CATALOGS OF NEURAL NETWORKS. DATASETS AND BENCHMARKS. SELECTION AND TRAINING OF MODELS.
05/08/2025
Updated on 08/10/2025
The number of neural networks is growing. They require more computing power — processors, memory, components, energy supply, and maintenance.
All of this, collectively, is data centers. Large complexes of electronic computing equipment, maintained by humans and robots. Some are funded by governments, others are built with investment capital. All of them provide access to high-performance computing for teams of scientists to accelerate the solution of complex tasks.
The presence not just of supercomputers but of super-powerful neural computing agents allows humans to obtain far more data in a short time. Neural computations mean phenomenal flexibility, multiple options for solving ultra-complex problems, and rapid calculation of results.
Data centers.
The most powerful and fastest supercomputer in the world today is El Capitan, located in the USA. It was built by Hewlett Packard Enterprise. Its performance is rated at 25.18 petaflops (PFlop/s), which is 25.18 quadrillion operations per second. The second and third most powerful in the world — Frontier and Aurora — are also located in the USA.
The number, location, and specifications of supercomputers can be found in the TOP500 catalog. Rankings and performance lists are also regularly published by the well-known scientific and analytical publication HPCWIRE and other tech resources.
Data center construction is a large market, led by major players such as the construction company AECOM, as well as DPR CONSTRUCTION, TURNER CONSTRUCTION CO., and other contractors. Rankings and lists can be found on MORDORINTELLIGENCE, COHERENT, or DATACENTREMAGAZINE.
In the USA, data centers must comply with the standards of the American National Standards Institute (ANSI), and in the European Union — with the CEN/CENELEC standards. Many companies also experiment with hot and cold air corridor isolation strategies between server racks to improve cooling efficiency.
The OpenAI project, in collaboration with the American cloud service ORACLE and the Japanese telecommunications holding SOFTBANK, is implementing the Stargate project with a budget of 500 billion USD. The project plans to build five new data centers in the USA for neural network data processing.
Elon Musk's company xAI is building the Colossus 2 supercomputer in Memphis, Tennessee, USA. Within six months, the company installed cooling systems with a capacity of about 200 megawatts, which supports the operation of around 110,000 servers based on Nvidia GB200 NVL72. Additional energy resources were also drawn from the neighboring state of Mississippi. The project is supported by the construction and technology energy company Solaris Energy Infrastructure. Colossus 2 is expected to become one of the most powerful neural data processing centers in the world.
Major players from the Asia-Pacific region are gradually entering the markets of Europe and the United States. For example, Taiwanese iPhone manufacturer FOXCONN is combining part of its capacities with Taiwanese electromechanical equipment producer TECO to strengthen their position on the global market.
Microsoft is building the world's largest data center Fairwater in Wisconsin, USA, which is expected to launch in early 2026. The company also participates in building data centers in Norway and the UK. The Microsoft Azure cloud has over 400 data centers in 70 regions worldwide. More information about the company's data centers can be found here.
In addition to stationary buildings, data centers can also be small, yet still contain servers, storage systems, switches, control systems, and elements of engineering infrastructure—such as climate control systems, fire suppression, and video surveillance. These help corporate businesses avoid delays in transferring data to large stationary data centers.
Container (mobile) data centers are roughly the size of freight containers to increase mobility. They are designed exclusively for outdoor installation.
Modular data centers are self-contained units that can be installed both indoors and in protected outdoor enclosures. Unlike containerized data centers, they are not transported as freight containers. Companies manufacturing such data centers include VERTIV, SCHNEIDER ELECTRIC, ZTE, CISCO, BMARKO, MODULAR DC, and others.
The market for mini-data centers—refrigerated server cabinets—is also growing rapidly. It is represented by companies such as CANOVATE and RITTAL, among others. A list of such companies can be found, for example, on INVEN.
The market is full of offers, and as they say — there's no better time to start a business than today.
NEURAL NETWORK CATALOGS.
The richest platform in terms of neural network discovery and interaction is undoubtedly the mega-popular GITHUB. Its vast repositories store all kinds of code. Every self-respecting developer has an account there. Giants like Google, Alibaba, OpenAI and others publish their models directly in GitHub repositories.
In addition to GitHub, there are other platforms based on the GIT version control system, including GITLAB, BITBUCKET, SOURCEFORGE, CODEBERG, GITEA and its fork FORGEJO, GOGS, the GITBUCKET hosting, as well as NOTABUG and SAVANNAH, home of the free operating system GNU.
On these platforms, you can find or build your own servers, libraries, and neural network configurations. You can store and run simple tasks using PYTHON scripts and frameworks such as PYTORCH, TENSORFLOW, and others.
More complex tasks like neural network training and weight generation are executed on server-based computing platforms that use GPUs — high-speed graphics cards with large memory capacity.
A major GPU-based platform is the ultra-popular HUGGING FACE with its model deployment service, Spaces. It hosts almost every popular open-source neural network, available through interactive demos. Models are sorted by domain, making it easy to find what you need. Many of them also offer APIs, which is a huge advantage for web developers. The platform also includes Hugging Face Datasets — a large collection of training and testing datasets.
In addition to Hugging Face, other computing platforms used for training neural networks, adjusting weights, and testing include GOOGLE COLABORATORY, which allows you to launch models directly from GitHub. Most pre-trained models can be found there. Training, evaluation, interface setup, and watermarking generations using invisible tags is possible with SynthID and the RESPONSIBLE GENERATIVE AI TOOLKIT.
PAPERSPACE GRADIENT is a machine learning platform that supports autonomous deployment via DOCKER. It also supports Jupyter Notebook (Piton Notebook) for .ipynb files directly from GitHub. It does not include datasets, but you can upload your own.
These platforms offer free tiers, but they're suitable only for very small and simple models. For broader deployment, payment is required. However, their main advantage is automation — everything you need to train and test a model for business use, quickly and efficiently.
Besides these specialized platforms, you may also benefit from other server solutions offered by large companies that provide their own machine learning tools.
AWS (Amazon Web Services) offers GPU servers and storage. There are free-tier options, but registration requires a valid credit card. AWS includes SageMaker — a model training and deployment platform, although setup can be quite complex.
MICROSOFT AZURE – GPU, visual environment for training neural networks, and virtual servers: NC (computing, neural network training, physical simulations on NVIDIA Tesla K80, V100, A100), ND (deep learning on NVIDIA Tesla P40, V100, A100), and others.
The demand for Microsoft Azure's computing power is growing, considering the contracts the company signs to lease it. Among its clients are OpenAI, the neural network technology provider NEBIUS, and other influential market players.
GOOGLE CLOUD PLATFORM – cloud service with Vertex AI (a suite of tools for training models). It features a special neural network processor, TPU (Tensor Processing Unit). Unlike standard GPUs, TPUs are optimized for tasks like training and running models, especially on machine learning frameworks like TensorFlow and Google’s JAX.
Google has developed the library TUNIX for optimizing post-training of large language models. It provides efficient and scalable support for fine-tuning, including reinforcement learning. The library integrates with the FLAX NNX framework with nested modules and a simplified API designed to simplify the creation, verification, debugging, and analysis of neural networks in JAX.
NVIDIA NGC offers Docker images with neural network models optimized for GPU and training toolkits (SDKs). The servers run on A100 and H100 GPUs, for which NVIDIA is well known. A wide selection of models is available, along with the NVIDIA NEMO FRAMEWORK for model training.
The agent created by RIGHTNOWAI and published on Product Hunt for optimizing and accelerating code performance on NVIDIA graphics processors (CUDA) has great prospects for improving the performance of video cards, and therefore for speeding up the operation of neural networks.
LAMBDA LABS – a platform designed for deep learning of neural network models. It is quite flexible in terms of compute rental — A100 GPU servers can be rented for various durations.
