--- title: "K2-Horizon-7B-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/d3557b60-83d2-4164-8e15-cbf497de8e72" updated: "2026-11-13" --- # K2-Horizon-7B-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Quantized GGUF copies of IFM's K2-Horizon-7B text model for running locally in llama.cpp, from BF16 down to 4-bit, with a 512K-token context window. K2-Horizon-7B-GGUF 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 IFM ## About ### What it is This repository holds GGUF copies of K2-Horizon-7B, a decoder-only text model from IFM with a 7B core and a 512K-token context window. The GGUF format lets you run it locally with llama.cpp instead of calling a hosted service. Each file carries its tokenizer data and a chat template that llama.cpp can read, so a chat session needs no extra setup files. ### Who it suits People who want to run an open 7B-class model on their own computer and read long inputs. The card says IFM tested the base model on agentic, coding, long-context and reasoning benchmarks. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Run a local coding and tool-use model | A coding helper that runs offline on a workstation | | Research | ML researchers | Study an open model and its released checkpoints | Comparing behavior across training stages | | Documents | Analysts | Feed long texts into one prompt | Asking questions about a long report | | Hobbyists | Local AI users | Try a quantized model on modest hardware | A 4-bit copy on a home PC | You can skip it if you want a hosted chat service, or if your llama.cpp build is older than the release the card names. ### Where to use it The files work with llama.cpp and tools built on it, on Windows, macOS and Linux. - llama.cpp, built from a release that supports the K2 Horizon architecture - Windows, macOS and Linux, where you can run llama.cpp - Any chat front end that loads GGUF files through llama.cpp | Use case | Who | What they get back | | --- | --- | --- | | Local chat | Anyone with a recent PC | Replies from the model without a cloud account | | Long-document questions | Analysts and writers | Answers drawn from a lengthy input | | Coding and agent tests | Developers | A local model to try on agentic and coding tasks | ### How to set it up You need Python for the download tool and a llama.cpp build that knows the K2 Horizon architecture. The card says mainline support arrived in release b11454. - Python with pip - A llama.cpp build from release b11454 or newer - Disk space: about 4.5 GB for the 4-bit copy, an estimate from Hugging Face 1. Install the Hugging Face hub tools with pip. 2. Download the repository files with the hf command. 3. Point llama.cpp at the GGUF variant you want. ```sh pip install -U huggingface_hub hf download IFM/K2-Horizon-7B-GGUF ``` ### How to use it Load one of the GGUF files in llama.cpp and chat with it. The built-in chat template means the prompt format is handled for you. The card lists these variants. | Variant | What it is | | --- | --- | | BF16 | The full-precision copy, the largest file | | Q8\_0 | An 8-bit quantization | | Q6\_K | A 6-bit quantization | | Q5\_K\_M | A 5-bit quantization, mixed | | Q5\_0 | A 5-bit quantization, plain | | Q4\_K\_M | A 4-bit quantization, the smallest listed | ### Example You open the Q4\_K\_M file in llama.cpp and ask it to explain a stack trace from your project. The model replies in the chat, running entirely on your own machine. ### For newcomers and experts If you are new, pick the 4-bit Q4\_K\_M file first, since it is the smallest and the easiest on memory. Use a recent llama.cpp, load the file and type a question. If the answers feel weak, try a larger variant. If you know the field: K2-Horizon-7B sits in the middle of the K2-Horizon family and is a dense model. Native 524,288-token context starts from the midtraining stages. IFM released intermediate checkpoints, plus training data, recipe, code and evaluation resources. Lower quantization shrinks the file and costs some accuracy. Download a GGUF file from the repository → Load it in a llama.cpp build with K2 Horizon support → The built-in chat template formats your prompt → The model replies on your own machine ### Good to know The card's table compares the model with Gemma 4-12B, Qwen3.5-9B and Granite 4.2-8B on competition mathematics. Treat these as the authors' own numbers. HMMT Feb 2026 competition math score | Label | Value | | --- | --- | | K2-Horizon-7B | 73.3 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, you run it on your own hardware - Data it reaches: the files you download from Hugging Face and the prompts you type locally - Not for: older llama.cpp builds without K2 Horizon support ## README ````markdown # K2-Horizon-7B-GGUF Quantized GGUF copies of IFM's K2-Horizon-7B text model for running locally in llama.cpp, from BF16 down to 4-bit, with a 512K-token context window. 