--- title: "Spark-X2.6-4B-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/31f3aff1-a9fb-4808-ad4b-6907e965fb45" updated: "2026-11-12" --- # Spark-X2.6-4B-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. A compact 4B language model in GGUF form for on-device use, tuned for coding, agents and long context, with three thinking levels from none to high. Works in English and Chinese. Spark-X2.6-4B-GGUF 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 XHToken ## About ### What it is Spark-X2.6-4B-GGUF holds GGUF files of Spark-X2.6-4B, a compact general-purpose language model from XHToken for running on your own device. Hugging Face lists it for English and Chinese. The model keeps the architecture of Spark-X2.5-4B. The authors say the gains come from better post-training, mainly in coding, agent work, long context, multilingual use and reasoning. ### Who it suits Developers who want a small local model that handles tool use, planning and code, and people who build apps that must run on a device with limited resources. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers of on-device agents | Run a small model that plans and calls tools | A local assistant that works through a coding task | | Product teams | Builders of apps with limited resources | Tune thinking to balance quality and speed | Use low effort for quick replies | | Research | People comparing small models | Test a 4B model against larger ones | Check it against a 9B model | Skip it if you need the largest models, since this one is built for small devices. ### Where to use it - Your own computer, using a local runner that reads GGUF files - Windows, macOS or Linux with Python, to download the files - Python programs, using the Transformers route the card shows for the original model | Use case | Who | What they get back | | --- | --- | --- | | Agentic workflows | Agent builders | Multi-step plans and tool calls from a small model | | Coding help | Developers | Code and explanations on local hardware | | Long-context tasks | Researchers | Answers over longer inputs | ### How to set it up - Python and pip to run the download commands - A local runner that opens GGUF files - Transformers 4.57.1, if you follow the card Python route for the original model 1. Install the Hugging Face tools. 2. Download the GGUF files from this repository. 3. Open the file in your runner. ```bash pip install -U huggingface_hub hf download XHToken/Spark-X2.6-4B-GGUF ``` ```bash pip install transformers==4.57.1 ``` ### How to use it Chat with the model through your runner and pick one of three thinking levels, none, low or high. The card Python example loads the original model, builds a chat prompt with thinking switched on and generates a reply. | Thinking level | What it trades | | --- | --- | | none | No thinking, for the fastest and cheapest replies | | low | Shorter reasoning, with a small score drop and much shorter output | | high | Full reasoning for the best quality | ### Example You ask the model a short factual question, with thinking set to low for a quick reply. The card says low effort cuts average output length by 22.0 percent for the 4B model, with a 1.7 point drop in score. ### For newcomers and experts If you are new to local models, download the files, open them in a runner and start with a plain question at the low thinking level. Move to high only when answers need more care. If you know the field, the architecture is unchanged from the earlier Spark-X2.5-4B and the improvements are credited to post-training. The authors say it runs as efficiently as Qwen3.5-4B and Gemma-E4B, or more so. Download the GGUF file → Open it in a local runner → Choose a thinking level → Read the reply ### Good to know - The benchmark claims, including matching some 9B models, come from the authors and are shown as pictures in the card - The 4-bit files come to roughly 2.2 GB, by Hugging Face estimate - Hugging Face lists 2 quantized and 1 fine-tuned version made by other people Average output length drop from high to low effort | Label | Value | | --- | --- | | Spark-X2.6-1.7B | 37.3 percent | | Spark-X2.6-4B | 22.0 percent | - License: Apache 2.0 - Cost to run: none named - Data it reaches: the files you download and the prompts you give it - Not for: the source states no restrictions ## README ````markdown # Spark-X2.6-4B-GGUF A compact 4B language model in GGUF form for on-device use, tuned for coding, agents and long context, with three thinking levels from none to high. Works in English and Chinese. Made by XHToken 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: Spark-X2.6-4B-GGUF holds GGUF files of Spark-X2.6-4B, a compact general-purpose language model from XHToken for running on your own device. Hugging Face lists it for English and Chinese. The model keeps the architecture of Spark-X2.5-4B. The authors say the gains come from better post-training, mainly in coding, agent work, long context, multilingual use and reasoning. Who it suits: Developers who want a small local model that handles tool use, planning and code, and people who build apps that must run on a device with limited resources. ```table Industry | Who uses it | How they use it | Example Software | Developers of on-device agents | Run a small model that plans and calls tools | A local assistant that works through a coding task Product teams | Builders of apps with limited resources | Tune thinking to balance quality and speed | Use low effort for quick replies Research | People comparing small models | Test a 4B model