IBM CLOUD – offering the WATSON STUDIO platform for developing, training, and deploying AI models. Watson Studio includes convenient tools for working with data, building models, and scaling them. IBM Cloud also provides GPU instances (virtual servers) that can be rented for accelerated neural network training.
ORACLE CLOUD – a platform offering rental of GPU-based virtual servers for model training at relatively affordable prices. In addition, it provides prebuilt neural network tools and APIs.
CEREBRAS – a cloud hosting provider from a manufacturer of neural processors and supercomputing hardware. Offers flexible rental options for its compute infrastructure. The CS-3 processors, containing four trillion transistors, are claimed to be the fastest for neural network workloads.
OPENBESTOF – a collection of tools for working with large language models (LLMs). The site includes tools for training, interfaces, app development frameworks with LLMs, server solutions, and benchmarks for evaluation and testing.
Small models can be tested in the cloud development service REPLIT. The service can be used in a browser or as a standalone application. An initial free package for testing is offered, along with many useful frameworks.
The company GROQ, which has created not only data centers and a service cloud but also its own chip with LPU architecture, offers model deployment on its own technical infrastructure. It provides a free plan for familiarization and getting started with models, along with the Groq Python library.
These lists are not exhaustive, as today’s server capacity market continues to grow and new offerings are emerging. It is always possible to find an option that suits your business model — or even to create one that many consumers simply won’t be able to refuse.
Local installation and use of Google's model.
If you need autonomous computing power, you should focus on the minimum requirements based on current average offerings.
For example, you can use Google’s transformer model GEMMA 3, known for its minimal hardware requirements while still being capable of recognizing and generating text, images, audio, and video. This model won’t solve complex olympiad-level tasks, but it is quite adequate for everyday use and can become a reliable assistant.
To run it locally, you’ll need a computer with at least 8 GB of RAM and 10 GB of disk space. The model can run on a CPU, but a NVIDIA graphics card with at least 4 GB of VRAM is recommended.
If you have that, download the PYTHON transformers library, which includes loaders and installers for Gemma models from the repository, as well as Docker packages for deploying a separate environment. Install the library, the PyTorch framework, and the CPU/GPU universalizer with “pip install transformers torch accelerate”. The library will create a cache folder in your system memory, where it will load the model’s weights (parameters) with the extension .safetensors or .bin, along with tokenizer files — configuration files that describe how to split text into tokens — including a vocabulary (tokenizer.json, vocab.json, etc.). It will also create the model configuration file config.json, which describes the model architecture (e.g., number of layers, activation function). Additionally, install LM metrics with “pip install evaluate”.
After the library completes installation and setup, your RAM, VRAM, and CPU take over the task of running calculations via the PyTorch framework.
At the preparation stage, you create an NLP (Natural Language Processing) object in your computer’s temporary memory with the variable nlp = pipeline("text-generation", model="google/gemma-3n-1b-instruct-4k"). Using this, the framework loads the model configuration, tokenizer, and weights into temporary memory. If they are not present on disk, they will be downloaded from the Hugging Face repository.
When you interact with the model, you call nlp and write, for example: result = nlp("Write a fried egg recipe", max_new_tokens=50). These words are formed into a template that the model understands and passed to the tokenizer, which converts text parts into numbers in vector form. These numbers are then packed into an input tensor — a familiar data structure in the PyTorch framework: an array of numbers, essentially a table where each row is a number vector.
The model itself and its weights also consist of large parameter tensors (arrays of numbers). When our small text data tensor enters the model, it is inserted into specific formulas — which I won’t describe here. Inside the model’s internal layers, computations take place to find suitable tokens for the response. The model processes the input tokens — establishing relationships between the words “Write a fried egg recipe” and creating new vectors that are contextually close within the meaning of our sentence. The final set of these vectors for the input text “Write a fried egg recipe” is transformed into logits — the model’s vocabulary of all possible tokens.
Each token in the vocabulary receives a similarity score relative to the input tokens, but it is not yet adjusted — the model doesn’t yet know which tokens are close enough to our query, i.e., which of them have a score of 1. After that, the Softmax function converts all token scores in the vocabulary into positive numbers that sum up to 1. This function works non-linearly: small numbers become even smaller, while the largest increase toward values close to 1. Then, the model must choose which tokens to output as a response. Most models do this randomly, calculating only the probability: a token with a score of 0.01 has a 1% chance of being selected, while a token with a score of 0.85 has an 85% chance of being chosen. Such random selection makes the model's output more diverse. This selection can be controlled by the well-known “temperature” parameter, which is available in many neural network interfaces.
If you didn’t fully understand how the model works — don’t worry. With time, everything will fall into place, and as you gain practical knowledge, you’ll be able to train your own model the way you see fit. But model training is a separate story — even if it fits within this context.
If the local option is too weak for your tasks, let’s return to the global community that is actively accelerating the creation of large neural network models.
In addition to the aforementioned cloud services for hosting neural networks, there are aggregators of information about neural networks. Among them is SCALE, recently acquired by Meta, which has extensive experience in data labeling as well as in developing solutions for large businesses and government programs.
Another large directory is FUTURETOOLS. It features a user-friendly interface and high-quality sorting of a wide variety of neural network services.
THERE’S AN AI FOR THAT is an excellent collection of neural network services, sorted by needs and tasks.
AITOOLS is a solid collection of neural network services with detailed descriptions and convenient sorting.
TOPAITOOLS is a simple directory of neural tools with detailed descriptions of pricing plans and features, sorted by tasks with an easy-to-use search function.
INSIDR is a neural network rating service featuring reviews and guides on how to use AI tools in business, work, and entertainment.
ALLTHINGSAI is a collection of neural network usage guides, reviews, interviews with developers, and model rankings.
TOOLIFY is a catalog of neural networks with a rich interface and a wide range of sorting options.
AICYCLOPEDIA is a directory of neural networks with detailed descriptions of their capabilities, as well as a collection of articles on the history of neural networks and cutting-edge tools in the field of artificial intelligence.
REPLICATE is a provider of ready-to-use neural tools with API offerings. Hosting services are also available.
OPENROUTER is a well-known neural network API provider with a decent free trial plan. It also features a model ranking table and a vibe-environment for connecting services to various apps without programming knowledge.
You can also use PROMPTHERO as a collection of prompts for various image and video generators. For ChatGPT specifically, there is an interesting collection of chat interfaces at FLOWGPT with ready-made prompts for various needs.
On the prompt collection platform PROMPTBASE, you can sell or buy prompts, as well as quickly find the right prompt for the desired neural network by category or search.
DATASETS.
What do neural networks learn and train on, what kind of data do they use, and how do they understand it?
Principles of neural network training.
To understand the principles of data annotation for training neural networks, it’s necessary to highlight the main types of their architectures:
1. Feedforward Neural Networks (FNN) — the signal moves only forward, from input to output. Used in classification and regression tasks (predicting numerical values based on input data and returning numbers).
2. Convolutional Neural Networks (CNN) — used for processing images and videos. Contain convolutional layers for feature extraction — face recognition, object detection, medical diagnostics from images.
3. Recurrent Neural Networks (RNN) — used for sequential data (text, audio, time series). They retain memory of previous inputs and are applied in machine translation, chatbots, and speech recognition.
4. Transformers — the standard for processing text and multimodal data. Work based on attention mechanisms (GPT, BERT, LLaMA, Gemini) and are used for text generation, translation, chatbots, and generating images and videos.
5. Generative Adversarial Networks (GANs) — consist of two networks: a generator and a discriminator. Used for image generation, deepfakes, upscaling, video, and music.
6. Autoencoders — compress data (encoder) and then reconstruct it (decoder). Used for compression, denoising, and generation.