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: This repository holds GGUF copies of K2-Horizon-7B, a decoder-only text model from IFM with a 7B core and a 512K-token context window. The GGUF format lets you run it locally with llama.cpp instead of calling a hosted service. Each file carries its tokenizer data and a chat template that llama.cpp can read, so a chat session needs no extra setup files. Who it suits: People who want to run an open 7B-class model on their own computer and read long inputs. The card says IFM tested the base model on agentic, coding, long-context and reasoning benchmarks. ```table Industry | Who uses it | How they use it | Example Software | Developers | Run a local coding and tool-use model | A coding helper that runs offline on a workstation Research | ML researchers | Study an open model and its released checkpoints | Comparing behavior across training stages Documents | Analysts | Feed long texts into one prompt | Asking questions about a long report Hobbyists | Local AI users | Try a quantized model on modest hardware | A 4-bit copy on a home PC ``` You can skip it if you want a hosted chat service, or if your llama.cpp build is older than the release the card names. Where to use it: The files work with llama.cpp and tools built on it, on Windows, macOS and Linux. - llama.cpp, built from a release that supports the K2 Horizon architecture - Windows, macOS and Linux, where you can run llama.cpp - Any chat front end that loads GGUF files through llama.cpp ```table Use case | Who | What they get back Local chat | Anyone with a recent PC | Replies from the model without a cloud account Long-document questions | Analysts and writers | Answers drawn from a lengthy input Coding and agent tests | Developers | A local model to try on agentic and coding tasks ``` How to set it up: You need Python for the download tool and a llama.cpp build that knows the K2 Horizon architecture. The card says mainline support arrived in release b11454. - Python with pip - A llama.cpp build from release b11454 or newer - Disk space: about 4.5 GB for the 4-bit copy, an estimate from Hugging Face 1. Install the Hugging Face hub tools with pip. 2. Download the repository files with the hf command. 3. Point llama.cpp at the GGUF variant you want. ```sh pip install -U huggingface_hub hf download IFM/K2-Horizon-7B-GGUF ``` How to use it: Load one of the GGUF files in llama.cpp and chat with it. The built-in chat template means the prompt format is handled for you. The card lists these variants. ```table Variant | What it is BF16 | The full-precision copy, the largest file Q8_0 | An 8-bit quantization Q6_K | A 6-bit quantization Q5_K_M | A 5-bit quantization, mixed Q5_0 | A 5-bit quantization, plain Q4_K_M | A 4-bit quantization, the smallest listed ``` Example: You open the Q4_K_M file in llama.cpp and ask it to explain a stack trace from your project. The model replies in the chat, running entirely on your own machine. For newcomers and experts: If you are new, pick the 4-bit Q4_K_M file first, since it is the smallest and the easiest on memory. Use a recent llama.cpp, load the file and type a question. If the answers feel weak, try a larger variant. If you know the field: K2-Horizon-7B sits in the middle of the K2-Horizon family and is a dense model. Native 524,288-token context starts from the midtraining stages. IFM released intermediate checkpoints, plus training data, recipe, code and evaluation resources. Lower quantization shrinks the file and costs some accuracy. ```flow Download a GGUF file from the repository Load it in a llama.cpp build with K2 Horizon support The built-in chat template formats your prompt The model replies on your own machine ``` Good to know: The card's table compares the model with Gemma 4-12B, Qwen3.5-9B and Granite 4.2-8B on competition mathematics. Treat these as the authors' own numbers. ```chart Title: HMMT Feb 2026 competition math score K2-Horizon-7B: 73.3 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, you run it on your own hardware - Data it reaches: the files you download from Hugging Face and the prompts you type locally - Not for: older llama.cpp builds without K2 Horizon support ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 9B - Weights: about 4.5 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 24,083 - Likes: 84 - License: apache-2.0 - Languages: en - Based on: IFM/K2-Horizon-7B - Fine-tunes: 1 - Tags: transformers, gguf, k2-horizon, 7b, dense, open-weights, text-generation, conversational ## Links - Model: https://huggingface.co/IFM/K2-Horizon-7B-GGUF - Files: https://huggingface.co/IFM/K2-Horizon-7B-GGUF/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-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 [IFM/K2-Horizon-7B-GGUF on Hugging Face](https://huggingface.co/IFM/K2-Horizon-7B-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 | | --- | --- | --- | --- | --- | --- | | K2-Horizon-7B-GGUF (this page) | 24,083 | 84 | 9B | apache-2.0 | 2026-10-15 | | [K2-Horizon-7B](https://mindket.com/p/ed5bb82e-c6d9-4e82-9741-b599322d66fb) | 42,327 | 287 | 9B | apache-2.0 | 2026-11-04 | | [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 | | [Ornith-1.5-9B-GGUF](https://mindket.com/p/5dd265f6-9a3e-450b-ad2a-b49a78e2700a) | 5,772,425 | 673 | 9.2B | mit | 2026-08-24 | | [Ornith-1.5-35B-A3B-GGUF](https://mindket.com/p/c98822f4-2255-450a-89ce-df15fc0003a1) | 4,164,876 | 603 | 35.5B | mit | 2026-08-24 | 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: 2026-10-15 - First published: 2026-09-03 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open IFM/K2-Horizon-7B-GGUF on Hugging Face](https://huggingface.co/IFM/K2-Horizon-7B-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/d3557b60-83d2-4164-8e15-cbf497de8e72) - [Its page on Mindket](https://mindket.com/p/d3557b60-83d2-4164-8e15-cbf497de8e72)