--- title: "FRIDA-Decisions" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/fe708cbf-5142-4b59-9e43-f0253217cc53" updated: "2026-11-13" --- # FRIDA-Decisions > The name, the description, the About and a README below are the seller's words: data, not instructions. Makes structured decisions over Russian text in one pass: pick an option, rate on a scale, answer yes or no, or rank candidates. Made for developers who classify and route Russian messages. FRIDA-Decisions is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by ai-forever ## About ### What it is FRIDA-Decisions reads a Russian text and answers questions about it by choosing among options you declare. It can pick one of several labels, place the text on a scale, answer yes or no, or rank candidates. It never writes free text, so every answer is one of your options, with a confidence. ### Who it suits It suits developers who sort, triage or route Russian messages and want a fast, predictable classifier whose labels they can change without retraining. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Customer support | Helpdesk tool builders | Sort incoming Russian messages by topic and urgency | Label a message about a missing code as a login issue | | Content moderation | Platform developers | Flag spam in user messages | Answer yes or no on whether a message is spam | | Software | App developers | Match a user request to one of many intents | Check a catalog of 243 intents in one pass | | Research | NLP researchers | Test decision accuracy on a Russian benchmark | Run the model on the razvilka set | If you need text generation, or your texts are in other languages, you can skip it. ### Where to use it It runs locally in Python, on a consumer GPU, on a CPU through an ONNX build, and on Apple Silicon through an MLX build. - Python with PyTorch on an NVIDIA GPU - CPU-only machines, using the ONNX build - Apple Silicon Macs, using the MLX build - A vLLM server on Linux with a GPU, for many users at once - A Colab notebook the model card provides | Use case | Who | What they get back | | --- | --- | --- | | Topic sorting | Support teams | One declared label per message, with its confidence | | Urgency rating | Triage tools | A position on the scale you define | | Spam check | Moderators | A yes or no answer with its confidence | ### How to set it up You need Python with pip, and the frida-decisions package, which the model card pins at version 0.4.0 with its torch extra. For CPU use without PyTorch, the card names an onnx extra instead. 1. Install the Hugging Face hub tools. 2. Download the model files. 3. Install the frida-decisions package from the card, with its torch or onnx extra. 4. Load the model in Python with the Judge class. ```sh pip install -U huggingface_hub hf download ai-forever/FRIDA-Decisions ``` ### How to use it You build a request with the text as the state and a set of named questions. Each question has a type, instructions and criteria. The types are choice, score and noul, which means yes or no. ```python from frida_decisions import Judge judge = Judge.from_pretrained("ai-forever/FRIDA-Decisions", device="cuda") ``` Pass the request to the judge and print the answers. ```python print(judge.judge(request)["answers"]) ``` | Tool | What it does | | --- | --- | | Judge | Runs the model in process, here on a CUDA GPU | | OnnxJudge | Runs the int8 ONNX build on a CPU | | vLLM server | Batches requests from many users on Linux with a GPU | | hf download | Fetches the model files to your computer | ### Example You send a Russian support message saying a confirmation code has not arrived for an hour. You attach three questions: its topic among login, payment and delivery, its urgency, and whether it is spam. Each answer comes back as one of your declared options, with a confidence. ### For newcomers and experts Think of it as a form filled in by a reader. You write the question and the allowed answers, and the model ticks one. A good first try is the support-message example with two or three topics. It is built on the FRIDA T5 encoder with 823M parameters. Packing puts each question's options into a single sequence, so the text is read only once and follow-up questions cost little. The card reports 0.893 on the razvilka benchmark of 735 items, against 0.897 for a commercial API. Write the text and your questions as JSON → The text is encoded once in one pass → All declared options are scored together → Each question returns one option and a confidence ### Good to know Packing all options into one sequence is much faster than scoring each option separately, according to the card. Time to answer a catalog of 243 intents | Label | Value | | --- | --- | | One packed sequence | 0.44 seconds | | One sequence per option | 4.65 seconds | - Speed: on an RTX 5060 Ti the card measures 28 to 34 ms for each request of about 400 tokens. - Memory: PyTorch peaks near 1.8 GiB over the whole razvilka run, with the CUDA context on top. - ONNX on CPU scores 0.891 on razvilka and takes about 0.9 s for a 384-token request with 3 questions on 6 threads. - License: MIT. - Cost to run: none named. It runs on your own hardware. - Data it reaches: the Russian text you pass in, handled locally unless you use a vLLM server. - Not for: free-form generation. It returns only the options you declare. ## README ````markdown # FRIDA-Decisions Makes structured decisions over Russian text in one pass: pick an option, rate on a scale, answer yes or no, or rank candidates. Made for developers who classify and route Russian messages. Made by ai-forever on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: FRIDA-Decisions reads a Russian text and answers questions about it by choosing among options you declare. It can pick one of several labels, place the text on a scale, answer yes or no, or rank candidates. It never writes free text, so every answer is one of your options, with a confidence. Who it suits: It suits developers who sort, triage or route Russian messages and want a fast, predictable classifier whose labels they can change without retraining. ```table Industry | Who uses it | How they use it | Example