--- title: "Zabibu Fikra Model 2 (4B)" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/eb7843c5-0089-48df-9731-9741e52ee900" updated: "2026-11-13" --- # Zabibu Fikra Model 2 (4B) > The name, the description, the About and a README below are the seller's words: data, not instructions. Answers in Swahili and English from a 4B model made to run privately on your own device, offline. Suits on-device assistants that need African context, such as Kenyan data. Zabibu Fikra Model 2 (4B) 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 zabibutechnika ## About ### What it is Zabibu Fikra Model 2 (4B) is a 4 billion parameter chat model from Zabibu Technika in Nairobi. It is a fine-tune of their fikra-v2-e3 checkpoint, which comes from Qwen3.5-4B. It is built for private, offline use on your own device, with Swahili and English and African context in mind. ### Who it suits People who want a small assistant that works without a network, and teams who need Swahili and English in one model. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | App developers | Build an assistant that runs on the user's device | A chat feature that answers in Swahili or English with no server call | | Edge and air-gapped IT | Deployment teams | Run the model where the network is off by design | An office machine with no internet access | | Local products | Product teams | Handle users who switch between Swahili and English mid-sentence | A support helper for a Kenyan app | | Research | NLP researchers | Compare a small model on African context cases | Try the cases the card lists, such as culture and local knowledge | You can skip it if you need a large hosted model or a language outside Swahili and English, since the card only lists those two. ### Where to use it The Safetensors weights load in the transformers library, and the GGUF builds open in desktop runtimes. - Python with the transformers library - llama.cpp - Ollama - LM Studio - Any other GGUF-compatible runtime on Windows, macOS or Linux | Use case | Who | What they get back | | --- | --- | --- | | Private on-device assistant | App developers | Replies without any external call at inference | | Swahili and English code-switching | Product teams | One model that follows both languages | | Edge deployment | IT teams | A small model that runs on CPU in its Q4\_K\_M build | ### How to set it up You need Python with pip to fetch the files, and disk space for the weights, about 8.4 GB in F16. - Python with pip - About 8.4 GB of disk space for the F16 weights, or about 2.1 GB at 4-bit as an estimate - A runtime of your choice: transformers, llama.cpp, Ollama or LM Studio 1. Install the Hugging Face download tool. 2. Download the model files to your computer. 3. For the GGUF build, take it from the maker's companion GGUF repository instead. 4. Open the files in your runtime. ```bash pip install -U huggingface_hub hf download zabibutechnika/Fikra-Model-2-4B ``` ### How to use it Load the weights in transformers, or open a GGUF build in llama.cpp, Ollama or LM Studio, then chat in Swahili or English. The card says the system prompt and deployment design assume no network at inference. | Tool | What it does | | --- | --- | | transformers | Loads the F16 Safetensors weights | | llama.cpp, Ollama, LM Studio | Open the GGUF builds | | Q4\_K\_M | The GGUF size that runs on CPU | | F16 | The GGUF reference copy | ### Example Ask it in Swahili who it is. The card's test suite has four Swahili identity cases and reports all four passed, so expect a Swahili reply about its identity. ### For newcomers and experts A language model reads your text and writes a reply. This one is small enough to run on your own computer, and the card says it needs no external calls to answer. A first small step is to download the GGUF build, open it in LM Studio and type a greeting in Swahili. For experts, this is an instruction-tuned fine-tune of fikra-v2-e3, itself derived from Qwen3.5-4B. Most of the MMLU gain over the 2B Fikra model comes from the larger base, not from Fikra training, and the card says so. The Fikra-specific gain is measured on its own 115-case release suite, 2.6 points over the e3 checkpoint. The trade-off is size against evidence, because the public benchmark runs used only 10 samples per task. Download the weights or a GGUF build → Open them in transformers or a GGUF runtime → Send a Swahili or English prompt → The model replies on your device, offline ### Good to know The release suite results below are the card's own numbers. - Sample size: The MMLU and HellaSwag runs used a limit of 10 samples per task, so read them loosely. - Not size-normalized: The card says the MMLU gain over the 2B model mostly comes from the bigger base. - Claims it declines: The card does not call the model first, best in Africa or state of the art. Fikra release suite results | Label | Value | | --- | --- | | Auto-checked suite | 91.3% | | Held-out paraphrases | 90.0% | | Earlier e3 checkpoint | 88.7% | - License: Apache 2.0, and the card says the base model's license is set by Qwen3.5-4B. - Cost to run: none named, the source lists no API key, account or paid service. - Data it reaches: nothing beyond what you type in, since the design assumes no network at inference. - Not for: claims of being the first African language model, the best in Africa or state of the art, which the card says it does not make. ## README ````markdown # Zabibu Fikra Model 2 (4B) Answers in Swahili and English from a 4B model made to run privately on your own device, offline. Suits on-device assistants that need African context, such as Kenyan data. Made by zabibutechnika 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: Zabibu Fikra Model 2 (4B) is a 4 billion parameter chat model from Zabibu Technika in Nairobi. It is a fine-tune of their fikra-v2-e3 checkpoint, which comes from Qwen3.5-4B. It is built for private, offline use on your own device, with Swahili and English and African context in mind. Who it suits: People who want a small assistant that works without a network, and teams who need Swahili and English in one model. ```table Industry | Who uses it | How they use it | Example Software | App developers | Build an assistant that runs on the user's device | A chat feature