--- title: "K2-Horizon-7B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/ed5bb82e-c6d9-4e82-9741-b599322d66fb" updated: "2026-11-12" --- # K2-Horizon-7B > The name, the description, the About and a README below are the seller's words: data, not instructions. Writes text, code and reasoning answers with a 512K-token context window, for developers and researchers who want an open dense model whose training data, code and checkpoints are all public. K2-Horizon-7B 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 IFM ## About ### What it is K2-Horizon-7B is the medium dense model in the K2-Horizon family from IFM. It is a decoder-only text generation model with a 7B core, and Hugging Face lists 9B parameters in total. Its context window reaches 524,288 tokens. ### Who it suits It suits developers and researchers who want an open dense model for agentic, coding, long-context and reasoning work, with training material they can inspect. Who uses this model | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Add coding and agent features backed by a local model | Ask it to fix a bug in a long file | | Research | Machine learning researchers | Study how abilities change during training | Compare the intermediate checkpoints with the final model | | AI labs | Training teams | Reuse the public data and recipe | Reproduce a training run with the released code | If you only need a tiny model for short chats, you can skip it, since this one has 9B parameters. ### Where to use it - Python scripts that use the transformers library - A GPU machine with room for the weights - Windows, macOS or Linux with Python, for downloading the files Jobs it does | Use case | Who | What they get back | | --- | --- | --- | | Coding help | Developers | Code and explanations in text | | Long documents | Analysts | Answers drawn from input up to 524,288 tokens | | Reasoning | Researchers | Written step-by-step answers | | Training study | Researchers | Intermediate checkpoints to compare across training | ### How to set it up You need Python, and disk space for the weights. - Python with pip - About 18 GB of disk space for BF16 weights, or about 4.5 GB for a 4-bit copy by Hugging Face's estimate - The transformers library 1. Install huggingface\_hub. 2. Run the download command to save the model files on your computer. 3. Load the files with transformers in your own script. ```bash pip install -U huggingface_hub hf download IFM/K2-Horizon-7B ``` ### How to use it Load the downloaded files with transformers and prompt the model in English. The card also points to a separate repository, K2-Horizon-7B-Uno, which holds Diffusion Adapters for faster inference. ### Example You give the model a long codebase file and ask it to find the cause of a failing test. It replies in text with an explanation and a suggested fix. ### For newcomers and experts Think of it as a general text and code model that can read long inputs. A first small test is a short coding question. Move to long documents once that works. It is a dense decoder-only model, not a mixture of experts. The long context is native from the midtraining stages onward, so it is not stretched afterward. The card compares it with Gemma 4-12B, Qwen3.5-9B and Granite 4.2-8B across math, coding and other benchmarks. Text in → Dense decoder-only model → Context up to 524,288 tokens → Text out ### Good to know The card reports 77.8 on HMMT Feb 2026 and 72.4 on SWE-bench Verified for this model. Other people have published 48 quantized and 16 fine-tuned versions. HMMT Feb 2026 score | Label | Value | | --- | --- | | K2-Horizon-7B | 77.8 points | | Granite 4.2-8B | 66.5 points | | Qwen3.5-9B | 65.7 points | | Gemma 4-12B | 63.1 points | - License: Apache 2.0. - Cost to run: none named. - Data it reaches: the files come from Hugging Face, and the model then runs on your own hardware. - Not for: languages beyond English, since Hugging Face lists only English. ## README ````markdown # K2-Horizon-7B Writes text, code and reasoning answers with a 512K-token context window, for developers and researchers who want an open dense model whose training data, code and checkpoints are all public. 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-7B is the medium dense model in the K2-Horizon family from IFM. It is a decoder-only text generation model with a 7B core, and Hugging Face lists 9B parameters in total. Its context window reaches 524,288 tokens. Who it suits: It suits developers and researchers who want an open dense model for agentic, coding, long-context and reasoning work, with training material they can inspect. ```table Title: Who uses this model Industry | Who uses it | How they use it | Example Software | Developers | Add coding and agent features backed by a local model | Ask it to fix a bug in a long file Research | Machine learning researchers | Study how abilities change during training | Compare the intermediate checkpoints with the final model AI labs | Training teams | Reuse the public data and recipe | Reproduce a training run with the released code ``` If you only need a tiny model for short chats, you can skip it, since this one has 9B parameters. Where to use it: - Python scripts that use the transformers library - A GPU machine with room for the weights - Windows, macOS or Linux with Python, for downloading the files ```table Title: Jobs it does Use case | Who | What they get back Coding help | Developers | Code and explanations in text Long documents | Analysts | Answers drawn from input up to 524,288 tokens Reasoning | Researchers | Written step-by-step answers Training study | Researchers | Intermediate checkpoints to compare across training ``` How to set it up: You need Python, and disk space for the weights. - Python with pip - About 18 GB of disk space for BF16 weights, or about 4.5 GB for a 4-bit copy by Hugging Face's estimate - The transformers library 1. Install huggingface_hub. 2. Run the download command to save the model files on your computer. 