading financial statements and press releases
- Answering questions about numeric data in documents
- Classifying financial texts
- Sizes
- 0.5B – 30B
- Hardware
- from: Laptop
Finance2023–2024
AI4Finance Foundation · USA
An open set of lightweight add-ons for ordinary language models that work with financial texts and news. Not investment advice: decisions are made by a specialist.
- Assessing the tone of financial news and reports
- Tagging mentions of companies and instruments in text
- Preparing digests from a stream of business news
- Sizes
- adapters for 6B - 20B base models
- Hardware
- from: 1 GPU
Visual document search2024
LightOn · France
A reranker for document pages as images: after a visual search it reorders the found pages by how well they answer the question. The card does not state the languages.
- Refining search results over scans and PDFs
- Selecting pages before an AI assistant answers
- Sorting retrieved slides and reports
- Sizes
- 2B (based on Qwen2-VL)
- Hardware
- from: 1 GPU
Forecasting2024
Auton Lab, Carnegie Mellon University · USA
A foundation model for numeric series: one engine is used for forecasting, anomaly detection, filling gaps and classification.
- Forecasting demand and load
- Detecting anomalies in sensor readings and metrics
- Filling gaps in historical data
- Sizes
- about 40M – 385M
- Hardware
- from: Laptop
Forecasting2024
IBM Research · USA
Tiny forecasting models from IBM: they run on an ordinary CPU and sit next to the business system without a separate GPU server.
- Forecasting sales and warehouse stock
- Forecasting energy use and equipment load
- Fast forecasts right on the company server
- Sizes
- very small: TinyTimeMixers have about 1M parameters
- Hardware
- from: Laptop
Tabular dataGGUF2024
RUCKBReasoning, Renmin University of China · China
A model for office work with tables: for a given question it returns either a direct answer or code to process the data in a table or document.
- Processing tables from Excel and documents from a text instruction
- Generating code for recalculations and selections
- Answering questions about data in reports
- Sizes
- 7B и 13B
- Hardware
- from: Laptop
Visual document search2024
University of Waterloo, Tevatron project · Canada
Searches page screenshots: the page is not OCRed but turned into a single vector, so the index is more compact than with late-interaction models. The card lists English and French.
- Search across scans and PDFs without OCR
- Search across presentations and reports with complex layouts
- Picking pages for an AI assistant answer
- Sizes
- 2B (based on Qwen2-VL)
- Hardware
- from: 1 GPU
Forecasting2024
The Time-MoE team · not disclosed
A forecasting model with a sparse architecture: only part of the network runs at each step, so it stays fast at a small size.
- Forecasting sales and stock levels
- Forecasting load on services and staff
- Planning purchases from history
- Sizes
- 50M and 200M
- Hardware
- from: Laptop
FinanceNot maintained2024
Writer · USA
A large model for financial documents with a context window of about 131k tokens: it holds long reports whole. Not investment advice: decisions are made by a specialist.
- Working with long annual reports and prospectuses
- Summaries and digests of financial documents
- Finding answers inside a large document pack
- Sizes
- 70B (the model card states 72 billion parameters)
- Hardware
- from: Cluster
Tabular dataNot maintained2024
ML Foundations · USA
A foundation model for predictions on tables: it classifies and forecasts from a handful of examples, with no separate task-specific training.
- Classifying table rows from a few examples
- Predicting a value from a data row
- Quickly testing hypotheses on new datasets
- Sizes
- 8B
- Hardware
- from: 1 GPU
Fact-checking and judgesNot maintained2024
Patronus AI · USA
Checks whether a chatbot invented a fact that is not in the source documents. The license is non-commercial. The checking model itself makes mistakes and does not replace manual review on important tasks.
- Finding invented facts in AI assistant answers
- Checking that answers rest on the attached documents
- Filtering out answers before they go to a customer
- Sizes
- 8B and 70B
- Hardware
- from: 1 GPU
Text to SQLOllamaNot maintained2023–2024
Defog · USA
One of the first open models that turn a plain-language question into an SQL query against a database. Available in Ollama, but newer competitors are already stronger.
- Answering managers' questions from the sales database without an analyst
- Drafting SQL queries for reports
- An assistant inside a BI system
- Sizes
- 7B – 70B
- Hardware
- from: Laptop
Text to SQLGGUFNot maintained2024
Chat2DB · China
A text-to-SQL model from the open Chat2DB database client: it supports different SQL dialects, with an English and Chinese model card.
- Turning a question into SQL inside a database client
- Drafting queries for different database engines
- Hints for developers working with a schema
- Sizes
- 7B
- Hardware
- from: Laptop
ForecastingNot maintained2024
ServiceNow, Mila and partners · Canada
One of the first open out-of-the-box forecasting models. Tiny, gives a probabilistic forecast, now behind Chronos and TimesFM.
- Probabilistic sales forecast
- Quick forecasting pilots
- Baseline model for comparison
- Sizes
- 2.4M
- Hardware
- from: Laptop
Text to SQLGGUFNot maintained2024
ChatDB · USA
A text-to-SQL model built on DeepSeek-Coder, aimed at complex questions spanning several tables and conditions.
