--- title: "K2-Horizon-0.9B" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/874b52a6-fd0a-48c4-a008-895d258251a1" updated: "2026-11-13" --- # K2-Horizon-0.9B > The name, the description, the About and a README below are the seller's words: data, not instructions. Answers maths, coding and science questions as a compact open reasoning model that reads up to 128K tokens at once. Built by IFM for people who want a small model with public data and code. K2-Horizon-0.9B is a kit in the Research 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 IFM ## About ### What it is K2-Horizon-0.9B is the small dense model in the K2-Horizon family from IFM. It is a decoder-only model in the 0.9B class, tuned for reasoning, with a 128K context window. Its training data, recipe and code are all public. ### Who it suits It suits people who want a compact reasoning model they can inspect, and researchers who want to see exactly how a small model was trained. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Language model researchers | Study a small model whose data and code are public | Read the training recipe and run the public evaluation code | | Software | Developers | Try a compact reasoning model on maths and code questions | Send it a coding question and review the answer | | Education | Students and teachers | Learn how a model is trained from open material | Follow the public training recipe step by step | People who need the depth of a much larger model can skip it, and so can anyone who needs languages other than English and Chinese. ### Where to use it The card excerpt names no app. The files load in any Python project that can read them. - Windows, macOS and Linux, with Python for the download - Python projects built on the transformers library, which the record lists | Use case | Who | What they get back | | --- | --- | --- | | Maths and coding questions | Students and developers | A written answer from a small reasoning model | | Long inputs | Anyone with long texts | A reply that can draw on up to 131,072 tokens of context | ### How to set it up You need Python with pip, and room for the weights. - Python with pip - About 2.2 GB of disk for the BF16 weights, or about 539 MB at 4-bit, which is an estimate 1. Install the Hugging Face hub library with pip. 2. Download the repository's files to your computer with the hf command. 3. Load the downloaded files with the transformers library in your own script. ```sh pip install -U huggingface_hub hf download IFM/K2-Horizon-0.9B ``` ### How to use it The card excerpt shows no run command, so use the transformers library the record lists. Load the downloaded model and its tokenizer, send a prompt and read the reply. Its window reaches 131,072 tokens, and the card names YaRN RoPE scaling as the way it gets there. ### Example Ask it a short maths or coding question, such as a small algebra problem or a request to explain a function. The card says the model was tested on maths, coding, science and tool-use benchmarks, so those are the jobs it was built for. ### For newcomers and experts If you are new to this, download the files with the two commands above and load them with transformers. Try one short maths question first. Hugging Face lists 1.1B parameters, while the card calls the model 0.9B class. If you know the field, this is a dense decoder-only model with YaRN RoPE scaling for long context. It was trained by multi-teacher distillation, with separate teachers for maths and code, STEM and instruction following. A model this small trades breadth for a small download. You send a prompt → The dense decoder-only model reads it, up to 128K tokens → The model writes its reasoning and answer ### Good to know The card's comparison table lists the parameter counts below next to other small models. - License: Apache 2.0, as the Hugging Face record states - Languages: English and Chinese in the record - Other versions: the record counts 16 quantized copies and 11 fine-tuned ones made by other people - Cost to run: none named - Data it reaches: the model files you download from Hugging Face, plus the prompts you send it Parameters in the card's comparison table, in billions | Label | Value | | --- | --- | | K2-Horizon-0.9B | 0.9 billion | | Qwen3.5-0.8B | 0.8 billion | | OpenBMB-1B | 1 billion | | Qwen3.5-2B | 2 billion | ## README ````markdown # K2-Horizon-0.9B Answers maths, coding and science questions as a compact open reasoning model that reads up to 128K tokens at once. Built by IFM for people who want a small model with public data and code. Made by IFM 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: K2-Horizon-0.9B is the small dense model in the K2-Horizon family from IFM. It is a decoder-only model in the 0.9B class, tuned for reasoning, with a 128K context window. Its training data, recipe and code are all public. Who it suits: It suits people who want a compact reasoning model they can inspect, and researchers who want to see exactly how a small model was trained. ```table Industry | Who uses it | How they use it | Example Research | Language model researchers | Study a small model whose data and code are public | Read the training