--- title: "Spark-X2.6-4B-Base" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/60b99ab3-4046-42ac-bca5-487445754660" updated: "2026-11-13" --- # Spark-X2.6-4B-Base > The name, the description, the About and a README below are the seller's words: data, not instructions. A 4.1B language model for on-device use, tuned for coding, agent tasks, long context and reasoning in English and Chinese, with three thinking levels. For developers who run models locally. Spark-X2.6-4B-Base 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 XHToken ## About ### What it is Spark-X2.6-4B-Base is a 4.1B-parameter language model from XHToken, aimed at running on devices rather than big servers. It uses the same architecture as the earlier Spark-X2.5-4B. The gains come from better post-training, mostly in coding, agent work, long context, multilingual use and reasoning. ### Who it suits Developers who want a small text model they can run themselves, especially for coding help and agent-style tasks that use tools. The page lists English and Chinese as its languages. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | App developers | Add a small local language model to an app | Run the model on a laptop for coding help | | AI agents | Agent builders | Try tool use and multi-step planning on a compact model | Test how well it plans a task with several steps | | Research | ML practitioners | Fine-tune a compact model for a narrow job | Tune it with Llama-Factory, which the card recommends | | Multilingual teams | Teams working in English and Chinese | Draft and answer text in both languages | Ask the same question in each language | You can skip it if you need a model that sees images or audio, since the page describes a text model. ### Where to use it - Windows, macOS and Linux with Python, through the transformers library - Llama-Factory, the tool the card suggests for fine-tuning - On-device setups, which the model is built for | Use case | Who | What they get back | | --- | --- | --- | | Coding help | Developers | Code and explanations as text | | Agent planning | Agent builders | Step-by-step plans that involve tools | | Long documents | Analysts | Answers from longer inputs, which the card lists as improved | | Fine-tuning | ML practitioners | A custom version trained for your task | ### How to set it up You need Python and enough memory for the weights, about 8.2 GB in BF16 or about 2.1 GB at 4-bit by estimate. - Python on Windows, macOS or Linux - The transformers library - Disk space for the Safetensors files 1. Install Python if you do not have it. 2. Install the Hugging Face hub tool. 3. Download the model files. 4. Load the model with transformers in your own script. ```bash pip install -U huggingface_hub hf download XHToken/Spark-X2.6-4B-Base ``` ### How to use it The card shows no run code. You load the files with transformers and send the model a prompt. The card says applications can pick one of three thinking levels, none, low or high, to trade reasoning quality against speed and cost. ### Example You ask it to plan the steps for fixing a failing test in a small Python project. It replies with text that lists the steps in order. ### For newcomers and experts This is a small language model that completes text and follows instructions on your own computer. A first small step is to download it, load it with transformers and ask one short question. It keeps the Spark-X2.5-4B architecture and improves through post-training alone. The card claims strong results for its size, matching or passing models of around 9B parameters. Those are the authors' claims, so test on your own tasks. The thinking level lets an application spend more or less effort per answer. Download the model files → Load them with transformers → Choose a thinking level → Send a prompt and read the text reply ### Good to know - The card says the repository holds weights and configuration files in the Hugging Face Transformers format. - The 4.1B parameters take about 8.2 GB in BF16, or about 2.1 GB at 4-bit by estimate. - Other people have published 5 quantized and 4 fine-tuned versions. - License: Apache 2.0. - Cost to run: none named, you use your own hardware. - Data it reaches: files downloaded from Hugging Face, and the text you give it. - Not for: the card names no limits. ## README ````markdown # Spark-X2.6-4B-Base A 4.1B language model for on-device use, tuned for coding, agent tasks, long context and reasoning in English and Chinese, with three thinking levels. For developers who run models locally. 