against larger ones | Check it against a 9B model ``` Skip it if you need the largest models, since this one is built for small devices. Where to use it: - Your own computer, using a local runner that reads GGUF files - Windows, macOS or Linux with Python, to download the files - Python programs, using the Transformers route the card shows for the original model ```table Use case | Who | What they get back Agentic workflows | Agent builders | Multi-step plans and tool calls from a small model Coding help | Developers | Code and explanations on local hardware Long-context tasks | Researchers | Answers over longer inputs ``` How to set it up: - Python and pip to run the download commands - A local runner that opens GGUF files - Transformers 4.57.1, if you follow the card Python route for the original model 1. Install the Hugging Face tools. 2. Download the GGUF files from this repository. 3. Open the file in your runner. ```bash pip install -U huggingface_hub hf download XHToken/Spark-X2.6-4B-GGUF ``` ```bash pip install transformers==4.57.1 ``` How to use it: Chat with the model through your runner and pick one of three thinking levels, none, low or high. The card Python example loads the original model, builds a chat prompt with thinking switched on and generates a reply. ```table Thinking level | What it trades none | No thinking, for the fastest and cheapest replies low | Shorter reasoning, with a small score drop and much shorter output high | Full reasoning for the best quality ``` Example: You ask the model a short factual question, with thinking set to low for a quick reply. The card says low effort cuts average output length by 22.0 percent for the 4B model, with a 1.7 point drop in score. For newcomers and experts: If you are new to local models, download the files, open them in a runner and start with a plain question at the low thinking level. Move to high only when answers need more care. If you know the field, the architecture is unchanged from the earlier Spark-X2.5-4B and the improvements are credited to post-training. The authors say it runs as efficiently as Qwen3.5-4B and Gemma-E4B, or more so. ```flow Download the GGUF file Open it in a local runner Choose a thinking level Read the reply ``` Good to know: - The benchmark claims, including matching some 9B models, come from the authors and are shown as pictures in the card - The 4-bit files come to roughly 2.2 GB, by Hugging Face estimate - Hugging Face lists 2 quantized and 1 fine-tuned version made by other people ```chart Title: Average output length drop from high to low effort Spark-X2.6-1.7B: 37.3 percent Spark-X2.6-4B: 22.0 percent ``` - License: Apache 2.0 - Cost to run: none named - Data it reaches: the files you download and the prompts you give it - Not for: the source states no restrictions ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 4.4B - Weights: about 2.2 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 687,668 - Likes: 310 - License: apache-2.0 - Languages: en, zh - Based on: XHToken/Spark-X2.6-4B - Quantized versions: 2 - Fine-tunes: 1 - Tags: transformers, gguf, sparkx2_5, text-generation, conversational ## Links - Model: https://huggingface.co/XHToken/Spark-X2.6-4B-GGUF - Files: https://huggingface.co/XHToken/Spark-X2.6-4B-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download XHToken/Spark-X2.6-4B-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 [XHToken/Spark-X2.6-4B-GGUF on Hugging Face](https://huggingface.co/XHToken/Spark-X2.6-4B-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 | | --- | --- | --- | --- | --- | --- | | Spark-X2.6-4B-GGUF (this page) | 687,668 | 310 | 4.4B | apache-2.0 | 2026-11-06 | | [Spark-X2.6-1.7B-GGUF](https://mindket.com/p/a5d36fe9-d793-4463-861f-ca584301af0d) | 26,744 | 50 | 2B | apache-2.0 | 2026-11-06 | | [Spark-X2.6-4B GGUF](https://mindket.com/p/9f20961f-2fc8-4db6-9a24-95a407628b98) | 27,090 | 20 | 4.1B | apache-2.0 | 2026-10-20 | | [Spark-X2.6-4B](https://mindket.com/p/1f30e23c-ac63-4f34-b227-130e4306e0f2) | 28,117 | 1,041 | 4.1B | apache-2.0 | 2026-11-06 | | [Spark-X2.5-4B](https://mindket.com/p/36f36232-524c-4161-bacd-b9b5c290fea4) | 8,697 | 1,442 | 4.1B | apache-2.0 | 2026-09-15 | | [Spark-X2.6-1.7B](https://mindket.com/p/427232ce-5988-41f7-985c-029c680c8947) | 5,095 | 84 | 1.7B | apache-2.0 | 2026-11-06 | 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. - [Bluesky · UndergroundAI · 2026-10-27](https://bsky.app/profile/undergroundai66.bsky.social/post/3mythlftvuu22): ⚡ Ein neues Sprachmodell sorgt gerade für Aufsehen. 🛠️ Spark-X2.6-4B-GGUF von XHToken erobert als Trend-Modell auf HuggingFace die Community… 🚀 Entdecke diesen und weitere KI-Geheimtipps auf Underground AI. 👉 https://underground-ai.bolt.app/ \#OpenSource \#KuenstlicheIntelligenz - [Bluesky · SparkLLM · 2026-10-30](https://bsky.app/profile/sparkllm.bsky.social/post/3mz2timshb327): Our 4B model, a 94.6/100 community NanoBench score. Spark-X2.6-4B-GGUF + Hermes Agent: 9/12 solved. Compact, Apache-2.0, built for local agents. Thanks, userr99\! Support our open models: tap ❤️ Like on Hugging Face 👇 https://huggingface.co/XHToken/Spark-X2.6-4B ## 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: 2026-10-19 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open XHToken/Spark-X2.6-4B-GGUF on Hugging Face](https://huggingface.co/XHToken/Spark-X2.6-4B-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/31f3aff1-a9fb-4808-ad4b-6907e965fb45) - [Its page on Mindket](https://mindket.com/p/31f3aff1-a9fb-4808-ad4b-6907e965fb45)