7. Residual Neural Networks (ResNets, DenseNets) — use residual connections. They improve trainability and are often used in complex computer vision tasks.
8. Graph Neural Networks (GNN) — process graph-structured data (social networks, molecules), used in recommendation systems and bioinformatics.
9. Spiking Neural Networks (SNN) — mimic the behavior of biological neurons, energy-efficient, and used in neuromorphic chips.
10. Hybrid and specialized neural networks:
Multimodal neural networks (e.g., Gemini, GPT-4o) — work with text, images, and audio simultaneously.
Diffusion models — generate images/videos, e.g., DALL·E 3, Stable Diffusion, Veo.
Reinforcement Learning — neural networks trained through trial and error.
Ensemble learning of models is increasingly being used, where each model produces its own result. The outputs of the models are processed in different ways within various architectures by a larger model, which provides the final outcome. In this article, the researchers propose an interesting "least squares" method for combining the outputs of different models. This method minimizes the sum of squared deviations between the model predictions and the physical values. According to the results obtained by the researchers, this approach yields more accurate predictions and fewer errors when dealing with noisy data.
Let’s not delve into the complex training processes of model classifications, which involve dozens or even hundreds of nuances. The rapid advancement of training technologies blends these types into complex architectural ensembles. Most modern models today are, for the most part, transformers.
For example, the powerful model HUNYUAN-A13B with 80 billion parameters operates as a transformer but enhances its architecture with a Mixture-of-Experts (MoE) approach, where the main neural network is supplemented by other neural networks — “experts” — each with 1.5 billion parameters. When a request is received, the main neural network decides which experts to assign the task to. It sends the query only to qualified networks. As a result, not all 80 billion parameters are used, but only 13 billion, which makes the model much faster and more energy-efficient. The technology isn’t new, but it is effective.
The large language model KIMI-K2-INSTRUCT from Moonshot AI, which topped benchmarks in mid-2025, operates similarly with 32 billion activated parameters out of 1 trillion total parameters. It was trained using the MUON optimizer, based on matrix orthogonalization (when the columns or rows of a 2D table become perpendicular to one another, and the table itself becomes 3-dimensional). According to the Hugging Face calculator, the Kimi-K2-Instruct model occupies 958.52 GB of storage on the platform.
The service BRAINPRO, which provides attention estimation for images and videos, uses convolutional and recurrent models trained on eye-tracking datasets. The service evaluates viewer attention to objects in an image or video frame. This feature is essential for marketers, designers, and cinematographers to understand what captures the viewer’s gaze. Implementing such an evaluation process requires combining multiple architectures trained on specialized datasets (in this case, the site mentions using a survey and attention dataset from 10,000 people).
The owner of TikTok, BYTEDANCE, took the path of changing the reasoning language of the model and created a neural network for theorem proving called SEED-PROVER. The issue is that natural human language, typically used by large LLMs, does not provide adequate feedback for verifying the correctness of mathematical proofs. However, the mathematical programming language LEAN helps neural networks verify responses using formal theorem proofs. The essence of the language is that its library already contains all proven theorems and axioms, which can be referenced when proving unproven ones. In addition, the language includes tools to assist mathematicians. A theorem-proving engine for geometry was also integrated into the system.
An enthusiast from the YouTube channel Build With Binh created and launched a language model with 260 thousand parameters based on the advanced SoC (System on Chip) microcontroller ESP32-S3.
Let’s look at the principle of dividing models based on data labeling. This principle involves either labeling the data with feature tags or leaving it unlabeled. There is also a method of partial labeling for fast training, but it too depends on the same dozens of nuances and specific tasks.
Labeled data is divided into synthetic and manual. Synthetic data is generated either by a neural network or an algorithm and usually consists of massive datasets with questionable accuracy.
Human-labeled data is more accurate but very expensive and rare, as labeling large datasets requires significant labor resources. For example, about 900 employees work in the data annotation department of Grok’s owner, the company xAI.
The process of creating labeled data follows a step-by-step sequence.
1. Data collection — downloading datasets with files or creating your own datasets with texts, images, audio, video, or other needed data. Usually, websites like Wikipedia and data-hosting platforms are scraped. Result: a dataset with unlabeled files.
2. Data cleaning using Python scripts: removing emojis and special characters from text, eliminating duplicates, and converting all data to a consistent format (.txt, .jpg, .mp4). For images and videos, the tensor will be pixel arrays with color values. But let’s take text data for a language model, where the tensor is simply numbers.
3. Creating a data structure using Python scripts: forming objects (text parts in a CSV table or files in a raw folder) with ID identifiers, e.g., from 00001 to avoid offset errors up to 99999. Files and text parts don’t yet have labels but are already treated as objects with IDs. Let’s choose the table method — it’s more structured for large data volumes. Each row in the table has an ID and the corresponding text part or a link to a file in the raw folder.
4. Creating a labeling scheme — a set of categories, classes, or tags (features) that need to be assigned to each object. Depending on our goals and model architecture, we must assign characteristics to each object in the table. For example, we might label the sentence “That’s wonderful” as “Positive,” “Good,” or “Excellent”; the sentence “I’m sorry” as “Bad,” “Negative,” or “Sad”; and the sentence “The chicken laid an egg” as “Neutral,” “Practical,” or “Normal.” Accordingly, we add a column EMOTION, where we specify tone: “Positive,” “Negative,” or “Neutral,” and also a column TOPIC, where we note that “I’m sorry” is an emotion, while “The chicken laid an egg” has neutral tone and a topic like “birds.” You can also add other columns for features — such as case, form, age, size, or even multi-tags like animal, bird, flightless bird. The number of columns may range from 3–4 up to 10–12. If you have a lot of data, it’s better to split them across several tables. Tools like LABEL STUDIO, PRODIGY, or others can be used, depending on your needs.
5. Creating dictionaries and numerical tags — for each column of features in our table, a dictionary is created. In the dictionary, we assign a numeric value to each unique feature. For instance, if the “positive” label occurs 25 times, “neutral” 2,500 times, and “negative” 2,450 times, then the dictionary will contain these three words assigned with numbers 1, 2, and 3 respectively.
6. Inserting feature numbers into our table — we create a new column and, next to each feature, write the corresponding number from the dictionary (i.e., the tensor).
7. LABEL — this is marked in the neural network training script as “y.” We select one of the most important numeric feature columns “X” from the table based on our task. This selected column becomes the numerical label column (label), the value of which the neural network will try to predict. Essentially, this column becomes the label vector — a sequence of numbers from top to bottom (or right to left). The other, less important feature columns can be conditionally referred to as the matrix. It is crucial to remember which column was selected as the label.
8. Experts typically recommend checking the table for errors, as they are common at this stage. Then the data is split into three categories — the table file is split by a Python script into: 70–80% training data (train), 10–15% validation data (validation) — used for model tuning, and 10–15% test data (test) — used for final quality evaluation. For this, you can use the NUMPY library, which helps Python perform mathematical operations on large data arrays.
9. Table cleaning — only the table consisting of columns with numbers taken from dictionaries will be loaded into the model. The model will see only the numeric tables: “X” — the matrix of less important feature columns, and “y” — the label column, which it must learn to predict.
We have now reviewed the preparation of data for training a neural network in order to understand what we need from datasets.
