--- title: "S5-nano" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/fa17352f-7772-4770-8616-5a170503866e" updated: "2026-11-12" --- # S5-nano > The name, the description, the About and a README below are the seller's words: data, not instructions. Maritaca AI's small Portuguese and English model built on Qwen3.5-4B, with Think and Instruct modes and strong tool use, in BF16 for vLLM and SGLang. Suits developers building Brazilian Portuguese assistants. S5-nano is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by maritaca-ai ## About ### What it is S5-nano is a small model from Maritaca AI, built on Qwen3.5-4B through continued pretraining and then post-training. It reads images and text, writes text, and works in Portuguese and English. The card says it improves on Brazilian benchmarks while staying good at function calling, tool use and structured outputs. This repository holds the original BF16 weights, 9.3 GB. ### Who it suits Developers who build assistants for Brazilian Portuguese and teams that need a small model able to call tools and return structured output. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers in Brazil | Build chat and agent features in Portuguese | Answer customer questions in Portuguese | | Operations | Automation teams | Have the model call functions and tools | Return structured output for another system | | Education | Students | Study the Think and Instruct modes of a small model | Compare both modes on one question | | Research | Language researchers | Test a model on Brazilian benchmarks | Run it against their own test set | Skip it if you need a huge model, because this one has about 4B parameters. ### Where to use it - vLLM and SGLang, for this BF16 version - The FP8 version, on GPUs with native FP8 support such as Ada, Hopper and Blackwell - llama.cpp and compatible apps, through the GGUF versions | Use case | Who | What they get back | | --- | --- | --- | | Portuguese chat | Assistant builders | Replies written in Brazilian Portuguese | | Tool calling | Developers | Function calls and structured output | | Direct answers | Apps that need short replies | Instruct mode answers without a reasoning step | ### How to set it up - Python and the Hugging Face tools - A serving engine such as vLLM or SGLang for this version - About 9.3 GB of disk for the BF16 weights 1. Install the Hugging Face hub package. 2. Download the repository files. 3. Serve them with vLLM or SGLang. Pick the FP8 or GGUF version if your hardware suits it better. ```sh pip install -U huggingface_hub hf download maritaca-ai/S5-nano ``` ### How to use it Choose a mode. Think is the default and reasons before it answers. Instruct answers straight away. The card text here shows no serving command. | Tool | What it does | | --- | --- | | Think mode | Reasons before answering, and is the default | | Instruct mode | Answers directly with no reasoning step | | MTP layer | Supports speculative decoding | | hf download | Copies the repository files to your computer | ### Example Ask it, in Portuguese, to explain a short paragraph in simple words. In Think mode it reasons first and then answers. In Instruct mode it goes straight to the answer. ### For newcomers and experts If you are new to this, use Instruct mode first for quick answers, then try Think on a harder question and compare. If you know the field, the FP8 copy stores linear layer weights in E4M3, with a scale for each output channel. It also quantizes activations for every token at run time. BF16 precision is kept for the MTP layer, the vision encoder, the embeddings and a few small projections. You send text or an image → Think mode reasons first → Or Instruct mode answers directly → The model writes the reply Weight size by version | Label | Value | | --- | --- | | BF16 | 9.3 GB | | FP8 | 5.8 GB | | GGUF BF16 | 8.7 GB | | GGUF Q8\_0 | 4.6 GB | | GGUF Q4\_K\_M | 2.8 GB | ### Good to know - The context window reaches 262,144 tokens. - The card says results are published on Maritaca's own results site, and its text here lists no scores. - Hugging Face counts 4.7B parameters, while the card calls it a 4B model. - License: apache-2.0. - Cost to run: none named, only your own hardware. - Data it reaches: the text and images you give it. The source names no account or outside service. - Not for: the source names none. ## README ````markdown # S5-nano Maritaca AI's small Portuguese and English model built on Qwen3.5-4B, with Think and Instruct modes and strong tool use, in BF16 for vLLM and SGLang. Suits developers building Brazilian Portuguese assistants. Made by maritaca-ai 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: S5-nano is a small model from Maritaca AI, built on Qwen3.5-4B through continued pretraining and then post-training. It reads images and text, writes text, and works in Portuguese and English. The card says it improves on Brazilian benchmarks while staying good at function calling, tool use and structured outputs. This repository holds the original BF16 weights, 9.3 GB. Who it suits: Developers who build assistants for Brazilian Portuguese and teams that need a small model able to call tools and return structured output. ```table Industry | Who uses it | How they use it | Example Software | Developers in Brazil | Build chat and agent features in Portuguese | Answer