--- title: "Kolibri-1" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/dd5e0e4c-3802-49b1-9d73-51a8833393f7" updated: "2026-11-13" --- # Kolibri-1 > The name, the description, the About and a README below are the seller's words: data, not instructions. Reasons and calls tools in German and English, with a context window of more than a million tokens. An open 78B mixture-of-experts model from Aleph Alpha that activates only 3.46B parameters per token. Kolibri-1 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 Aleph-Alpha ## About ### What it is Kolibri-1 is a reasoning model from Aleph Alpha, built as a mixture of experts and focused on German and English. It has an explicit reasoning mode and supports tool calling. Only 3.46B of its 78B parameters work on each token, which keeps the cost of serving low. ### Who it suits Teams that need an open model for German and English work, such as assistants, document processing and agents, and that can host a large model. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Build coding and agent features on an open model | An assistant that calls tools in a workflow | | Public sector | IT teams | Host a model on their own servers for German-language tasks | An internal question-answering service | | Legal and finance | Analysts | Process long documents in German or English | Extracting fields from a long contract | | Research | ML researchers | Study a mixture-of-experts model with a long context | A test of retrieval over extra-long inputs | If you only need a small model for a laptop, you can skip it, since it needs about 78 GB of memory. ### Where to use it - Windows, macOS or Linux with Python, for downloading the files - Python programs that use the transformers library - Servers with large accelerators, as the card's hardware list describes | Use case | Who | What they get back | | --- | --- | --- | | Multi-step reasoning | Developers | Worked answers from the explicit reasoning mode | | Retrieval-augmented generation | Search and support teams | Answers grounded in documents you supply | | Agentic tool calling | Agent builders | Calls to your own tools from the model | | Long documents | Analysts | Processing of contexts up to 1,048,576 tokens | ### How to set it up - Python, to run the Hugging Face download tool - About 78 GB of memory and disk for the FP8 weights - Hardware from the card's list, such as 2 A100 80 GB cards, 2 H100 SXM5 cards, or one H200, B200 or B300 1. Install the Hugging Face hub tools with pip. 2. Download the model files with the hf command. 3. Follow the model card to serve the model on suitable hardware. ```bash pip install -U huggingface_hub hf download Aleph-Alpha/Kolibri-1 ``` ### How to use it You send German or English text to the served model, turn on its reasoning mode when a task needs several steps, and let it call tools when you define them. The card recommends contexts of at most 262,144 tokens for speed and hard tasks. ### Example You give the model a long German report and ask for a summary in English with the key figures listed. With reasoning on, it works through the report and returns the summary. ### For newcomers and experts Start by reading the model card to check your hardware, then try short German and English prompts before long documents. Keep early tests well under the recommended context size. The model is a 50-layer transformer with 384 experts per layer, one shared and six routed, and its attention mixes sliding-window and grouped-query layers at a 4 to 1 ratio. Most attention layers look at nearby text, which keeps long contexts affordable. The trade is memory, since the whole model must be loaded. Send German or English text → Router picks 6 of 384 experts → Reasoning mode works the task → Answer or tool call comes back ### Good to know - License: Apache 2.0 - Cost to run: none named, but the card lists hardware such as 2 H100 cards - Data it reaches: downloads files from Hugging Face, then runs where you host it - Not for: small machines, since the model needs about 78 GB for its weights - Not for: languages other than German and English, which are the ones the card lists ## README ````markdown # Kolibri-1 Reasons and calls tools in German and English, with a context window of more than a million tokens. An open 78B mixture-of-experts model from Aleph Alpha that activates only 3.46B parameters per token. Made by Aleph-Alpha 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: Kolibri-1 is a reasoning model from Aleph Alpha, built as a mixture of experts and focused