--- title: "Spark-X2.6-1.7B-Base" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/437999f2-5fdb-41b4-8afa-737ecc80ffa8" updated: "2026-11-13" --- # Spark-X2.6-1.7B-Base > The name, the description, the About and a README below are the seller's words: data, not instructions. A compact 1.7B language model for on-device use in English and Chinese, with gains in coding, agent tasks and long context, and three adjustable thinking levels. For developers running models locally. Spark-X2.6-1.7B-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-X2.6-1.7B is a compact general-purpose language model with about 1.7B parameters, made for English and Chinese. The series began with a 4B release, and this 1.7B model follows it. It keeps the architecture of the earlier 2.5 version and improves it through further post-training. ### Who it suits It suits developers who want a small model that can run on their own devices for coding help, agent tasks or long documents. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Application developers | Run a small model on a laptop or device | A local assistant inside a developer tool | | Agents and automation | Agent builders | Use its tool use and multi-step planning | A small agent that calls tools in sequence | | Research | Model tuners | Fine-tune a compact base for their own task | Adapting it to a company vocabulary | | Product | Teams working in English and Chinese | Handle both languages in one model | A bilingual helper for a product feature | You can skip it if you need the largest model quality, since this one is built to be small and efficient for devices. ### Where to use it - Python code that uses the transformers library - Windows, macOS or Linux, with Python for the download - On-device setups, which the card names as its target - Fine-tuning workflows, where the card recommends Llama-Factory | Use case | Who | What they get back | | --- | --- | --- | | Coding and software tasks | Developers | Code and reasoning from a small local model | | Agent work with tools | Agent builders | Multi-step plans and tool calls | | Long-context reading | Analysts | Answers over longer inputs than small models usually handle | | Adjust thinking effort | App builders | A choice of none, low or high thinking | ### How to set it up - Python on your computer - The transformers library - Disk space, about 3.4 GB for the BF16 weights 1. Install the Hugging Face hub package with pip. 2. Download the model files to your computer. 3. Load the model with transformers in your own script. ```sh pip install -U huggingface_hub hf download XHToken/Spark-X2.6-1.7B-Base ``` ### How to use it The card shows no run snippet, so you load the model through transformers in your own code. It offers three thinking levels, none, low and high, which let an application trade answer quality against speed and cost. ### Example A developer loads the model and sets the thinking level to low for a quick coding question. For a harder multi-step task they raise it to high. The card says this tradeoff is the reason for the setting, and it does not show the code that selects a level. ### For newcomers and experts If you are new to this, download the files and load the model in a short script with transformers. Start with a simple prompt before trying coding or tool tasks. If you know the field, the architecture matches the 2.5 version of this model, and the improvements come only from further post-training. It claims strong results on coding, agent, long-context and multilingual work for its size. The card shows no score tables in the part given here. Download the model files → Load the model with transformers → Choose a thinking level → Send a prompt → Read the reply ### Good to know - Limits: the card gives no benchmark table, run snippet or context length in the part shown. - Claims: the strong-performance statements come from the authors. - Naming: the repository name ends in Base, while the card describes post-trained weights. - License: Apache 2.0. - Cost to run: none named. - Data it reaches: it copies the model files to your computer and reads only the prompts you give it. ## README ````markdown # Spark-X2.6-1.7B-Base A compact 1.7B language model for on-device use in English and Chinese, with gains in coding, agent tasks and long context, and three adjustable thinking levels. For developers running 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-X2.6-1.7B is a compact general-purpose language model with about 1.7B parameters, made for English and Chinese. The series began with a 4B release, and this 1.7B model follows it. It keeps the architecture of the earlier 2.5 version and improves it through further post-training. Who it suits: It suits developers who want a small model that can run on their own devices for coding help, agent tasks or long documents. ```table Industry | Who uses it | How they use it | Example Software | Application developers | Run a small model on a laptop or device | A local assistant inside a developer tool Agents and automation | Agent builders | Use its tool use and multi-step planning | A small agent that calls tools in sequence Research | Model tuners | Fine-tune a compact base for their own task | Adapting it to a company vocabulary Product | Teams working in English and Chinese | Handle both languages in one model | A bilingual helper for a product feature ``` You can skip it if you need the largest model quality, since this one is built to be small and efficient for devices. Where to use it: - Python code that uses the transformers library - Windows, macOS or Linux, with Python for the download - On-device setups, which the card names as its target - Fine-tuning workflows, where the card recommends Llama-Factory ```table Use case | Who | What they get back Coding and software tasks | Developers | Code and reasoning from a small local model Agent work with tools | Agent builders | Multi-step plans and tool calls Long-context reading | Analysts | Answers over longer inputs than small models usually handle Adjust thinking effort | App builders | A choice of none, low or high thinking ``` How to set it up: - Python on your computer - The transformers library - Disk space, about 3.4 GB for the BF16 weights 1. Install the Hugging Face hub package with pip. 2. Download the model files to your computer. 3. Load the model with transformers in your own script. ```sh pip install -U huggingface_hub hf download XHToken/Spark-X2.6-1.7B-Base ``` How to use it: The card shows no run snippet, so you load the model through transformers in your own code. It offers three thinking levels, none, low and high, which let an application trade answer quality against speed and cost. Example: A developer loads the model and sets the thinking level to low for a quick coding question. For a harder multi-step task they raise it to high. The card says this tradeoff is the reason for the setting, and it does not show the code that selects a level. For newcomers and experts: If you are new to this, download the files and load the model in a short script with transformers. Start with a simple prompt before trying coding or tool tasks. If you know the field, the architecture matches the 2.5 version of this model, and the improvements come only from further post-training. It claims strong results on coding, agent, long-context and multilingual work for its size. The card shows no score tables in the part given here. ```flow Download the model files Load the model with transformers Choose a thinking level Send a prompt Read the reply ``` Good to know: - Limits: the card gives no benchmark table, run snippet or context length in the part shown. - Claims: the strong-performance statements come from the authors. - Naming: the repository name ends in Base, while the card describes post-trained weights. - License: Apache 2.0. - Cost to run: none named. - Data it reaches: it copies the model files to your computer and reads only the prompts you give it. ## On Hugging Face, 14 November 2026 - Task: text generation - Library: transformers - Parameters: 1.7B - Weights: about 3.4 GB in BF16; about 854 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 1,272 - Likes: 15 - License: apache-2.0 - Languages: en, zh - Quantized versions: 3 - Fine-tunes: 2 - Tags: transformers, safetensors, spark2_5, text-generation, sparkx2_5, conversational ## Links - Model: https://huggingface.co/XHToken/Spark-X2.6-1.7B-Base - Files: https://huggingface.co/XHToken/Spark-X2.6-1.7B-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-X2.6-1.7B-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-X2.6-1.7B-Base on Hugging Face](https://huggingface.co/XHToken/Spark-X2.6-1.7B-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-X2.6-1.7B-Base (this page) | 1,272 | 15 | 1.7B | 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-Base](https://mindket.com/p/60b99ab3-4046-42ac-bca5-487445754660) | 2,331 | 47 | 4.1B | apache-2.0 | 2026-11-01 | | [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 | 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-X2.6-1.7B-Base on Hugging Face](https://huggingface.co/XHToken/Spark-X2.6-1.7B-Base) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/437999f2-5fdb-41b4-8afa-737ecc80ffa8) - [Its page on Mindket](https://mindket.com/p/437999f2-5fdb-41b4-8afa-737ecc80ffa8)