The training process itself is a set of mathematical computations based on a weight matrix W of size (m, k), where k is the number of neurons (output units) in the first trainable layer with weights. If we have 4 features (i.e., m = 4) and want the first hidden layer to have 10 neurons (i.e., k = 10), then the weight matrix W will be 4×10. Each of the 10 neurons receives 4 features (e.g., weight, emotion, text language, author). Each neuron has its own weight for each of the 4 features. As a result, we have 40 weights for the first hidden layer. Each neuron receives 4 inputs, multiplies them by 4 weights, sums the results, and adds a bias (b) — an offset that in most cases prevents the function from passing through the zero coordinate (i.e., when a feature X is missing, X = 0), because multiplication by zero distorts the calculation of real-world characteristics. In essence, the bias is also a weight — any number multiplied by 1. The formula is expressed as z = w₁x₁ + w₂x₂ + ... + wₘxₘ + b×1, where z is the activation vector of each neuron. The weights of the layer are the parameters that connect input features to these neurons. To fine-tune the weights and minimize the error value, coordinate system planes are used to help track the direction of steepest increase of the error function, with minimal changes to weights in multidimensional space (hills and valleys) — gradient descent step by step determines the direction opposite to the gradient — toward error minimization. However, we won’t go deep into this complex topic now — it’s often covered in entire books.
It’s also important to keep an eye on tools supported by the community that are highly effective in improving training, such as libraries like TRANSFORMERS, ACCELERATE, frameworks like COLOSSALAI, FASTCHAT, and others — along with execution libraries like vLLM, DEEPSPEED, FASTERTRANSFORMER by Nvidia, and frameworks like NVIDIA DYNAMO-TRITON, OPENLLM, and others.
Try a text corpus tokenizer (unlabeled texts) LLM Tokenizer with Pricing Calculator in Zig , built in the cross-compilable language Zig. The algorithm for splitting text corpora into tokens, Byte-Pair Encoding , was originally developed as a text compression algorithm. Later, it was used by OpenAI for tokenization during the pretraining of GPT. It is applied in many transformer models, including GPT, GPT-2, RoBERTa, BART, and DeBERTa. This algorithm uses unique sets of words found in the corpus (after normalization and preliminary tokenization stages), then builds a vocabulary containing all the characters used to write these words. This makes it possible to avoid unnecessary characters from ASCII and other patterns.
The open community developing Zig has created a compiler, a language, and a library in a single tool. The backend of the language is the core of the LLVM project — a C++-based toolkit for building highly optimized compilers, optimizers, and runtime environments. Zig supports all modern processor architectures, works with string literals (separate strings divided by \0) and Unicode code point literals, just like C++. The Zig source code is presented in UTF-8 encoding.
If you don’t want to start from scratch, you can use ready-made services to train pre-trained models. These include popular services like OLLAMA, LMSTUDIO, GPT4ALL from Nomic, TEXTGEN, Google LiteRT-LM, and others that offer local models out-of-the-box for Windows and Linux. All the processes described above are already worked out and configured here. You only need to install the program and load the model onto your computer.
If you have launched a neural network locally but want to access it from a mobile interface, the simplest GitHub-hosted web interface connected to the API of your local server, tunneled through NGROK, will solve this task not only for you but also for anyone you want to share the neural network with. To check API errors when setting up the server, you can use POSTMAN or the legendary curl.
Also, take note of Google's machine learning automation tool MLE-STAR. The authors state that MLE-STAR tackles machine learning tasks by first searching the internet for suitable models to build a solid foundation. It then carefully improves this foundation by checking the most critical parts of the code. MLE-STAR also uses a new method of combining multiple models for even better results. This approach has proven highly successful — it won medals in 63% of Kaggle competitions for MLE-Bench-Lite, significantly outperforming alternatives. The agent is configured for strict adherence to Python coding guidelines and NumPy documentation when writing executable code. Google has also launched a vibe-coding service OPAL for building applications using a node-based system and AI-generated prompts. To work with code, Google has an agent called JULES, which can monitor repositories, update dependencies, and test code execution on its internal virtual machine.
To check code for critical vulnerabilities, Google DeepMind has created an agent called CodeMender. CodeMender uses a comprehensive approach, instantly eliminating entire classes of vulnerabilities.
It’s worth noting that not every task requires training a neural network from scratch. For simple tasks, there are three popular methods of training a pre-existing model on data it hasn’t seen before.
1. SAG (System Assistant Generator) — a prompt template for a neural network that a script-server adds to the user’s request. It is used for agents on small websites and in applications. A large LLM receives both the user request and an instruction with data on which it bases its response. The size of the SAG prompt is usually small, as it depends on the number of input tokens allowed by the neural network’s API. If the neural network has a built-in browser, the prompt can contain page URLs with instructions and answer texts. Upon receiving such a prompt, the model follows the links, reads the content, and generates a reply based on the gathered information. This is a simple, cheap, and quite flexible way to tune the agent’s tone. Do not confuse it with Stochastic Average Gradient (SAG), a loss optimizer used in training neural networks.
2. RAG (Retrieval-Augmented Generation) — a prompt generation architecture in which a prompt is sent to the neural network along with the user’s request. This is a more complex and labor-intensive process, allowing for the handling of large volumes of data.
To create RAG from a text, the text must first be prepared — split into topic-based chunks of 512–1024 tokens (roughly 100–200 words), or smaller. The chunks should overlap by 15–20% to help the neural network retain the full meaning of the segmented text.
These text chunks must be passed through a neural network model that transforms them into vectors (embeddings) and stored in a vector database such as MONGODB, WEAVIATE, ZILLIZ, or others. Embedding models are available from providers like Google’s VERTEX AI, OPENAI, and other AI companies. Vectors can also be stored in a traditional relational database as JSON arrays if the dataset is small, e.g., in MYSQL or Google’s DATABASE.
How RAG works: when a user sends a query, the text of the query is passed to an embedding model, which converts it into vectors. These vectors are then compared to the vectors in your database using a similarity metric (e.g., cosine similarity) via a script. For large datasets, it’s better to use a storage system with fast indexing to find the nearest vectors. The most relevant chunks are passed to the script and then to the large language model (just like in SAG), which returns a response based on the retrieved data. The script compares query and database vectors mathematically (Bi-Encoder), which is fast but — if not configured properly — may cause the LLM to hallucinate by using irrelevant text chunks. An indexed vector database like PINECONE or QDRANT can speed up this process by avoiding full vector scans, though at the cost of slightly reduced accuracy.
To refine the search results, you can use an intermediate Cross-Encoder model to rerank the query/response candidate pairs. Such models are also available — for example, MINILM, BAAI, Google EmbeddingGemma, and others.
Overall, RAG works well after careful tuning of all these parameters for a specific task. Defining the agent’s specific task is when the RAG parameters must be configured.
If you know that a large language model will often have to respond to similar queries with the same answers,
you
can
add the CAG (Cache Assistant Generator) method to the system. CAG allows storing previously
generated
responses from the model in a cache (on the server if responses are numerous), assigning keys to them, and
retrieving
them via a comparison script using "key = hash(user_input)", for example with the hashlib
library. First, assign this hash key to the query and search for the same in the cache. If the query is
exactly
the
same, the hash keys will also be identical. If no such key is found, the user request is sent for normal RAG
processing. After the LLM returns a response, the response is assigned the same hash key as the query and
stored
in
the cache. Since queries to LLMs are rarely identical (unlike URLs in online stores), and because
hashlib.sha256(user_input.encode('utf-8')).hexdigest() is highly sensitive to every letter, many
keys
will be created — which may bloat the cache.
When developing and configuring RAG for business, check out the study by Google DeepMind, which describes the limitations even for simple queries. The researchers provide the tools and datasets they used themselves. The repository contains everything needed to avoid mistakes.
Meta also proposes a method to eliminate redundant context computations in RAG during decoding. The company introduced REFRAG – an efficient decoding framework that compresses, recognizes, and expands data to reduce latency in RAG applications. By exploiting the sparsity structure, a 30.85× acceleration in time-to-first-token is achieved (a 3.75× improvement over previous work) without loss of accuracy.
