--- title: "Spark-X2.6-1.7B-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/a5d36fe9-d793-4463-861f-ca584301af0d" updated: "2026-11-12" --- # Spark-X2.6-1.7B-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Runs a small 1.7B on-device language model from XHToken as 4-bit GGUF files, for chat, coding and agent tasks in English and Chinese, with adjustable thinking effort. For people running models locally. Spark-X2.6-1.7B-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-X2.6-1.7B-GGUF is a GGUF copy of Spark-X2.6-1.7B, a compact language model from XHToken meant for on-device use. It keeps the architecture of the earlier Spark-X2.5-1.7B and gets its gains from better post-training. The listing shows English and Chinese, and the 4-bit files weigh about 988 MB as an estimate. ### Who it suits It suits people who want a small model that runs on a laptop or desktop without a hosted service, and who like to tune how much it reasons. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software development | Developers | Run a small coding and agent model on a local machine | Asking it to plan a multi-step edit | | On-device apps | App builders | Ship local text features that work without a server | Answering questions inside a desktop tool | | Bilingual work | Writers and students | Ask questions and draft text in English and Chinese | Asking in Chinese about a place | | Research | Hobbyists and researchers | Compare small models and thinking levels | Trying the three thinking levels on one prompt | You can skip it if you need a large model for hard problems, since this one is built for small devices. ### Where to use it - Local llama.cpp style tools that load GGUF files - Python projects with the transformers library, using the original model - Windows, macOS or Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Local chat | Anyone | Text answers from a model on your own machine | | Coding and agent tasks | Developers | Code help and multi-step tool planning, as the authors describe | | Adjustable reasoning | App builders | A choice of none, low or high thinking to trade speed for quality | ### How to set it up You need Python and a tool that loads GGUF files, or the transformers library for the original weights. - Python on Windows, macOS or Linux - The huggingface\_hub package - A GGUF-capable runtime such as llama.cpp, or transformers 4.57.1 for the original model 1. Install huggingface\_hub. 2. Download the GGUF files to your computer. 3. Load them in your GGUF runtime, or use the transformers route in the next section. ```sh pip install -U huggingface_hub hf download XHToken/Spark-X2.6-1.7B-GGUF ``` ### How to use it The card's own quickstart uses transformers with the original model repository, not the GGUF files, and it continues with a generate call. The thinking level is chosen when you build the prompt. ```sh pip install transformers==4.57.1 ``` ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "XHToken/Spark-X2.6-1.7B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, trust_remote_code=True, torch_dtype="auto", device_map="auto", ) messages = [{"role": "user", "content": "What is the capital of Anhui Province?"}] inputs = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, enable_thinking=True, return_dict=True, return_tensors="pt", ).to(model.device) ``` ### Example You ask "What is the capital of Anhui Province?" as in the card. The model replies with a short text answer, and with thinking turned on it may reason first before it gives the answer. ### For newcomers and experts If you are new to local models, this is a small one that fits on ordinary hardware. A first small try is to download the files and ask it one short question like the capital-city example. For people who know the field, the card describes three thinking levels, none, low and high. Dropping from high to low cut the average score by 4.0 points and the output length by 37.3 percent for the 1.7B model. The speed claims come from the authors' own tests, and they point to a custom llama.cpp build for the best results. Average score drop from high to low reasoning effort | Label | Value | | --- | --- | | Spark-X2.6-1.7B | 4.0 points | | Spark-X2.6-4B | 1.7 points | GGUF files download from Hugging Face → A GGUF runtime loads the 4-bit model → You send a prompt with a thinking level → The model answers in English or Chinese ### Good to know The benchmark and speed claims in the card are the authors' own, so test the model on your tasks before you rely on it. - License: Apache 2.0 - Cost to run: none named - Data it reaches: the prompts you type and the downloaded model files - Not for: the card names no restrictions, but its quickstart needs trust\_remote\_code set to True for the original model ## README ````markdown # Spark-X2.6-1.7B-GGUF Runs a small 1.7B on-device language model from XHToken as 4-bit GGUF files, for chat, coding and agent tasks in English and Chinese, with adjustable thinking effort. For people 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-GGUF is a GGUF