on German and English. It has an explicit reasoning mode and supports tool calling. Only 3.46B of its 78B parameters work on each token, which keeps the cost of serving low. Who it suits: Teams that need an open model for German and English work, such as assistants, document processing and agents, and that can host a large model. ```table Industry | Who uses it | How they use it | Example Software | Developers | Build coding and agent features on an open model | An assistant that calls tools in a workflow Public sector | IT teams | Host a model on their own servers for German-language tasks | An internal question-answering service Legal and finance | Analysts | Process long documents in German or English | Extracting fields from a long contract Research | ML researchers | Study a mixture-of-experts model with a long context | A test of retrieval over extra-long inputs ``` If you only need a small model for a laptop, you can skip it, since it needs about 78 GB of memory. Where to use it: - Windows, macOS or Linux with Python, for downloading the files - Python programs that use the transformers library - Servers with large accelerators, as the card's hardware list describes ```table Use case | Who | What they get back Multi-step reasoning | Developers | Worked answers from the explicit reasoning mode Retrieval-augmented generation | Search and support teams | Answers grounded in documents you supply Agentic tool calling | Agent builders | Calls to your own tools from the model Long documents | Analysts | Processing of contexts up to 1,048,576 tokens ``` How to set it up: - Python, to run the Hugging Face download tool - About 78 GB of memory and disk for the FP8 weights - Hardware from the card's list, such as 2 A100 80 GB cards, 2 H100 SXM5 cards, or one H200, B200 or B300 1. Install the Hugging Face hub tools with pip. 2. Download the model files with the hf command. 3. Follow the model card to serve the model on suitable hardware. ```bash pip install -U huggingface_hub hf download Aleph-Alpha/Kolibri-1 ``` How to use it: You send German or English text to the served model, turn on its reasoning mode when a task needs several steps, and let it call tools when you define them. The card recommends contexts of at most 262,144 tokens for speed and hard tasks. Example: You give the model a long German report and ask for a summary in English with the key figures listed. With reasoning on, it works through the report and returns the summary. For newcomers and experts: Start by reading the model card to check your hardware, then try short German and English prompts before long documents. Keep early tests well under the recommended context size. The model is a 50-layer transformer with 384 experts per layer, one shared and six routed, and its attention mixes sliding-window and grouped-query layers at a 4 to 1 ratio. Most attention layers look at nearby text, which keeps long contexts affordable. The trade is memory, since the whole model must be loaded. ```flow Send German or English text Router picks 6 of 384 experts Reasoning mode works the task Answer or tool call comes back ``` Good to know: - License: Apache 2.0 - Cost to run: none named, but the card lists hardware such as 2 H100 cards - Data it reaches: downloads files from Hugging Face, then runs where you host it - Not for: small machines, since the model needs about 78 GB for its weights - Not for: languages other than German and English, which are the ones the card lists ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 78.1B - Weights: about 78.1 GB in F8_E4M3; about 39.1 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 13,319 - Likes: 1,011 - License: apache-2.0 - Languages: de, en - Based on: Aleph-Alpha/Kolibri-1-BF16 - Quantized versions: 28 - Fine-tunes: 8 - Tags: transformers, safetensors, kolibri1, text-generation, reasoning, conversational, fp8 ## Links - Model: https://huggingface.co/Aleph-Alpha/Kolibri-1 - Files: https://huggingface.co/Aleph-Alpha/Kolibri-1/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Aleph-Alpha/Kolibri-1 ``` 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 [Aleph-Alpha/Kolibri-1 on Hugging Face](https://huggingface.co/Aleph-Alpha/Kolibri-1) ## 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 | | --- | --- | --- | --- | --- | --- | | Kolibri-1 (this page) | 13,319 | 1,011 | 78.1B | apache-2.0 | 2026-11-06 | | [KVARK Sparkle 1.0 (27B) FP8](https://mindket.com/p/acc7b74d-4609-4c56-abe3-47d4ccc946e3) | 24,270 | 5 | 27.8B | apache-2.0 | 2026-10-29 | | [DeepSeek-V4.1-Flash](https://mindket.com/p/df96103d-374e-4ebe-b9f2-b75b09e3526d) | 1,759,335 | 4,949 | 763B | mit | 2026-10-16 | | [DeepSeek-R1](https://mindket.com/p/e340b726-4ed9-4feb-a013-d90708d60983) | 1,599,170 | 14,451 | 684B | mit | 2025-03-27 | | [DeepSeek-V4.1-Flash-1017](https://mindket.com/p/dc968c8e-249a-450a-af4c-bd0b11b76d78) | 398,161 | 5,600 | 763B | mit | 2026-11-10 | | [Qwen3.8-27B NVFP4](https://mindket.com/p/0bab94ef-84bc-4129-902d-c0241f777c51) | 268,673 | 239 | 18.2B | apache-2.0 | 2026-09-17 | 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 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · kkm · 2026-10-10](https://news.ycombinator.com/item?id=50035267): Aleph Alpha's latest Model playing Doom - [Bluesky · Claudia · 2026-10-08](https://bsky.app/profile/signorina37.ransomnews.online/post/3mxeehpblj22t): In fascia alta c'è Mistral Large4 (1K Mld di parametri); in fascia media (dai 20 ai 100 Mld di parametri) ci sono Kolibri-1 di Aleph Alpha (Germania - appena fusa con la canadese Cohere, poi Apertus 1.5 (Svizzera), ALIA-40b (Spagna), Holo4-35B di H Company (Francia) e TildeOpen-30b (Lettonia). - [Hacker News · kkm · 2026-10-06](https://news.ycombinator.com/item?id=49983114): Aleph Alpha's Kolibri-1 playing Breakout – Making decisions under 25ms per move - [Bluesky · Hackobar · 2026-10-18](https://bsky.app/profile/hackobar.bsky.social/post/3my5nxjrbhl2g): 7. Low-Overhead Hallucination Detection via FlashAttention Statistics LINK 8. llama.cpp adds support for Aleph-Alpha Kolibri-1 model LINK - [Hacker News · iab · 2026-10-20](https://news.ycombinator.com/item?id=50159779): Ask HN: What happened to open weights models?: I mean: - Aug 26: GLM-5.3 Flash, Qwen3.8 Flash Next - Aug 28: GLM-5.3, Tencent Hy4 Preview - Aug 31: DeepSeek V4 Flash Vision Exp - Sep 3: Ling-3.0 Flash Fin - Sep 8: Ling-3.0 Flash VL - Sep 10: DeepSeek V4.1 Flash - Sep 22: Xiaomi MiMo V2.6 - Oct 3: Aleph Alpha Kolibri-1 - Oct 13: Ling-3.1 Flash… - [Bluesky · Luca · 2026-10-15](https://bsky.app/profile/lpanarella.bsky.social/post/3mxwhcpqvtj22): Ottobre è il mese dei modelli aperti. L'europo Aleph Alpha ha pubblicato Kolibri-1: 78 miliardi di parametri scaricabili, licenza Apache 2.0, contesto da un milione di token. E per ogni token attiva solo 3,5 miliardi di parametri, grazie all'architettura a esperti. \#opensource - [Hacker News · befelix · 2026-10-03](https://news.ycombinator.com/item?id=49946931): Kolibri: A Sovereign Open-Weight Model: FP8 weights are actually the default and what we trained for: http://huggingface.co/Aleph-Alpha/Kolibri-1 We have a separate repo for BF16: Kolibri-1-BF16. Will still be tough to put it on a Mac though :( Disclaimer: I'm part of the team that trained Kolirbi - [Bluesky · labmemo · 2026-10-10](https://bsky.app/profile/labmemo.bsky.social/post/3mxjsmsux6h2o): Aleph Alpha「Kolibri 1」 何が起きたか: Aleph Alphaが独英バイリンガルMoEモデル「Kolibri 1」をApache 2.0で公開。 要点を画像にまとめました。 - [Hacker News · kkm · 2026-10-03](https://news.ycombinator.com/item?id=49944434): Kolibri: A Sovereign Open-Weight Model: Thank you Aleph Alpha team for making it open. We as many other’s were curious to try and benchmark it. On that note, as a small gesture of support, we’ve hosted and made Kolibri-1 free for anyone to try for the next few days. No GPU. No setup. Just try it. tesseracted.com/kolibri-1-chat/… - [Bluesky · @bsemion.bsky.social · 2026-11-11](https://bsky.app/profile/bsemion.bsky.social/post/3n22olul5kd2g): Kolibri-1 von Aleph Alpha: offene Gewichte, Deutsch und Englisch, Apache 2.0. Aber: Die starken Benchmarks sind Eigenangaben. Herkunft ersetzt keinen Test mit deiner Aufgabe. Würdest du KI selbst hosten? - [Bluesky · AKI.IO · 2026-10-22](https://bsky.app/profile/aki-io.bsky.social/post/3myhz2m2yq22x): A little birdie from Heidelberg is now chirping in German on AKI.IO. Kolibri 1 by Aleph Alpha. Built for anyone who wants full control: public administration, industry, defense, or aviation. Hosted in Germany. Zero Retention. No training. §203-ready. - [Bluesky · labmemo · 2026-10-10](https://bsky.app/profile/labmemo.bsky.social/post/3mxjucbxfct2s): https://labmemo.com/aleph-alpha-kolibri-1-german-english-moe-78b-guide-2026-10/ ## 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: 2026-10-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Aleph-Alpha/Kolibri-1 on Hugging Face](https://huggingface.co/Aleph-Alpha/Kolibri-1) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/dd5e0e4c-3802-49b1-9d73-51a8833393f7) - [Its page on Mindket](https://mindket.com/p/dd5e0e4c-3802-49b1-9d73-51a8833393f7)