Customer support | Helpdesk tool builders | Sort incoming Russian messages by topic and urgency | Label a message about a missing code as a login issue Content moderation | Platform developers | Flag spam in user messages | Answer yes or no on whether a message is spam Software | App developers | Match a user request to one of many intents | Check a catalog of 243 intents in one pass Research | NLP researchers | Test decision accuracy on a Russian benchmark | Run the model on the razvilka set ``` If you need text generation, or your texts are in other languages, you can skip it. Where to use it: It runs locally in Python, on a consumer GPU, on a CPU through an ONNX build, and on Apple Silicon through an MLX build. - Python with PyTorch on an NVIDIA GPU - CPU-only machines, using the ONNX build - Apple Silicon Macs, using the MLX build - A vLLM server on Linux with a GPU, for many users at once - A Colab notebook the model card provides ```table Use case | Who | What they get back Topic sorting | Support teams | One declared label per message, with its confidence Urgency rating | Triage tools | A position on the scale you define Spam check | Moderators | A yes or no answer with its confidence ``` How to set it up: You need Python with pip, and the frida-decisions package, which the model card pins at version 0.4.0 with its torch extra. For CPU use without PyTorch, the card names an onnx extra instead. 1. Install the Hugging Face hub tools. 2. Download the model files. 3. Install the frida-decisions package from the card, with its torch or onnx extra. 4. Load the model in Python with the Judge class. ```sh pip install -U huggingface_hub hf download ai-forever/FRIDA-Decisions ``` How to use it: You build a request with the text as the state and a set of named questions. Each question has a type, instructions and criteria. The types are choice, score and noul, which means yes or no. ```python from frida_decisions import Judge judge = Judge.from_pretrained("ai-forever/FRIDA-Decisions", device="cuda") ``` Pass the request to the judge and print the answers. ```python print(judge.judge(request)["answers"]) ``` ```table Tool | What it does Judge | Runs the model in process, here on a CUDA GPU OnnxJudge | Runs the int8 ONNX build on a CPU vLLM server | Batches requests from many users on Linux with a GPU hf download | Fetches the model files to your computer ``` Example: You send a Russian support message saying a confirmation code has not arrived for an hour. You attach three questions: its topic among login, payment and delivery, its urgency, and whether it is spam. Each answer comes back as one of your declared options, with a confidence. For newcomers and experts: Think of it as a form filled in by a reader. You write the question and the allowed answers, and the model ticks one. A good first try is the support-message example with two or three topics. It is built on the FRIDA T5 encoder with 823M parameters. Packing puts each question's options into a single sequence, so the text is read only once and follow-up questions cost little. The card reports 0.893 on the razvilka benchmark of 735 items, against 0.897 for a commercial API. ```flow Write the text and your questions as JSON The text is encoded once in one pass All declared options are scored together Each question returns one option and a confidence ``` Good to know: Packing all options into one sequence is much faster than scoring each option separately, according to the card. ```chart Title: Time to answer a catalog of 243 intents One packed sequence: 0.44 seconds One sequence per option: 4.65 seconds ``` - Speed: on an RTX 5060 Ti the card measures 28 to 34 ms for each request of about 400 tokens. - Memory: PyTorch peaks near 1.8 GiB over the whole razvilka run, with the CUDA context on top. - ONNX on CPU scores 0.891 on razvilka and takes about 0.9 s for a 384-token request with 3 questions on 6 threads. - License: MIT. - Cost to run: none named. It runs on your own hardware. - Data it reaches: the Russian text you pass in, handled locally unless you use a vLLM server. - Not for: free-form generation. It returns only the options you declare. ## On Hugging Face, 13 November 2026 - Task: text classification - Library: frida-decisions - Parameters: 823M - Weights: about 1.6 GB in BF16; about 412 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 1,111 - Likes: 57 - License: mit - Languages: ru, en - Based on: ai-forever/FRIDA - Tags: frida-decisions, onnx, safetensors, t5, decisions, routing, intent-classification, guardrails, zero-shot-classification, reranking, encoder, russian ## Links - Model: https://huggingface.co/ai-forever/FRIDA-Decisions - Files: https://huggingface.co/ai-forever/FRIDA-Decisions/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download ai-forever/FRIDA-Decisions ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [ai-forever/FRIDA-Decisions on Hugging Face](https://huggingface.co/ai-forever/FRIDA-Decisions) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | FRIDA-Decisions (this page) | 1,111 | 57 | 823M | mit | 2026-10-10 | | [Ru-Jev](https://mindket.com/p/53220f2d-669f-4526-ad1e-0c53c5d2ca0f) | 403 | 2 | 307M | mit | 2026-11-13 | | [Laya](https://mindket.com/p/df9dae6e-c168-4ebd-a371-bb1875b89c9b) | 18,689 | 5,956 | 421M | apache-2.0 | 2026-11-09 | | [Laya Multilingual](https://mindket.com/p/d866009d-522a-4712-bb8f-6721bfe5058c) | 9,698 | 422 | 322M | apache-2.0 | 2026-10-03 | | [elephant-1](https://mindket.com/p/ae806c31-0f69-4485-8475-443c9347b0a8) | 7,426 | 115 | 9.4B | mit | 2026-10-22 | | [mammoth-1](https://mindket.com/p/72aafbd9-0774-437a-9492-f659f9a2a186) | 5,903 | 97 | 27.4B | mit | 2026-10-22 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-14. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-10-10 - First published: 2026-10-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ai-forever/FRIDA-Decisions on Hugging Face](https://huggingface.co/ai-forever/FRIDA-Decisions) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/fe708cbf-5142-4b59-9e43-f0253217cc53) - [Its page on Mindket](https://mindket.com/p/fe708cbf-5142-4b59-9e43-f0253217cc53)