--- title: "S5-nano-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/d6dae919-5103-4dd7-aa2a-be6b9f617bf1" updated: "2026-11-12" --- # S5-nano-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Runs Maritaca AI's S5-nano, a 4B model tuned for Brazilian Portuguese with function calling and a think mode, from GGUF files of 2.8 to 8.7 GB for llama.cpp and compatible apps. S5-nano-GGUF 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-GGUF holds GGUF copies of S5-nano, a 4B-parameter model from Maritaca AI. Maritaca built it on Qwen3.5-4B with more pretraining and post-training, and the card says it improves a lot on Brazilian benchmarks. The repository offers three sizes for llama.cpp and compatible apps: BF16 at 8.7 GB, Q8\_0 at 4.6 GB and Q4\_K\_M at 2.8 GB. ### Who it suits People who want a small model that works well in Brazilian Portuguese and handles function calling, tool use and structured output on their own machine. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software development | Developers in Brazil | Add a local Portuguese-language model to an app | A chat feature that answers in Portuguese | | Agent building | Agent developers | Use function calling and structured output | An agent that calls a tool with typed fields | | Education | Teachers and students | Practice with a model that reads Brazilian text | Questions answered in Brazilian Portuguese | | Research | Language-model researchers | Compare small quantized models | Q4\_K\_M against Q8\_0 | If you want the GPU-ready BF16 or FP8 weights for vLLM or SGLang, you can skip this repository, because they live in the original repositories. ### Where to use it - llama.cpp and apps that load GGUF files - Windows, macOS or Linux, after you fetch the files with Python - vLLM and SGLang, but through the original weights rather than this repository | Use case | Who | What they get back | | --- | --- | --- | | Local chat | Individuals | Answers in Portuguese or English from a small model | | Tool use | Agent builders | Function calls and structured outputs | | Long documents | Analysts | A model with a context window of up to 262,144 tokens | ### How to set it up - Python and pip - Disk space for the file you pick, from 2.8 GB to 8.7 GB - llama.cpp or another app that loads GGUF files 1. Install the Hugging Face hub tools. 2. Download the model files. 3. Open one GGUF file in llama.cpp or your app, following the model card. ```sh pip install -U huggingface_hub hf download maritaca-ai/S5-nano-GGUF ``` ### How to use it Load a file in llama.cpp and chat. The model has two modes. Think reasons before it answers and is the default. Instruct answers directly with no reasoning step. ### Example You ask a question in Brazilian Portuguese with the model in Think mode. It reasons first and then answers. In Instruct mode, the same question gets a direct reply with no reasoning step. ### For newcomers and experts If you are new to GGUF, start with Q4\_K\_M, the smallest file, and ask a short question in Portuguese. Switch to Q8\_0 if you want closer quality and have the memory. The weights include a multi-token prediction layer that can be used for speculative decoding. The context window goes up to 262,144 tokens. The card reports evaluation results for the original BF16 checkpoint, not for these GGUF files, so quantized quality may differ. Pick a GGUF size for your memory → Load it in llama.cpp → Choose Think or Instruct mode → Ask in Portuguese or English ### Good to know - Evaluation: the card's results are for the original BF16 weights, not these GGUF files - Other formats: vLLM and SGLang users need the original BF16 or FP8 repositories Size of each S5-nano version in GB | Label | Value | | --- | --- | | GGUF Q4\_K\_M | 2.8 GB | | GGUF Q8\_0 | 4.6 GB | | FP8 | 5.8 GB | | GGUF BF16 | 8.7 GB | | Original BF16 | 9.3 GB | - License: apache-2.0 - Cost to run: no API key or paid service is named, only your own computer - Data it reaches: downloading fetches model files, and chats run locally in your app - Not for: vLLM or SGLang serving from this repository, since the card points those users to the original weights ## README ````markdown # S5-nano-GGUF Runs Maritaca AI's S5-nano, a 4B model tuned for Brazilian Portuguese with function calling and a think mode, from GGUF files of 2.8 to 8.7 GB for llama.cpp and compatible apps. 