- Complex queries joining several tables
- Answering database questions without an analyst
- Drafting SQL for reports and exports
- Sizes
- 7B
- Hardware
- from: Laptop
Documents and OCRNot maintained2024
Microsoft · USA
One model for every document task: reading, answering questions about a page, extracting fields, classification. In HR it is used to parse resumes and attached scans. A human makes the decision about a candidate; automatic screening without review must not be used.
- Extracting fields from a resume and its attachments
- Answering questions about document content
- Classifying incoming documents
- Sizes
- 742M
- Hardware
- from: Laptop
Text to SQLOllamaNot maintained2024
MotherDuck and Numbers Station · USA
A model for turning questions into SQL, built for the embedded analytics database DuckDB. Available in Ollama, convenient for working with CSV and Parquet locally.
- Plain-language questions about CSV and Parquet exports
- DuckDB queries inside analytics scripts
- Quick analytics on a laptop without a server
- Sizes
- 7B
- Hardware
- from: Laptop
FinanceGGUFNot maintained2023
AdaptLLM · not disclosed
Finance-tuned versions of Llama 2: reading industry texts, reports and questions about terminology. Not investment advice: decisions are made by a specialist.
- Reading financial news and reports
- Answering questions about financial terminology
- A base for fine-tuning to your own financial task
- Sizes
- 7B и 13B
- Hardware
- from: Laptop
Documents and OCRNot maintained2022–2023
Microsoft · USA
Small models that find tables on PDF and scanned pages and restore their structure: rows, columns, headers. The text inside is read by a separate OCR.
- Finding tables in reports, statements and invoices
- Restoring rows and columns for export to Excel
- Preparing tabular data for analysis and RAG
- Sizes
- 29M
- Hardware
- from: Laptop
Tabular dataNot maintained2023
OSU NLP Group, Ohio State University · USA
A general-purpose model for tables: filling gaps, finding rows, matching columns and answering questions about the data.
- Answering questions about tables inside documents
- Matching columns across different tables
- Finding and completing records in reference books
- Sizes
- 7B
- Hardware
- from: Laptop
FinanceNot maintained2023
Fudan-DISC, Fudan University · China
A financial assistant made of several fine-tuned experts: advice, calculations, document reading and knowledge-base search. Not investment advice: decisions are made by a specialist.
- In-house advice on financial questions
- Reading financial documents and news
- Prompts for front-office staff
- Sizes
- 13B
- Hardware
- from: 1 GPU
Text to SQLNot maintained2023
RUCKBReasoning, Renmin University of China · China
An early line of open text-to-SQL models starting at 1B, including variants fine-tuned for specific database schemas.
- Turning an employee question into an SQL query
- Drafting warehouse queries for a report
- Embedding into a BI dashboard as a helper
- Sizes
- 1B – 15B
- 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
Text to SQLGGUFNot maintained2023
Numbers Station · USA
One of the first open text-to-SQL lines, including very small versions from 350M that run on an ordinary PC.
- Turning a question into SQL from a table description
- Hints while writing queries
- A local analyst helper with no data leaving the company
- Sizes
- 350M – 7B
- Hardware
- from: Laptop
Image + textNot maintained2023
Google · USA
Reads a document or a screenshot as an image and answers with structure: text, fields, answers to questions. In HR it is fine-tuned for resumes and forms. A human makes the decision about a candidate; automatic screening without review must not be used.
- Extracting data from resumes and forms supplied as images
- Questions about the content of a scan
- Parsing tables and diagrams in documents
- Sizes
- 282M – 1.3B
- Hardware
- from: Laptop
Documents and OCRNot maintained2022
SCUT DLVC Lab, South China University of Technology · China
A light model that takes both the text and the position of blocks on the page into account: trained in one language and transferable to others. Good for tagging fields in resumes and forms. A human makes the decision about a candidate; automatic screening without review must not be used.
- Tagging fields in resumes and forms
- Extracting data from forms and templates
- Parsing documents in several languages
- Sizes
- about 130M for the English version and about 280M for the multilingual one
- Hardware
- from: Laptop
Text analysisNot maintained2020–2022
Microsoft · USA
Classic document understanding models: they take into account the text, its position on the page and the image. They are fine-tuned to extract fields from forms and receipts. Only the first version is free for commercial use.
- Extracting fields from questionnaires, forms and receipts after fine-tuning
- Classifying document types
- Answering questions about a scanned page
- Sizes
- about 110M – 370M
- Hardware
- from: Laptop
Documents and OCRNot maintained2022
NAVER CLOVA · South Korea
Reads a scanned document and returns a filled-in field structure straight away, with no separate OCR step. In HR it is fine-tuned for parsing resumes and forms. A human makes the decision about a candidate; automatic screening without review must not be used.
- Extracting fields from forms and resumes
- Parsing scans of certificates and diplomas
- Detecting the type of an incoming document
- Sizes
- about 200M
- 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
FinanceNot maintained2020
Prosus · Netherlands
A classic model that determines the tone of financial news: positive, negative or neutral. English only, runs fast on a CPU.
- Scoring the tone of company news
- Labeling reports and press releases
- Signals for analytics dashboards
- Sizes
- 110M
- 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