You can use frameworks for agent development such as EKO with a unified interface from FellouAI in JS, MIDSCENEJS for building multimodal agents, LangChain for LLMs in Python, DIFYAI for rapid development of LLM applications with deployment via Docker, CREWAI with UIStudio, the HAYSTACK, environments for running models FIREWORKS, environment for multimodal agents, Semantic Kernel from Microsoft for enterprise-level solutions, OpenAI Agents SDK and OpenAIAgentKit, COZE, and the browser-based site parsing agent BROWSERUSE.
One of the leaders in deploying neural network agents is COHERE, the company behind the Command family of LLMs. The company offers a wide range of services, from deploying the models themselves for enterprise use to integrating agent environments into other digital ecosystems.
The creation of agents for robotic systems can be implemented in LATTICE-SDK from the military-industrial company Anduril. The service provides a real data simulation environment, Lattice Sandboxes, for controlling technical means without involving physical hardware. Similar agent "sandboxes" for automation debugging are also offered by REPLIT, RUNPOD, UNITYML-Agents, GAZEBOSIM, AI2THOR, open UnifoLM-WMA-0 and others.
If you do not want to get into issues of load distribution, database setup, and other technical complexities, you can take a ready-made agent and customize it to your needs. In this case, you can try the agent ELYSIA, an agent for programming SIMULAR, agent systems MAGENTIC-ONE, II-Agent, MAO-ARAG, the framework ELIZA. For the operation of autonomous search agents in the RAG system, there are projects with detailed descriptions here. For RAG personalization, there are also projects. The description of one of them can be read here.
For busy people, there are vibe-coding systems - online services where the sequential connection of ready-made code blocks (nodes) determines the agent’s workflow. These platforms do everything for you: you only need to choose the logic of your agent and input the data. You can try automation platforms like ZAPIER, RETOOL, LANGFLOW, ARIZE, or others. For web interfaces, you can use prompt-coding services that create interfaces and applications from descriptions. This can be LOVABLE, TELEX, SALESFORCE, or others.
The MGX service, running on LRM, creates personal agents for your website or project. You just need to describe what you want, for example an agent for reading emails, and it will create a repository with project files that can be deployed in a development environment and connected to your neural network’s API.
You can also add the capabilities of the open service UISHADCN on VITETAMPLATE with templates that generate component designs in different programming languages.
Using the Model Context Protocol standardized by the company Anthropic for agents’ access to your resources would be a not bad solution. At the same time, already ready MCP servers can be downloaded from Anthropic itself or, for example, from Awesome MCP Servers.
You can also use the inter-agent protocol Agent2Agent (A2A), designed for interaction between agents operating in different frameworks.
When configuring an agent to access your databases, use intermediate security resources such as TELEPORT, STRONGDM, BOUNDARY, CASBIN, or others that grant the agent permissions to access information. This helps prevent "prompt injections" into LLMs aimed at obtaining unrestricted access to all information. Monitor the operation of applications, since the neural network works unstably. You can try for this services like LANGSMITH, which create a trace of executed operations.
3. Fine-tuning - additional training of a large language model by sending it data (e.g., in a `gpt_data.jsonL` file for GPT). Each line in the file should contain a prompt in the following format:
{ "messages": [ {"role": "system", "content": "Your system context"}, {"role": "user", "content": "User question"}, {"role": "assistant", "content": "Desired model response"} ] },
For example, one prompt in a single line would look like this:
{"messages": [{"role": "system", "content": "You are a consultant for the STOP AI DECEPTION project. You respond briefly (up to 30 words), in a friendly and to-the-point manner using conversational phrases ('helping people deceived by AI', 'how can I assist you?', 'let me share a few tips to avoid being misled'). Don’t be afraid to ask for clarification (like the story of the deception). No emojis, no fluff, confident tone."}, {"role": "user", "content": "I want to share my story."}, {"role": "assistant", "content": "Great. I'm ready to listen. Your story will be shared on our site to help warn others."}]}
— each such prompt should occupy one line/sample in the code editor. You can write prompts in convenient Google Sheets (CSV), and then convert them to `.jsonL` using an app script.
Training large models through the OpenAI API is paid. You’ll need a script to work with the prompt, along with the OpenAI library for training. All details can be found in the OpenAI documentation. I won’t rewrite the docs here, but I will say that fine-tuning a model — even just its adapter layers — is a non-trivial task.
For the model to interpret data correctly, careful tuning of weights is required, which means multiple passes (epochs) through the entire dataset. For large datasets of 5–7k sample lines, the number of epochs can reach 8–10. For each epoch, you need to define the batch size — for example, 50, meaning 50 lines per batch. For a 5k dataset, this results in 100 batches per epoch. You also need to define the learning rate — which controls the gradient descent (step-by-step movement in the direction opposite to where the error function increases most, in order to minimize the error). All of this is configured in a script tailored to your specific task. Don’t forget to split your data into training and test sets.
I would like to add that self-learning neural network models are no longer rare. For example, Meta is constantly improving its DINO models using SSL (Self-Supervised Learning), which are trained on self-generated input signals by an internal “Teacher–Student” algorithm. Such models do not require labeled data for training. They are trained on universal datasets and learn to predict averaged values. For example, they can complete images and track cats, dogs, and birds on a screen.
Mistral offers various tools for training neural networks in its Console. Within reasonable limits, you can use their neural networks via the API together with ready-made tools. Mistral chose the convenient approach of Google Cloud Console, where all tools are gathered and integrated with each other.
Thinking Machines is developing TINKER — an API for fine-tuning models after training. The project includes two libraries: 1. tinker - a training SDK for researchers and developers that enables precise fine-tuning of language models. The user sends API requests to obtain distributed training configurations. 2. tinker-cookbook - realistic examples of fine-tuning language models. It is based on the TINKER API and provides general abstractions for fine-tuning language models.
An interesting methodology for training neural networks to detect stress from text written by a person online is proposed by the authors of the paper in the journal Nature. The novelty lies in the integration of several advanced text representation methods, such as FastText, Global Vectors for Word Representation (GloVe), DeepMoji, and XLNet, with Depth-wise Separable Convolution with Residual Network (DSC-ResNet) for precise stress detection. The Chaotic Fennec Fox (CFFO) optimization algorithm tunes the hyperparameters. The DSC-ResNet model is enhanced through hybridization of the depth-wise separable convolution layer with the ResNet model. The proposed model is implemented on the Python platform.
That concludes our basic overview of training methods. Let’s move on to publicly available datasets.
Databases for training neural networks.
You can of course use Google's dedicated dataset search to find datasets. However, this tool is more suitable for discovering data sources than for locating ready-to-use datasets for model training. It may point you to many websites with analytics and data, but often that data is hidden behind paywalls or available only as industry-specific reports — for instance, for a fee on INFINITIVE DATA EXPERT, or for free on FAOSTAT. Such data will require you to clean and sort it manually.
The search results will often include links to well-known dataset platforms like KAGGLE, Google’s OPEN IMAGE V7, and other dataset repositories like TENSORFLOW, as well as cleaned Wikipedia text on Hugging Face.
Google has a large dataset called Natural Questions of 42 GB, which contains real user search queries paired with the corresponding Wikipedia pages. The dataset is a text corpus (not tokenized), annotated with long/short/no answer labels to mark answer boundaries. 49% of the examples contain long_answer. The training set includes over 300K examples, the validation set — about 8K examples, and the test set - about 8K examples.
If you have raw text data, such as old books or magazine articles, you can use data cleaning services like LLAMAINDEX, UNSTRUCTERED, TEXTMECHANIC, TEXTCLEANER, and many other web-based tools. Alternatively, you can use Python parser libraries: DOCLING for text and PANDAS for tables.