that answers in Swahili or English with no server call Edge and air-gapped IT | Deployment teams | Run the model where the network is off by design | An office machine with no internet access Local products | Product teams | Handle users who switch between Swahili and English mid-sentence | A support helper for a Kenyan app Research | NLP researchers | Compare a small model on African context cases | Try the cases the card lists, such as culture and local knowledge ``` You can skip it if you need a large hosted model or a language outside Swahili and English, since the card only lists those two. Where to use it: The Safetensors weights load in the transformers library, and the GGUF builds open in desktop runtimes. - Python with the transformers library - llama.cpp - Ollama - LM Studio - Any other GGUF-compatible runtime on Windows, macOS or Linux ```table Use case | Who | What they get back Private on-device assistant | App developers | Replies without any external call at inference Swahili and English code-switching | Product teams | One model that follows both languages Edge deployment | IT teams | A small model that runs on CPU in its Q4_K_M build ``` How to set it up: You need Python with pip to fetch the files, and disk space for the weights, about 8.4 GB in F16. - Python with pip - About 8.4 GB of disk space for the F16 weights, or about 2.1 GB at 4-bit as an estimate - A runtime of your choice: transformers, llama.cpp, Ollama or LM Studio 1. Install the Hugging Face download tool. 2. Download the model files to your computer. 3. For the GGUF build, take it from the maker's companion GGUF repository instead. 4. Open the files in your runtime. ```bash pip install -U huggingface_hub hf download zabibutechnika/Fikra-Model-2-4B ``` How to use it: Load the weights in transformers, or open a GGUF build in llama.cpp, Ollama or LM Studio, then chat in Swahili or English. The card says the system prompt and deployment design assume no network at inference. ```table Tool | What it does transformers | Loads the F16 Safetensors weights llama.cpp, Ollama, LM Studio | Open the GGUF builds Q4_K_M | The GGUF size that runs on CPU F16 | The GGUF reference copy ``` Example: Ask it in Swahili who it is. The card's test suite has four Swahili identity cases and reports all four passed, so expect a Swahili reply about its identity. For newcomers and experts: A language model reads your text and writes a reply. This one is small enough to run on your own computer, and the card says it needs no external calls to answer. A first small step is to download the GGUF build, open it in LM Studio and type a greeting in Swahili. For experts, this is an instruction-tuned fine-tune of fikra-v2-e3, itself derived from Qwen3.5-4B. Most of the MMLU gain over the 2B Fikra model comes from the larger base, not from Fikra training, and the card says so. The Fikra-specific gain is measured on its own 115-case release suite, 2.6 points over the e3 checkpoint. The trade-off is size against evidence, because the public benchmark runs used only 10 samples per task. ```flow Download the weights or a GGUF build Open them in transformers or a GGUF runtime Send a Swahili or English prompt The model replies on your device, offline ``` Good to know: The release suite results below are the card's own numbers. - Sample size: The MMLU and HellaSwag runs used a limit of 10 samples per task, so read them loosely. - Not size-normalized: The card says the MMLU gain over the 2B model mostly comes from the bigger base. - Claims it declines: The card does not call the model first, best in Africa or state of the art. ```chart Title: Fikra release suite results Auto-checked suite: 91.3% Held-out paraphrases: 90.0% Earlier e3 checkpoint: 88.7% ``` - License: Apache 2.0, and the card says the base model's license is set by Qwen3.5-4B. - Cost to run: none named, the source lists no API key, account or paid service. - Data it reaches: nothing beyond what you type in, since the design assumes no network at inference. - Not for: claims of being the first African language model, the best in Africa or state of the art, which the card says it does not make. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 4.2B - Weights: about 8.4 GB in F16; about 2.1 GB at 4-bit (estimate) - Formats: Safetensors, GGUF - Downloads in the last 30 days: 682 - Likes: 3 - License: apache-2.0 - Languages: en, sw - Based on: zabibutechnika/fikra-v2-e3 - Quantized versions: 3 - Tags: transformers, safetensors, gguf, qwen3_5_text, text-generation, africa, kenya, swahili, on-device, offline-first, fikra, zabibu-technika ## Links - Model: https://huggingface.co/zabibutechnika/Fikra-Model-2-4B - Files: https://huggingface.co/zabibutechnika/Fikra-Model-2-4B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download zabibutechnika/Fikra-Model-2-4B ``` 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 [zabibutechnika/Fikra-Model-2-4B on Hugging Face](https://huggingface.co/zabibutechnika/Fikra-Model-2-4B) ## 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 | | --- | --- | --- | --- | --- | --- | | Zabibu Fikra Model 2 (4B) (this page) | 682 | 3 | 4.2B | apache-2.0 | 2026-11-06 | | [Mica v0.1 4B](https://mindket.com/p/55daa34c-73de-47b6-a176-900851a69bf1) | 3,436 | 52 | 4.2B | apache-2.0 | 2026-09-28 | | [Qwen3.5-4B-3D-Html](https://mindket.com/p/1d50eb48-06b7-44ab-9107-6e91729ba5b6) | 1,735 | 5 | 4.2B | apache-2.0 | 2026-11-11 | | [NeoHorse-1-9B](https://mindket.com/p/95b26668-372e-4030-bfa8-779ae38d878f) | 1,548 | 1,051 | 9B | apache-2.0 | 2026-09-10 | | [Qwen3.5-4B Qwenputer Filesystem](https://mindket.com/p/d7c3dc02-1c93-4ac4-93ac-d53e6cfce431) | 1,016 | 15 | 11.1B | apache-2.0 | 2026-10-22 | | [hs-decide-1-flash](https://mindket.com/p/6af342ec-c349-4bbd-95ad-e2f3ef1740b4) | 858 | 20 | 752M | apache-2.0 | 2026-11-09 | 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: 2026-11-06 - First published: 2026-11-06 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open zabibutechnika/Fikra-Model-2-4B on Hugging Face](https://huggingface.co/zabibutechnika/Fikra-Model-2-4B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/eb7843c5-0089-48df-9731-9741e52ee900) - [Its page on Mindket](https://mindket.com/p/eb7843c5-0089-48df-9731-9741e52ee900)