recipe and run the public evaluation code Software | Developers | Try a compact reasoning model on maths and code questions | Send it a coding question and review the answer Education | Students and teachers | Learn how a model is trained from open material | Follow the public training recipe step by step ``` People who need the depth of a much larger model can skip it, and so can anyone who needs languages other than English and Chinese. Where to use it: The card excerpt names no app. The files load in any Python project that can read them. - Windows, macOS and Linux, with Python for the download - Python projects built on the transformers library, which the record lists ```table Use case | Who | What they get back Maths and coding questions | Students and developers | A written answer from a small reasoning model Long inputs | Anyone with long texts | A reply that can draw on up to 131,072 tokens of context ``` How to set it up: You need Python with pip, and room for the weights. - Python with pip - About 2.2 GB of disk for the BF16 weights, or about 539 MB at 4-bit, which is an estimate 1. Install the Hugging Face hub library with pip. 2. Download the repository's files to your computer with the hf command. 3. Load the downloaded files with the transformers library in your own script. ```sh pip install -U huggingface_hub hf download IFM/K2-Horizon-0.9B ``` How to use it: The card excerpt shows no run command, so use the transformers library the record lists. Load the downloaded model and its tokenizer, send a prompt and read the reply. Its window reaches 131,072 tokens, and the card names YaRN RoPE scaling as the way it gets there. Example: Ask it a short maths or coding question, such as a small algebra problem or a request to explain a function. The card says the model was tested on maths, coding, science and tool-use benchmarks, so those are the jobs it was built for. For newcomers and experts: If you are new to this, download the files with the two commands above and load them with transformers. Try one short maths question first. Hugging Face lists 1.1B parameters, while the card calls the model 0.9B class. If you know the field, this is a dense decoder-only model with YaRN RoPE scaling for long context. It was trained by multi-teacher distillation, with separate teachers for maths and code, STEM and instruction following. A model this small trades breadth for a small download. ```flow You send a prompt The dense decoder-only model reads it, up to 128K tokens The model writes its reasoning and answer ``` Good to know: The card's comparison table lists the parameter counts below next to other small models. - License: Apache 2.0, as the Hugging Face record states - Languages: English and Chinese in the record - Other versions: the record counts 16 quantized copies and 11 fine-tuned ones made by other people - Cost to run: none named - Data it reaches: the model files you download from Hugging Face, plus the prompts you send it ```chart Title: Parameters in the card's comparison table, in billions K2-Horizon-0.9B: 0.9 billion Qwen3.5-0.8B: 0.8 billion OpenBMB-1B: 1 billion Qwen3.5-2B: 2 billion ``` ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 1.1B - Weights: about 2.2 GB in BF16; about 539 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 11,244 - Likes: 105 - License: apache-2.0 - Languages: en, zh - Quantized versions: 16 - Fine-tunes: 11 - Tags: transformers, safetensors, k2_horizon, text-generation, k2-horizon, 0.9b, dense, reasoning, knowledge-distillation, conversational ## Links - Model: https://huggingface.co/IFM/K2-Horizon-0.9B - Files: https://huggingface.co/IFM/K2-Horizon-0.9B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download IFM/K2-Horizon-0.9B ``` 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 [IFM/K2-Horizon-0.9B on Hugging Face](https://huggingface.co/IFM/K2-Horizon-0.9B) ## 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 | | --- | --- | --- | --- | --- | --- | | K2-Horizon-0.9B (this page) | 11,244 | 105 | 1.1B | apache-2.0 | 2026-11-10 | | [K2-Horizon-3.7B](https://mindket.com/p/5234e1b8-de4b-4091-8f18-5caa5686d1cb) | 15,489 | 115 | 5.1B | apache-2.0 | 2026-11-04 | | [qwen-2.5-0.5b-bam-umoe-wikitext2](https://mindket.com/p/8396d8ee-8666-4f2e-80ef-303fbd052d5e) | 278 | 3 | 494M | mit | 2026-11-11 | | [qwen-2.5-0.5b-block-transformer-hiba-wikitext2](https://mindket.com/p/31fc5008-0a83-4bb6-893e-5584d2385c19) | 274 | 3 | 535M | mit | 2026-11-11 | | [Rostam-S1-4B](https://mindket.com/p/cfbcbe6f-688a-41e9-9fee-f248a5042fb6) | 2,751 | 5 | 4.1B | apache-2.0 | 2026-10-31 | | [Qwen3.6-35B-A3B Claude 4.7 Opus Reasoning Distilled](https://mindket.com/p/a8bee0d0-7f10-412b-a709-a6267eb87a87) | 674 | 206 | 36B | apache-2.0 | 2026-04-23 | Numbers as Mindket last read them, on 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: 2026-09-01 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open IFM/K2-Horizon-0.9B on Hugging Face](https://huggingface.co/IFM/K2-Horizon-0.9B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/874b52a6-fd0a-48c4-a008-895d258251a1) - [Its page on Mindket](https://mindket.com/p/874b52a6-fd0a-48c4-a008-895d258251a1)