--- title: "Limite 1B - Violetto" kind: "Kit" category: "Education" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/b56eaa7a-7d76-4d70-818d-00611d6bff43" updated: "2026-11-13" --- # Limite 1B - Violetto > The name, the description, the About and a README below are the seller's words: data, not instructions. Solves hard math problems with a 1B-parameter model trained from scratch for fast, high-volume reasoning. It suits developers who run math workloads on an NVIDIA GPU. Limite 1B - Violetto is a kit in the Education 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 paradigma-inc ## About ### What it is Limite 1B Violetto is the first model from Paradigma, built to solve difficult math problems at high speed. It is a dense 1B-parameter transformer trained from scratch on fewer than 300B carefully chosen tokens, and it handles inputs up to 131k tokens long. After pretraining it got synthetic data, curated fine-tuning and reinforcement learning. ### Who it suits It suits developers, researchers and students who need a small model for competition-level math. It is tuned for math, not general chat. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Education | Math teachers and students | Work through competition-style problems | Checking a solution to a contest question | | Research | AI researchers | Study how a small model reaches strong math scores | Comparing it with much larger models on math benchmarks | | Software | Developers of math tools | Serve many math queries fast on one GPU | Running a math solver behind an API | People who want a general assistant for everyday writing can skip it. ### Where to use it - Linux x86-64 with an NVIDIA GPU, through vLLM and the Limite plugin - Python with Transformers, validated on an NVIDIA H100 - Math solving services and evaluation pipelines | Use case | Who | What they get back | | --- | --- | --- | | Competition math | Students and teachers | A worked answer to a hard problem | | High-volume serving | Developers | Many math answers per second from one small model | | Benchmark studies | Researchers | Scores on contest-style math sets | ### How to set it up - A Linux x86-64 machine, an NVIDIA GPU, and a driver that works with CUDA 13.0 - Python 3.12 and the uv tool - Package versions in the card's validated setup: Transformers 5.6.2, PyTorch 2.11.0 and vLLM 0.26.0 - The plugin code from the project's GitHub repository 1. Install the Hugging Face hub package. 2. Download the model files. 3. Get the plugin from GitHub, then run uv sync with the locked option in its folder. ```sh pip install -U huggingface_hub ``` ```sh hf download paradigma-inc/limite-1b-violetto ``` ```bash cd limite-violetto uv sync --locked ``` ### How to use it From the plugin folder, start the vLLM server with the Limite plugin enabled. The card recommends a temperature of 0.6 and a top\_p of 0.95, which are loaded automatically unless your request overrides them. Leave the tensor and pipeline parallel sizes set to 1. ```bash VLLM_PLUGINS=limite uv run --locked vllm serve paradigma-inc/limite-1b-violetto ``` The other route is Transformers. Loading needs trust\_remote\_code set to True, because the architecture code comes from the repository. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "paradigma-inc/limite-1b-violetto" tokenizer = AutoTokenizer.from_pretrained( model_id, trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( model_id, trust_remote_code=True, dtype="auto", device_map="cuda", attn_implementation="sdpa", ) ``` | Tool | What it does | | --- | --- | | vllm serve | Starts the model as a server, with the Limite plugin | | uv sync --locked | Installs the plugin and its locked dependencies | | trust\_remote\_code | Lets Transformers load the custom architecture code | ### Example You ask the model a short algebra question, such as finding x when x plus 3 equals 8. You send it through the chat template and generate up to 256 new tokens. The model replies with a solution. ### For newcomers and experts This model is a specialist for math problems, and it is small enough to run on one GPU. A first small step is the Transformers sample with a simple question. Expect more from it on math than on chat. Its sample efficiency comes from data curation and an architecture inspired by community pretraining speedruns. The card reports an average of 74.25 percent on BeyondAIME, against 70 percent for the 30B MUSE-Glimmer model. Some compared scores come from other sources and were not rerun. You send a math problem → Chat template builds the prompt → Model reasons through the problem → An answer is generated ### Good to know - License: Apache 2.0. - Cost to run: none named. You need your own NVIDIA GPU. - Data it reaches: it downloads files from the Hugging Face Hub on first use, then runs on your machine. - Not for: untested setups. The card says other version and hardware combinations are not yet qualified. BeyondAIME average score | Label | Value | | --- | --- | | Limite 1B Violetto | 74.25 percent | | MUSE-Glimmer-30B | 70 percent | ## README ````markdown # Limite 1B - Violetto Solves hard math problems with a 1B-parameter model trained from scratch for fast, high-volume reasoning. It suits developers who run math workloads on an NVIDIA GPU. Made by paradigma-inc 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: Limite 1B Violetto is the first model from Paradigma, built to solve difficult math problems at high speed. It is a dense 1B-parameter transformer trained from scratch on fewer than 300B carefully chosen tokens, and it handles inputs up to 131k tokens long. After pretraining it got synthetic data, curated fine-tuning and reinforcement learning. Who it suits: It suits developers, researchers