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-GGUF holds GGUF copies of S5-nano, a 4B-parameter model from Maritaca AI. Maritaca built it on Qwen3.5-4B with more pretraining and post-training, and the card says it improves a lot on Brazilian benchmarks. The repository offers three sizes for llama.cpp and compatible apps: BF16 at 8.7 GB, Q8_0 at 4.6 GB and Q4_K_M at 2.8 GB. Who it suits: People who want a small model that works well in Brazilian Portuguese and handles function calling, tool use and structured output on their own machine. ```table Industry | Who uses it | How they use it | Example Software development | Developers in Brazil | Add a local Portuguese-language model to an app | A chat feature that answers in Portuguese Agent building | Agent developers | Use function calling and structured output | An agent that calls a tool with typed fields Education | Teachers and students | Practice with a model that reads Brazilian text | Questions answered in Brazilian Portuguese Research | Language-model researchers | Compare small quantized models | Q4_K_M against Q8_0 ``` If you want the GPU-ready BF16 or FP8 weights for vLLM or SGLang, you can skip this repository, because they live in the original repositories. Where to use it: - llama.cpp and apps that load GGUF files - Windows, macOS or Linux, after you fetch the files with Python - vLLM and SGLang, but through the original weights rather than this repository ```table Use case | Who | What they get back Local chat | Individuals | Answers in Portuguese or English from a small model Tool use | Agent builders | Function calls and structured outputs Long documents | Analysts | A model with a context window of up to 262,144 tokens ``` How to set it up: - Python and pip - Disk space for the file you pick, from 2.8 GB to 8.7 GB - llama.cpp or another app that loads GGUF files 1. Install the Hugging Face hub tools. 2. Download the model files. 3. Open one GGUF file in llama.cpp or your app, following the model card. ```sh pip install -U huggingface_hub hf download maritaca-ai/S5-nano-GGUF ``` How to use it: Load a file in llama.cpp and chat. The model has two modes. Think reasons before it answers and is the default. Instruct answers directly with no reasoning step. Example: You ask a question in Brazilian Portuguese with the model in Think mode. It reasons first and then answers. In Instruct mode, the same question gets a direct reply with no reasoning step. For newcomers and experts: If you are new to GGUF, start with Q4_K_M, the smallest file, and ask a short question in Portuguese. Switch to Q8_0 if you want closer quality and have the memory. The weights include a multi-token prediction layer that can be used for speculative decoding. The context window goes up to 262,144 tokens. The card reports evaluation results for the original BF16 checkpoint, not for these GGUF files, so quantized quality may differ. ```flow Pick a GGUF size for your memory Load it in llama.cpp Choose Think or Instruct mode Ask in Portuguese or English ``` Good to know: - Evaluation: the card's results are for the original BF16 weights, not these GGUF files - Other formats: vLLM and SGLang users need the original BF16 or FP8 repositories ```chart Title: Size of each S5-nano version in GB GGUF Q4_K_M: 2.8 GB GGUF Q8_0: 4.6 GB FP8: 5.8 GB GGUF BF16: 8.7 GB Original BF16: 9.3 GB ``` - License: apache-2.0 - Cost to run: no API key or paid service is named, only your own computer - Data it reaches: downloading fetches model files, and chats run locally in your app - Not for: vLLM or SGLang serving from this repository, since the card points those users to the original weights ## On Hugging Face, 13 November 2026 - Library: gguf - Parameters: 4.3B - Weights: about 2.2 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 3,421 - Likes: 14 - License: apache-2.0 - Languages: pt, en - Based on: maritaca-ai/S5-nano - Tags: gguf, llama.cpp, conversational ## Links - Model: https://huggingface.co/maritaca-ai/S5-nano-GGUF - Files: https://huggingface.co/maritaca-ai/S5-nano-GGUF/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-GGUF ``` 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-GGUF on Hugging Face](https://huggingface.co/maritaca-ai/S5-nano-GGUF) ## 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-GGUF (this page) | 3,421 | 14 | 4.3B | apache-2.0 | 2026-11-10 | | [Qwen3.8-35B-A3B-Distill-GGUF](https://mindket.com/p/d707b0c8-89b8-4c4f-8d49-2ff67de613c7) | 1,337,097 | 277 | 35.5B | apache-2.0 | 2026-09-16 | | [Tess-4.5-27B GGUF](https://mindket.com/p/2c9f4a01-ed9d-4dd7-94a5-94a5fe30c6a8) | 1,095,620 | 65 | 26.9B | apache-2.0 | 2026-11-08 | | [Sharp-Spark-X2.5-4B-GGUF](https://mindket.com/p/d4d90f81-4886-4de3-ab95-d64c344a708c) | 875,346 | 164 | 4.1B | apache-2.0 | 2026-11-04 | | [Qwen3.8-4B-Distill-GGUF](https://mindket.com/p/ef3614cf-441f-4388-b81f-7b72b3e7ed30) | 570,974 | 208 | 4.3B | apache-2.0 | 2026-08-16 | | [Qwen3.8-9B-Distill-GGUF](https://mindket.com/p/cfc3041d-4b8a-4012-b704-ce834019b236) | 537,695 | 356 | 9.2B | apache-2.0 | 2026-08-16 | 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: 2026-11-10 - 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-GGUF on Hugging Face](https://huggingface.co/maritaca-ai/S5-nano-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/d6dae919-5103-4dd7-aa2a-be6b9f617bf1) - [Its page on Mindket](https://mindket.com/p/d6dae919-5103-4dd7-aa2a-be6b9f617bf1)