3. Load the files with transformers in your own script. ```bash pip install -U huggingface_hub hf download IFM/K2-Horizon-7B ``` How to use it: Load the downloaded files with transformers and prompt the model in English. The card also points to a separate repository, K2-Horizon-7B-Uno, which holds Diffusion Adapters for faster inference. Example: You give the model a long codebase file and ask it to find the cause of a failing test. It replies in text with an explanation and a suggested fix. For newcomers and experts: Think of it as a general text and code model that can read long inputs. A first small test is a short coding question. Move to long documents once that works. It is a dense decoder-only model, not a mixture of experts. The long context is native from the midtraining stages onward, so it is not stretched afterward. The card compares it with Gemma 4-12B, Qwen3.5-9B and Granite 4.2-8B across math, coding and other benchmarks. ```flow Text in Dense decoder-only model Context up to 524,288 tokens Text out ``` Good to know: The card reports 77.8 on HMMT Feb 2026 and 72.4 on SWE-bench Verified for this model. Other people have published 48 quantized and 16 fine-tuned versions. ```chart Title: HMMT Feb 2026 score K2-Horizon-7B: 77.8 points Granite 4.2-8B: 66.5 points Qwen3.5-9B: 65.7 points Gemma 4-12B: 63.1 points ``` - License: Apache 2.0. - Cost to run: none named. - Data it reaches: the files come from Hugging Face, and the model then runs on your own hardware. - Not for: languages beyond English, since Hugging Face lists only English. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 9B - Weights: about 18 GB in BF16; about 4.5 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 42,327 - Likes: 287 - License: apache-2.0 - Languages: en - Quantized versions: 48 - Fine-tunes: 15 - Tags: transformers, safetensors, k2_horizon, text-generation, k2-horizon, 7b, dense, open-weights, conversational ## Links - Model: https://huggingface.co/IFM/K2-Horizon-7B - Files: https://huggingface.co/IFM/K2-Horizon-7B/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-7B ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [IFM/K2-Horizon-7B on Hugging Face](https://huggingface.co/IFM/K2-Horizon-7B) ## 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-7B (this page) | 42,327 | 287 | 9B | apache-2.0 | 2026-11-04 | | [K2-Horizon-7B-GGUF](https://mindket.com/p/d3557b60-83d2-4164-8e15-cbf497de8e72) | 24,083 | 84 | 9B | apache-2.0 | 2026-10-15 | | [K2-Horizon-3.7B-GGUF](https://mindket.com/p/569aa0ff-40d8-41de-8f67-2b1f20cd01b7) | 19,581 | 60 | 5.1B | apache-2.0 | 2026-10-15 | | [Ming-3.0-tts-0.5B by inclusionAI](https://mindket.com/p/f13d7a41-4e55-40ee-b52d-b5fd3d09578b) | 14,756 | 225 | 1.5B | mit | 2026-10-27 | | [Qwen3-0.6B](https://mindket.com/p/0598bfe0-640a-4bf0-b9f4-ca4cf70a04ae) | 19,718,599 | 1,894 | 752M | apache-2.0 | 2025-07-26 | | [Qwen3-14B](https://mindket.com/p/56074bfc-f2ce-4109-8314-1af039530db4) | 12,965,376 | 540 | 14.8B | apache-2.0 | 2025-07-26 | 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 · cpobuda · 2026-11-05](https://news.ycombinator.com/item?id=50380633): Ask HN: What's your current AI agentic setup?: OpenCode + DeepSeek V4.1 Flash 1017 and IFM K2 Horizon 7B with speculative decoding via custom inference engine (MLX). Helyn Implement for coding, I work on design and specification, agentic protocol handles coding, testing, automated testing, and reviews (multiple models), process works extremely… - [Bluesky · AI Planet · 2026-09-17](https://bsky.app/profile/aiplanet.bsky.social/post/3mvqkeatrge2i): 📰 Reddit r/LocalLLaMA IFM/K2-Horizon-7B-Uno · Hugging Face - 5200tps with no quality loss https://www.reddit.com/r/LocalLLaMA/comments/1wj2hsm/ifmk2horizon7buno\_hugging\_face\_5200tps\_with\_no/ \#IA \#AI \#ML - [Hacker News · kennywinker · 2026-09-04](https://news.ycombinator.com/item?id=49561669): K2 Horizon: A connected fleet of six open models: If I am reading this right, the 7b model performs as well as qwen3.6-35b-a3b at coding? K2 horizon 7b scores 70.6 on swe-bench-verified. Qwen3.6-35b-a3b scores a 70.0 on swe-bench-verified. That’s pretty interesting. I assume the benchmark and reality don’t line up, but i’m downloading it now to… - [Bluesky · AI Planet · 2026-09-16](https://bsky.app/profile/aiplanet.bsky.social/post/3mvltjx4rkq2w): 📰 Reddit r/LocalLLaMA I benchmarked IFM/K2-Horizon-7B on 16GB VRAM https://www.reddit.com/r/LocalLLaMA/comments/1wha6sv/i\_benchmarked\_ifmk2horizon7b\_on\_16gb\_vram/ \#IA \#AI \#ML - [Hacker News · kzrdude · 2026-09-03](https://news.ycombinator.com/item?id=49557308): K2 Horizon: A connected fleet of six open models: The Uno "diffusion adaptor" will take a while for me to understand, but sounds very interesting. https://huggingface.co/IFM/K2-Horizon-7B-Uno ## 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: 2026-11-04 - First published: 2026-09-01 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open IFM/K2-Horizon-7B on Hugging Face](https://huggingface.co/IFM/K2-Horizon-7B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/ed5bb82e-c6d9-4e82-9741-b599322d66fb) - [Its page on Mindket](https://mindket.com/p/ed5bb82e-c6d9-4e82-9741-b599322d66fb)