COMMON CRAWL is a non-commercial resource that provides open data collected through web crawling. These data can be used in research and the development of neural networks requiring large volumes of textual information from the internet. The resource contains HTML pages, metadata, headers, and other components. It is updated once a month, collecting around 250 TB of data. The datasets are available on the Amazon Web Services PUBLIC DATASET platform at paths listed here, or they can be searched via the CDX file index system of the resource. Common Crawl data is used by OpenAI, Meta, Google Research, as well as the open high-quality filtered text data service for training its own models — LLM ELEUTHERAI.
CORNELL UNIVERSITY is a private research university in the USA, located in Ithaca, New York. It was founded in 1865 by Ezra Cornell and Andrew Dickson White as a university where any person can find instruction in any study. The university provides ready-made datasets for training in computer vision and a repository of open data with convenient sorting where datasets can be found.
On the university’s servers is the large open-access archive arXiv, containing nearly 2.4 million scientific papers in physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, as well as economics. The archive includes many preprints on artificial intelligence. There, one can find technical reports on the development and testing of powerful LLMs such as EXAONE 4.0, and access various benchmarks, such as OLYMPIADBENCH (8,476 problems from mathematics and physics olympiads), LIVECODEBENCH (source code), OJBENCH (competitive programming), BIG-BENCH EXTRA HARD (LLMs' ability to find logical errors), and many others.
ZENODO is a repository of numerous datasets in various formats. The OpenAIRE project, at the forefront of the open access and open data movement in Europe, was commissioned by the EUROPEAN COMMISSION to support the emerging open data policy by creating a universal repository for research funded by the European Commission. The collider builder and fundamental particle structure researcher CERN partnered with OpenAIRE to ensure open access to data. The project launched the Generalist Repository Ecosystem Initiative of the National Institutes of Health (NIH GREI), involving such data processors as: the collection of open scientific data repositories DATAVERSE, the open data publication platform DRYAD, the open research repository infrastructure provider FIGSHARE, the free cloud collaborative repository MENDELEYDATA, the free platform for research support OSF, and the independent non-profit platform for clinical trial data sharing VIVLI. To work with repositories, Zenodo uses the INVENIO framework, which supports a system of persistent digital identifiers DOI according to ISO standards, as well as integration of small scientific databases into larger national-level infrastructures, such as NFDI in Germany, UK data analytics organization JISC, the humanities archive UK DATA SERVICE, and the French agricultural archive of the INRAE institute.
SQuAD – datasets from Stanford University used in natural language processing research and contributing to the development of question-answering systems and machine reading comprehension. The Stanford Question Answering Dataset consists of more than 100,000 question-answer pairs selected from various articles, books, and other textual sources. Each question is linked to a specific paragraph containing the answer. This diverse collection covers a wide range of topics, ensuring that models trained on SQuAD can handle queries of different types across various domains. It is a benchmark in the field, providing a rich collection of questions and corresponding texts. The dataset also includes unanswerable questions, serving as an excellent benchmark for testing natural language understanding.
UC IRVINE MACHINE LEARNING REPOSITORY – a large repository of ready-to-use datasets with descriptions. The archive was originally created in FTP format in 1987 by University of California, Irvine graduate student David Aha. The current version of the website was released in 2023.
OPEN POWER SYSTEM DATA – a free data platform designed for power system researchers. It collects, validates, and publishes data that is publicly available but currently inconvenient to use. The project provides services to the energy system modeling community.
MIMIC-III is a large, publicly available database containing de-identified health data of over forty thousand patients who stayed in the intensive care units of the Beth Israel Deaconess Medical Center from 2001 to 2012. The database includes information such as demographic data, vital sign measurements at the bedside (approximately one data point per hour), lab test results, procedures, medications, caregiver notes, imaging reports, and mortality data (including post-discharge mortality). Also available: MIMIC-CXR and PADCHEST.
IGSR – the International Genome Sample Resource, based on the 1000 Genomes Project, provides a unified representation of data and samples from nearly 5,000 samples across various studies. All data are fully open and publicly accessible.
CHEXPERT – a dataset consisting of 224,316 chest X-rays from 65,240 patients who underwent radiological exams at Stanford University Medical Center from October 2002 to July 2017, both inpatient and outpatient. The CheXpert dataset includes training, validation, and test sets. The validation and test sets have labels created by board-certified radiologists. The training set contains three sets of labels automatically extracted from corresponding radiology reports using various labeling tools (CheXpert, CheXbert, and VISUALCHEXBERT). A collection of related publications is available here. Information about training on both ChestX-Ray14 and CheXpert datasets and the differences between them can be found here. The NIH CHESTX-RAY14 DATASET (CXR8) contains 112,120 chest X-ray images, 30,805 patients, and 14 diagnoses. A form must be filled out on the site to access the dataset.
HEALTHBENCH by OpenAI uses input from over 260 doctors across 60 countries to develop medically grounded evaluation criteria and tests AI performance in broad clinical scenarios using more than 5,000 multi-turn doctor–patient dialogues and over 48,000 rubric items. It utilizes datasets on cancer, COVID-19, cardiology, neurology, and other diseases. Its data builds are accessible for training your own models.
COVID-19 IMAGE DATA COLLECTION – a dataset of chest X-rays and CT scans from patients with suspected COVID-19 or other viral and bacterial pneumonias.
Awesome-biological-image-analysis – tools for analyzing and processing biological images, as well as datasets for training neural networks.
BRAIN TUMOR SEGMENTATION CHALLENGE – datasets of multi-institutional preoperative MRI scans focused on brain tumor segmentation. Many other useful datasets are also available.
MULTIPL-E – a dataset for training and evaluating models on Multi Programming Languages. It contains programming problems with solutions in various languages.
MINARI – a collection of ready-to-use datasets for training reinforcement learning agents (reward and punishment). Each dataset includes environment data: states (pre-recorded agent–environment interaction sequences), which may be represented as either images or vectors of blind actions. These states are pre-vectorized, for example using convolutional neural networks, to convert images into numerical data understandable by neural networks. Based on the vectorized states, the neural network chooses actions aimed at maximizing total reward. After each action, the agent receives a reward or punishment signal. By repeating this cycle, the model learns optimal behavior within the environment. Similar workflows are used by OFFLINERL-KIT, RL UNPLUGGED, GYMNASIUM by OpenAI, and others.
PAPERSWITHCODE-DATA – an archive of metadata in JSON format with links to
external
datasets, scientific papers, and machine learning methods. The sota-extractor Python library
included in
the repository is used to work with the files. Due to the previous unavailability of the once extremely
popular
PAPERSWITHCODE site (now
redirected
to GitHub) which hosted a large dataset repository, some links may be outdated.
IMAGENET – a free image dataset. According to the site, the database hierarchy is similar to the Princeton lexical database WORDNET for the English language. WordNet groups objects by meaning and then connects those groups with small conceptual relationships. For example, if an armchair is a kind of chair, and a chair is a kind of furniture, then an armchair is a kind of furniture. WordNet distinguishes between types (common nouns) and instances (specific people, countries, and geographic entities). Thus, an armchair is a kind of chair, while Barack Obama is an instance of a president. Instances are always leaf nodes (end points) in their hierarchies.
COCO is a service with datasets for detecting and segmenting large objects and assigning labels to them, offering an API. It provides its own application, plugin, and the open-source library FIFTYONE for image cubing, location-based data visualization, data cleaning, complex object extraction, and much more. It also includes the FiftyOne Model Zoo — an interface for downloading models and applying them to FiftyOne datasets. This interface gives access to pre-trained models and supports uploading custom public or private models.