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-Base is a 4.1B-parameter language model from XHToken, aimed at running on devices rather than big servers. It uses the same architecture as the earlier Spark-X2.5-4B. The gains come from better post-training, mostly in coding, agent work, long context, multilingual use and reasoning. Who it suits: Developers who want a small text model they can run themselves, especially for coding help and agent-style tasks that use tools. The page lists English and Chinese as its languages. ```table Industry | Who uses it | How they use it | Example Software | App developers | Add a small local language model to an app | Run the model on a laptop for coding help AI agents | Agent builders | Try tool use and multi-step planning on a compact model | Test how well it plans a task with several steps Research | ML practitioners | Fine-tune a compact model for a narrow job | Tune it with Llama-Factory, which the card recommends Multilingual teams | Teams working in English and Chinese | Draft and answer text in both languages | Ask the same question in each language ``` You can skip it if you need a model that sees images or audio, since the page describes a text model. Where to use it: - Windows, macOS and Linux with Python, through the transformers library - Llama-Factory, the tool the card suggests for fine-tuning - On-device setups, which the model is built for ```table Use case | Who | What they get back Coding help | Developers | Code and explanations as text Agent planning | Agent builders | Step-by-step plans that involve tools Long documents | Analysts | Answers from longer inputs, which the card lists as improved Fine-tuning | ML practitioners | A custom version trained for your task ``` How to set it up: You need Python and enough memory for the weights, about 8.2 GB in BF16 or about 2.1 GB at 4-bit by estimate. - Python on Windows, macOS or Linux - The transformers library - Disk space for the Safetensors files 1. Install Python if you do not have it. 2. Install the Hugging Face hub tool. 3. Download the model files. 4. Load the model with transformers in your own script. ```bash pip install -U huggingface_hub hf download XHToken/Spark-X2.6-4B-Base ``` How to use it: The card shows no run code. You load the files with transformers and send the model a prompt. The card says applications can pick one of three thinking levels, none, low or high, to trade reasoning quality against speed and cost. Example: You ask it to plan the steps for fixing a failing test in a small Python project. It replies with text that lists the steps in order. For newcomers and experts: This is a small language model that completes text and follows instructions on your own computer. A first small step is to download it, load it with transformers and ask one short question. It keeps the Spark-X2.5-4B architecture and improves through post-training alone. The card claims strong results for its size, matching or passing models of around 9B parameters. Those are the authors' claims, so test on your own tasks. The thinking level lets an application spend more or less effort per answer. ```flow Download the model files Load them with transformers Choose a thinking level Send a prompt and read the text reply ``` Good to know: - The card says the repository holds weights and configuration files in the Hugging Face Transformers format. - The 4.1B parameters take about 8.2 GB in BF16, or about 2.1 GB at 4-bit by estimate. - Other people have published 5 quantized and 4 fine-tuned versions. - License: Apache 2.0. - Cost to run: none named, you use your own hardware. - Data it reaches: files downloaded from Hugging Face, and the text you give it. - Not for: the card names no limits. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 4.1B - Weights: about 8.2 GB in BF16; about 2.1 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 2,331 - Likes: 47 - License: apache-2.0 - Languages: en, zh - Quantized versions: 5 - Fine-tunes: 4 - Tags: transformers, safetensors, spark2_5, text-generation, sparkx2_5, conversational ## Links - Model: https://huggingface.co/XHToken/Spark-X2.6-4B-Base - Files: https://huggingface.co/XHToken/Spark-X2.6-4B-Base/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-Base ``` 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-Base on Hugging Face](https://huggingface.co/XHToken/Spark-X2.6-4B-Base) ## 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-Base (this page) | 2,331 | 47 | 4.1B | apache-2.0 | 2026-11-01 | | [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 | | [Spark-X2.6-4B-FP8](https://mindket.com/p/febc8500-5824-4ac8-abec-ec783a54755f) | 2,048 | 39 | 4.1B | apache-2.0 | 2026-11-06 | | [Spark-X2.6-1.7B-Base](https://mindket.com/p/437999f2-5fdb-41b4-8afa-737ecc80ffa8) | 1,272 | 15 | 1.7B | apache-2.0 | 2026-11-01 | 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: 2026-11-01 - First published: 2026-10-19 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open XHToken/Spark-X2.6-4B-Base on Hugging Face](https://huggingface.co/XHToken/Spark-X2.6-4B-Base) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/60b99ab3-4046-42ac-bca5-487445754660) - [Its page on Mindket](https://mindket.com/p/60b99ab3-4046-42ac-bca5-487445754660)