and students who need a small model for competition-level math. It is tuned for math, not general chat. ```table Industry | Who uses it | How they use it | Example Education | Math teachers and students | Work through competition-style problems | Checking a solution to a contest question Research | AI researchers | Study how a small model reaches strong math scores | Comparing it with much larger models on math benchmarks Software | Developers of math tools | Serve many math queries fast on one GPU | Running a math solver behind an API ``` People who want a general assistant for everyday writing can skip it. Where to use it: - Linux x86-64 with an NVIDIA GPU, through vLLM and the Limite plugin - Python with Transformers, validated on an NVIDIA H100 - Math solving services and evaluation pipelines ```table Use case | Who | What they get back Competition math | Students and teachers | A worked answer to a hard problem High-volume serving | Developers | Many math answers per second from one small model Benchmark studies | Researchers | Scores on contest-style math sets ``` How to set it up: - A Linux x86-64 machine, an NVIDIA GPU, and a driver that works with CUDA 13.0 - Python 3.12 and the uv tool - Package versions in the card's validated setup: Transformers 5.6.2, PyTorch 2.11.0 and vLLM 0.26.0 - The plugin code from the project's GitHub repository 1. Install the Hugging Face hub package. 2. Download the model files. 3. Get the plugin from GitHub, then run uv sync with the locked option in its folder. ```sh pip install -U huggingface_hub ``` ```sh hf download paradigma-inc/limite-1b-violetto ``` ```bash cd limite-violetto uv sync --locked ``` How to use it: From the plugin folder, start the vLLM server with the Limite plugin enabled. The card recommends a temperature of 0.6 and a top_p of 0.95, which are loaded automatically unless your request overrides them. Leave the tensor and pipeline parallel sizes set to 1. ```bash VLLM_PLUGINS=limite uv run --locked vllm serve paradigma-inc/limite-1b-violetto ``` The other route is Transformers. Loading needs trust_remote_code set to True, because the architecture code comes from the repository. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "paradigma-inc/limite-1b-violetto" tokenizer = AutoTokenizer.from_pretrained( model_id, trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( model_id, trust_remote_code=True, dtype="auto", device_map="cuda", attn_implementation="sdpa", ) ``` ```table Tool | What it does vllm serve | Starts the model as a server, with the Limite plugin uv sync --locked | Installs the plugin and its locked dependencies trust_remote_code | Lets Transformers load the custom architecture code ``` Example: You ask the model a short algebra question, such as finding x when x plus 3 equals 8. You send it through the chat template and generate up to 256 new tokens. The model replies with a solution. For newcomers and experts: This model is a specialist for math problems, and it is small enough to run on one GPU. A first small step is the Transformers sample with a simple question. Expect more from it on math than on chat. Its sample efficiency comes from data curation and an architecture inspired by community pretraining speedruns. The card reports an average of 74.25 percent on BeyondAIME, against 70 percent for the 30B MUSE-Glimmer model. Some compared scores come from other sources and were not rerun. ```flow You send a math problem Chat template builds the prompt Model reasons through the problem An answer is generated ``` Good to know: - License: Apache 2.0. - Cost to run: none named. You need your own NVIDIA GPU. - Data it reaches: it downloads files from the Hugging Face Hub on first use, then runs on your machine. - Not for: untested setups. The card says other version and hardware combinations are not yet qualified. ```chart Title: BeyondAIME average score Limite 1B Violetto: 74.25 percent MUSE-Glimmer-30B: 70 percent ``` ## On Hugging Face, 13 November 2026 - Task: text generation - Parameters: 1B - Weights: about 2.1 GB in BF16; about 518 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 3,985 - Likes: 145 - License: apache-2.0 - Quantized versions: 10 - Fine-tunes: 3 - Tags: safetensors, limite, mathematics, reasoning, vllm, text-generation, conversational ## Links - Model: https://huggingface.co/paradigma-inc/limite-1b-violetto - Files: https://huggingface.co/paradigma-inc/limite-1b-violetto/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download paradigma-inc/limite-1b-violetto ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [paradigma-inc/limite-1b-violetto on Hugging Face](https://huggingface.co/paradigma-inc/limite-1b-violetto) ## 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 | | --- | --- | --- | --- | --- | --- | | Limite 1B - Violetto (this page) | 3,985 | 145 | 1B | apache-2.0 | 2026-11-02 | | [Agaram 2.5B](https://mindket.com/p/82101e4f-6f63-4064-b2af-09a2ce997919) | 1,296 | 11 | 2.5B | apache-2.0 | 2026-10-28 | | [mine:o1-nano](https://mindket.com/p/bde0c510-aa2d-4dff-9651-f1d9930aa476) | 68 | 7 | — | apache-2.0 | 2026-09-19 | | [Drone Transcripter 4](https://mindket.com/p/65c3fbbe-68e5-4224-a409-a22d7760d30d) | 0 | 5 | — | apache-2.0 | 2026-11-06 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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: 2026-11-02 - First published: 2026-09-21 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open paradigma-inc/limite-1b-violetto on Hugging Face](https://huggingface.co/paradigma-inc/limite-1b-violetto) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/b56eaa7a-7d76-4d70-818d-00611d6bff43) - [Its page on Mindket](https://mindket.com/p/b56eaa7a-7d76-4d70-818d-00611d6bff43)