CIFAR is a service that in 2006 copied 53,464 object names from WordNet and then downloaded their images via internet search. The feature of its datasets is that all 80 million images are low resolution — 32x32. It consists of the Cifar10 dataset — 60,000 images across 10 classes, and Cifar100 — 100 classes with 600 images each. Each class contains 500 training and 100 test images. The 100 classes in CIFAR-100 are grouped into 20 superclasses. Suitable for training computer vision models.
LSUN (Large-Scale Scene Understanding) is a large dataset of images of structures and objects, categorized into “bicycle”, “bedroom”, “bridge”, etc., ranging in size from 3GB to 160GB. For downloading data and converting pre-labeled data into the binary format .tfrecord (for large-volume storage), it uses Google’s TFDS library for TensorFlow in Python, which can also be used with PyTorch and NumPy. It also handles .tfrecord to tensor conversion.
LAION-5B is an open dataset containing 5.8 billion image-text pairs for training convolutional and transformer models to recognize images and link them to text. Using these two encoders, the model learns to recognize closely related image and description vector pairs.
ACADEMIC TORRENTS is a repository of raw data of various formats — from programming video courses to downloaded WIKIPEDIA data. The repository is community-driven.
VGGSOUND is a large open dataset of sound clips and videos collected by the Visual Geometry Group (VGG) at the University of Oxford. It contains thousands of short videos with various sounds used to train and test sound recognition and multimodal analysis models.
The open neural network research community LLM360, which released the mathematical model K2THINK with support from the Mohamed bin Zayed University of Artificial Intelligence in Masdar City, Abu Dhabi, presents its own models, datasets, and metrics. One of the community’s developments is the toolkit Analysis360, which covers mechanistic interpretability, visualization, machine unlearning, data memorization, AI safety, toxicity and bias evaluation, as well as a broad set of benchmarking metrics.
The Pile - a diverse open-source language modeling dataset of 825 GB, consisting of 22 smaller high-quality datasets combined together. The format of The Pile is jsonlines data compressed with zstandard.
For the food industry, one can use the multilingual product database OPEN FOOD FACTS, the database of nutrients, vitamins, minerals, portion sizes, and standardized product names USDA FOODDATA CENTRAL, as well as the International Food Composition Tables with averaged values by country and region FAO/INFOODS.
OPENSLR is a database containing a wide variety of speech, language, and audio datasets.
VOXCELEB is an audiovisual dataset consisting of short clips of human speech extracted from interview videos uploaded to YouTube. The VoxCeleb1 dataset contains over 150,000 utterances from 1,251 celebrities, and VoxCeleb2 contains over 1,000,000 utterances from 6,112 celebrities.
Two human emotional state datasets — Emoset with 3.3 million images, of which 118,102 are carefully annotated by human annotators, and CAER with 13,000 annotated videos and 70,000 images — are offered by the ELBENCH benchmark.
COMMON VOICE by the Mozilla community is a publicly available open dataset of speech in over 130 languages for ASR, STT, TTS, and other NLP contexts, created with community participation.
AUDIOSET is an expanding ontology of 632 audio event classes and a collection of 2,084,320 labeled 10-second audio clips taken from YouTube videos. The ontology is structured as a hierarchical graph of event categories, covering a wide range of sounds produced by humans and animals, musical instruments and genres, as well as common environmental sounds. The data is manually annotated, which ensures higher accuracy in sound recognition.
If you haven’t found what you’re looking for here or elsewhere on the internet, you can turn to data collection and labeling specialists such as SHAIP, APPEN, LABELBOX, APACHEHIVE, SUPERANNOTATE, SAMA, or the familiar SCALE by Meta, and use their services for collecting the desired dataset, proper data labeling, or weight adjustment.
And yes, many neural network service providers do use the services of these specialists.
NEURAL NETWORK BENCHMARKS (TESTERS).
Experts in training and deploying neural networks say that the best benchmark is the user’s personal experience.
This cliché truth still holds depth and value. We cannot verify benchmark data, and when we see hallucinations and lies from neural networks during interaction, we lose part of our trust.
The major Q&A forum for developers, Stack Overflow, conducted its annual developer survey in 2025, asking 300 questions to more than 49,000 technical professionals. Developers from the USA, Germany, and India were the top participants.
According to the survey results, 84% of respondents use neural networks in their work. The biggest frustration mentioned by 66% was “AI solutions that are almost correct, but not quite,” which often leads to the second most cited issue: “Debugging AI-generated code takes more time” (45%).
As we can see, programming professionals do not fully trust the outputs of neural networks, yet still use them.
Many benchmarks can also be found on platforms like GITHUB, HUGGINGFACE, Google’s datasets, GOOGLESCHOLAR, or other neural network platforms mentioned earlier. There you can find a lot through convenient search and filtering. Also read Google’s RESEARCH BLOG, where they often describe neural network testing mechanisms.
One of the most popular benchmarks is HUMANITY'S LAST EXAM — a multimodal test covering advanced human knowledge across a wide range of topics. The dataset contains 2,500 complex questions across more than 100 subjects. Over 1,000 subject-matter experts from more than 500 institutions in 50 countries contributed to creating the questions. The benchmark includes multiple-choice and short-answer questions suitable for automated evaluation. The questions are publicly available, although some remain hidden to assess model overfitting.
Benchmarks where users evaluate responses from different neural networks to the same prompt are also popular.
The popular benchmark LMARENA invites users to evaluate models. You enter a prompt, and two neural networks generate answers. For example: “List the most popular neural network benchmarks.” Then assistants A and B provide two responses. You read both and choose the better one, declare a tie, or rate both answers as poor. You can choose specific models or a single one, and also view rankings.
SCIARENA is a similar online platform by the nonprofit Allen Institute for AI, focused on analyzing scientific challenges in artificial intelligence and natural language processing. Users can submit scientific questions and tasks. Two randomly selected models respond, drawing from the ScholarQA system of relevant academic article search. As with other “arenas,” users rate the results. The platform helps researchers test models, compare outputs, and share progress.
HUMANEVAL is a collection of benchmarks that evaluate neural networks through meaningful human interaction, rather than just synthetic metrics. It uses an agent-based infrastructure that enables interaction with models tied to specific platforms, allowing for assessments not available in systems like LMArena. Like those, it features “blind” testing for best answers.
LMSYS is a collection of projects from a research team at UC Berkeley, including open models, datasets, systems, and evaluation tools for large models. It also offers public voting on models, similar to LMArena.
SUPERGLUE is an advanced version of the GLUE benchmark, including a diagnostic dataset and tasks. It helps identify what a model is lacking — such as issues with logical operators, double negatives, rare knowledge, and more. It also allows for the detection of gender or subject bias (e.g., alternating he/she).
SPIDER is a benchmark that tests a neural network's understanding of natural language questions and its ability to generate correct SQL queries for a database. The model doesn't just return text — it constructs a SQL query, executes it, and returns the answer from the database tables. This is one way to get a neural network to answer a question: CoT (Chain-of-Thought + self-correction) — a step-by-step query building process where the model reflects on each step. The model first outlines a schema (which tables and columns to use), drafts a SQL query, selects filters, then clarifies missing details, checks itself, and finally returns the result. The method is precise but resource-intensive, as it requires many queries to the model.
WINOGRANDE is a manually crafted benchmark containing 44,000 challenging tasks designed to evaluate understanding of the core meaning of language sentences. A specially developed algorithm, AfLite, removes tasks that can be solved without reasoning — using only simple statistical word patterns. This allows testing neural networks under truly realistic conditions, eliminating reliance on memorized, frequently encountered data.
MLCOMMONS - an open international consortium that creates standards and tools for objective measurement of performance, quality, and safety of neural network solutions. It offers various testers for neural networks as well as for your devices on which you plan to run neural networks. You can use it with NANOREVIEW, PASSMARK or the universal GEEKBENCH - to compare processors, smartphones, and other gadgets, along with the UX benchmark for application interfaces CrUX.