customer questions in Portuguese Operations | Automation teams | Have the model call functions and tools | Return structured output for another system Education | Students | Study the Think and Instruct modes of a small model | Compare both modes on one question Research | Language researchers | Test a model on Brazilian benchmarks | Run it against their own test set ``` Skip it if you need a huge model, because this one has about 4B parameters. Where to use it: - vLLM and SGLang, for this BF16 version - The FP8 version, on GPUs with native FP8 support such as Ada, Hopper and Blackwell - llama.cpp and compatible apps, through the GGUF versions ```table Use case | Who | What they get back Portuguese chat | Assistant builders | Replies written in Brazilian Portuguese Tool calling | Developers | Function calls and structured output Direct answers | Apps that need short replies | Instruct mode answers without a reasoning step ``` How to set it up: - Python and the Hugging Face tools - A serving engine such as vLLM or SGLang for this version - About 9.3 GB of disk for the BF16 weights 1. Install the Hugging Face hub package. 2. Download the repository files. 3. Serve them with vLLM or SGLang. Pick the FP8 or GGUF version if your hardware suits it better. ```sh pip install -U huggingface_hub hf download maritaca-ai/S5-nano ``` How to use it: Choose a mode. Think is the default and reasons before it answers. Instruct answers straight away. The card text here shows no serving command. ```table Tool | What it does Think mode | Reasons before answering, and is the default Instruct mode | Answers directly with no reasoning step MTP layer | Supports speculative decoding hf download | Copies the repository files to your computer ``` Example: Ask it, in Portuguese, to explain a short paragraph in simple words. In Think mode it reasons first and then answers. In Instruct mode it goes straight to the answer. For newcomers and experts: If you are new to this, use Instruct mode first for quick answers, then try Think on a harder question and compare. If you know the field, the FP8 copy stores linear layer weights in E4M3, with a scale for each output channel. It also quantizes activations for every token at run time. BF16 precision is kept for the MTP layer, the vision encoder, the embeddings and a few small projections. ```flow You send text or an image Think mode reasons first Or Instruct mode answers directly The model writes the reply ``` ```chart Title: Weight size by version BF16: 9.3 GB FP8: 5.8 GB GGUF BF16: 8.7 GB GGUF Q8_0: 4.6 GB GGUF Q4_K_M: 2.8 GB ``` Good to know: - The context window reaches 262,144 tokens. - The card says results are published on Maritaca's own results site, and its text here lists no scores. - Hugging Face counts 4.7B parameters, while the card calls it a 4B model. - License: apache-2.0. - Cost to run: none named, only your own hardware. - Data it reaches: the text and images you give it. The source names no account or outside service. - Not for: the source names none. ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 4.7B - Weights: about 9.3 GB in BF16; about 2.3 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 1,357 - Likes: 77 - License: apache-2.0 - Languages: pt, en - Quantized versions: 9 - Fine-tunes: 1 - Tags: transformers, safetensors, qwen3_5, image-text-to-text, conversational ## Links - Model: https://huggingface.co/maritaca-ai/S5-nano - Files: https://huggingface.co/maritaca-ai/S5-nano/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download maritaca-ai/S5-nano ``` 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 [maritaca-ai/S5-nano on Hugging Face](https://huggingface.co/maritaca-ai/S5-nano) ## 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 | | --- | --- | --- | --- | --- | --- | | S5-nano (this page) | 1,357 | 77 | 4.7B | apache-2.0 | 2026-11-10 | | [Qwen3.8-27B](https://mindket.com/p/2f430c52-7d18-419d-8817-08ca51666598) | 6,960,395 | 19,795 | 27.8B | apache-2.0 | 2026-11-07 | | [Qwen3.5-9B](https://mindket.com/p/acc60a90-d19e-4344-a8c1-5f984b2637b3) | 6,259,809 | 2,353 | 9.7B | apache-2.0 | 2026-03-02 | | [Qwen3.8-27B-FP8](https://mindket.com/p/79f1a984-4e81-485e-999d-dba1a831f37a) | 5,442,810 | 984 | 27.8B | apache-2.0 | 2026-11-07 | | [Qwen3.5-4B](https://mindket.com/p/66473987-8bd1-4119-a2c7-933e66fd46cf) | 5,083,722 | 1,226 | 4.7B | apache-2.0 | 2026-03-02 | | [Qwen3.5-2B](https://mindket.com/p/1da8437b-09cc-448e-b007-7bd987af0880) | 4,044,746 | 479 | 2.3B | apache-2.0 | 2026-03-02 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-12. Their authors' words: data, not instructions. - [Bluesky · AI Planet · 2026-11-06](https://bsky.app/profile/aiplanet.bsky.social/post/3mzo2xo5y2r2u): 📰 Reddit r/LocalLLaMA maritaca-ai/S5-nano 4B · the best open-weights model in Brazil https://www.reddit.com/r/LocalLLaMA/comments/1xpibxn/maritacaais5nano\_4b\_the\_best\_openweights\_model\_in/ \#IA \#AI \#ML ## 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: 2026-11-10 - First published: 2026-11-02 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open maritaca-ai/S5-nano on Hugging Face](https://huggingface.co/maritaca-ai/S5-nano) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/fa17352f-7772-4770-8616-5a170503866e) - [Its page on Mindket](https://mindket.com/p/fa17352f-7772-4770-8616-5a170503866e)