HELM (Holistic Evaluation of Language Models) is a benchmark from Stanford University for testing language models across various metrics (multitask understanding, safety, extraction of structured information from images, and more).
EVALPLUS is a benchmark suite for evaluating code generation by neural networks. It enables objective comparison of the performance of various LLMs, helps identify weaknesses in existing models, and guides improvement efforts. It serves as a foundation for publications and research in LLMs and programming.
The Claude-code-security-review helps detect vulnerabilities in program code. The tool uses the Claude neural network to analyze code changes for vulnerabilities. According to the developers, it provides intelligent context-aware security analysis of pull requests using the Claude Code tool from Anthropic for deep semantic security analysis.
The interactive platform CIBench is designed for comprehensive evaluation of LLM capabilities when using code interpreters in data science tasks. It includes a benchmark dataset and two evaluation modes. The dataset was created using a cooperative LLM-human approach and simulates a real workflow through sequential interactive IPython sessions. The two evaluation modes test LLM performance with and without human involvement.
The TERMINALBENCH benchmark was created with the participation of researchers from Stanford University and the Laude Institute, founded by Andy Konwinski, co-founder of Perplexity and Databricks. It is a set of tasks and an evaluation system that helps agent developers quantitatively measure the terminal skills of their systems.
The OpenAI GDPVAL benchmark measures model performance on tasks based directly on the real-world knowledge of experienced professionals from 44 different professions and industries, providing a clearer understanding of how models handle economically significant tasks.
The Gödel Test: - a test developed by Israeli researchers Moran Feldman and Amin Karbasi for LLMs, which evaluates a model’s ability to provide correct proofs for very simple, previously unsolved hypotheses.
SUPERGPQA is a rigorous test designed to assess LLM capabilities. It overcomes limitations of existing tests that focus on common fields and overlook diverse, practical professional disciplines. It includes 26,529 questions across 13 categories, 72 knowledge areas, and 285 graduate-level subjects, with at least 50 questions per subject. This ensures accessibility and relevance for various real-world professional scenarios, including expert knowledge often missed by other benchmarks. It covers specialized, rarely tested fields — top models score just above 60%.
VALS is a platform for independent evaluation of large language model (LLM) performance in specific industries such as law, finance, and taxation. It provides open benchmarks that help understand how models handle real-world professional tasks, not just academic tests.
BABILong is a generative benchmark for evaluating the performance of natural language processing models when processing long documents with facts distributed in different places. It consists of 20 tasks designed to assess basic aspects of reasoning. The tasks are generated by simulating a set of characters and objects performing various movements and interacting with each other in different locations. Each interaction is represented as a fact, for example, “Mary went to the office,” and the task is to answer a question using facts from the current simulation, such as “Where is Mary?”. The tasks vary in the number of facts, question complexity, and aspects of reasoning. The authors claim that even models advertised as supporting 128K tokens, such as GPT-4, experience degradation when exceeding 10% of their input capacity. RAG methods do not help, whereas fine-tuning of small-scale models demonstrates that these tasks are solvable.
MMMU is a benchmark designed to evaluate multimodal models in large-scale interdisciplinary tasks that require college-level subject knowledge and deliberate reasoning. It includes 11.5 thousand carefully collected multimodal questions from college exams, tests, and textbooks, covering six main disciplines: arts and design, business, science, healthcare and medicine, humanities and social sciences, as well as technology and engineering.
Safety evaluations hub – a center providing access to the results of OpenAI model safety evaluations. These assessments are included in system cards and are taken into account when making internal decisions about safety and model deployment. They evaluate the models’ tendency to generate harmful content, hallucinations, and much more.
The PERSONA benchmark tests the adaptation of language models to different personalities and value systems. It involves creating synthetic personalities with various individual and demographic traits, as well as a dataset with prompts and feedback. Based on interactions between language models and these personalities, a pluralistic approach to model alignment called Persona Bench is developed. More details can be found here.
DYNABENCH by MLCommons, Inc. is a platform for interactive testing of models, generating challenging tasks to evaluate LLMs, and fostering a community of AI experts and enthusiasts. It offers competitions for testing LLMs and a collaborative environment where participants can study and assess model effectiveness in areas such as bias generation, domain-specific expertise, and more.
VENDINGBENCH is a simulation environment that tests how well neural network models handle a simple yet long-term business scenario: managing a vending machine. The agent must track inventory, place orders, set prices, and pay daily fees — individually simple tasks that over time stretch a model's ability to maintain consistency and make reasoned decisions. An interesting article features an interview with the benchmark's creator and Andon Labs founder, Axel Backlund.
SWE-BENCH VERIFIED is a benchmark from OpenAI and the authors of the original SWE-Bench, featuring 500 selected Python tasks out of 1699 from the old benchmark. OpenAI also offers an interesting benchmark — SimpleQA, which tests language models' ability to answer short questions requiring fact retrieval.
ARC-AGI is a general test by François Chollet, a well-known researcher who created the deep learning library Keras.
GAME-ARENA – a benchmark from Kaggle where leading models from AI labs such as Google, Anthropic, and OpenAI compete in matches streamed live and available for replay. The matches are defined by game environments, systems, and visualizers running on Kaggle’s evaluation infrastructure.
The hallucination model VECTARA is a neural network developed to track hallucinations in language models. It is based on the concept of two parts of text, representing a claim and its verification.
OPENCOMPASS is a Chinese benchmark suite. Its vertical domain evaluations cover key areas such as finance, healthcare, and education. It collaborates with top Chinese universities and tech companies to co-publish authoritative datasets and rankings of vertical evaluations, promoting the creation of a standardized evaluation system for large-scale domain-specific models.
C-EVAL is another Chinese comprehensive benchmark toolkit for evaluating foundational Chinese language models. It consists of 13,948 multiple-choice questions across 52 different subjects and four difficulty levels.
WEBARENA is a standalone, self-hosted web environment for building autonomous neural agents that perform tasks within web interfaces. WebArena generates websites in four popular categories, with functionality and data mimicking real-world counterparts. Agents learn to use maps for route planning, manage e-commerce orders, update website information, and create repositories on GitHub.
VIDEOARENA is a video evaluation platform where the best video is selected from among already generated ones. It also displays rankings of popular video generation tools.
CONTRA is an online benchmark in the format of an arena for testing models that generate images, videos, and program code. You can enter and test queries, then receive the names of the models that executed them.
To find out up to what date data was uploaded into a popular neural network, check EXPLODINGTOPICS. Similar information is available via the neural search site optimizer ALLMO, and even more can be found or added via this GitHub repository.
If you need an automated system for checking programming problem solutions, use the open DMOJ or online services like CODEFORCES, ATCODER, or others.
For advanced testing of your creation, you can turn to professional testers. These include the OpenAI-acquired product testing service STATSIG, the enterprise-oriented LAUNCHDARKLY, HARNESS, which acquired the Software as a Service platform SPLIT, and others.
So now we have met the large community of developers, enthusiasts, researchers, and experts in the field of artificial intelligence.
They all work hard and create products capable of changing our future.
If you want to join them, help, or simply learn to better understand neural networks — you are someone who also wants to change the world for the better.
Join us! Engage on HackerNews, in Reddit and LinkedIn communities.
Perhaps with your help, soon we will add one or more great neural network services to the catalogs.
Humanity is currently only at the threshold of new discoveries, and I am sure that somewhere people are making small steps toward a big leap for humanity into a better future. Perhaps one of these people is you.
Stay calm, mindful, and take care of yourself.
Take the SAID test to once again make sure that AI is not capable of deceiving us.
said-correspondent🌐
You can create a separate thread on the community forum.
Comments