copy of Spark-X2.6-1.7B, a compact language model from XHToken meant for on-device use. It keeps the architecture of the earlier Spark-X2.5-1.7B and gets its gains from better post-training. The listing shows English and Chinese, and the 4-bit files weigh about 988 MB as an estimate. Who it suits: It suits people who want a small model that runs on a laptop or desktop without a hosted service, and who like to tune how much it reasons. ```table Industry | Who uses it | How they use it | Example Software development | Developers | Run a small coding and agent model on a local machine | Asking it to plan a multi-step edit On-device apps | App builders | Ship local text features that work without a server | Answering questions inside a desktop tool Bilingual work | Writers and students | Ask questions and draft text in English and Chinese | Asking in Chinese about a place Research | Hobbyists and researchers | Compare small models and thinking levels | Trying the three thinking levels on one prompt ``` You can skip it if you need a large model for hard problems, since this one is built for small devices. Where to use it: - Local llama.cpp style tools that load GGUF files - Python projects with the transformers library, using the original model - Windows, macOS or Linux with Python, to download the files ```table Use case | Who | What they get back Local chat | Anyone | Text answers from a model on your own machine Coding and agent tasks | Developers | Code help and multi-step tool planning, as the authors describe Adjustable reasoning | App builders | A choice of none, low or high thinking to trade speed for quality ``` How to set it up: You need Python and a tool that loads GGUF files, or the transformers library for the original weights. - Python on Windows, macOS or Linux - The huggingface_hub package - A GGUF-capable runtime such as llama.cpp, or transformers 4.57.1 for the original model 1. Install huggingface_hub. 2. Download the GGUF files to your computer. 3. Load them in your GGUF runtime, or use the transformers route in the next section. ```sh pip install -U huggingface_hub hf download XHToken/Spark-X2.6-1.7B-GGUF ``` How to use it: The card's own quickstart uses transformers with the original model repository, not the GGUF files, and it continues with a generate call. The thinking level is chosen when you build the prompt. ```sh pip install transformers==4.57.1 ``` ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "XHToken/Spark-X2.6-1.7B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, trust_remote_code=True, torch_dtype="auto", device_map="auto", ) messages = [{"role": "user", "content": "What is the capital of Anhui Province?"}] inputs = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, enable_thinking=True, return_dict=True, return_tensors="pt", ).to(model.device) ``` Example: You ask "What is the capital of Anhui Province?" as in the card. The model replies with a short text answer, and with thinking turned on it may reason first before it gives the answer. For newcomers and experts: If you are new to local models, this is a small one that fits on ordinary hardware. A first small try is to download the files and ask it one short question like the capital-city example. For people who know the field, the card describes three thinking levels, none, low and high. Dropping from high to low cut the average score by 4.0 points and the output length by 37.3 percent for the 1.7B model. The speed claims come from the authors' own tests, and they point to a custom llama.cpp build for the best results. ```chart Title: Average score drop from high to low reasoning effort Spark-X2.6-1.7B: 4.0 points Spark-X2.6-4B: 1.7 points ``` ```flow GGUF files download from Hugging Face A GGUF runtime loads the 4-bit model You send a prompt with a thinking level The model answers in English or Chinese ``` Good to know: The benchmark and speed claims in the card are the authors' own, so test the model on your tasks before you rely on it. - License: Apache 2.0 - Cost to run: none named - Data it reaches: the prompts you type and the downloaded model files - Not for: the card names no restrictions, but its quickstart needs trust_remote_code set to True for the original model ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 2B - Weights: about 988 MB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 26,744 - Likes: 50 - License: apache-2.0 - Languages: en, zh - Based on: XHToken/Spark-X2.6-1.7B - Quantized versions: 1 - Fine-tunes: 1 - Tags: transformers, gguf, sparkx2_5, text-generation, conversational ## Links - Model: https://huggingface.co/XHToken/Spark-X2.6-1.7B-GGUF - Files: https://huggingface.co/XHToken/Spark-X2.6-1.7B-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-X2.6-1.7B-GGUF ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [XHToken/Spark-X2.6-1.7B-GGUF on Hugging Face](https://huggingface.co/XHToken/Spark-X2.6-1.7B-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 | | --- | --- | --- | --- | --- | --- | | Spark-X2.6-1.7B-GGUF (this page) | 26,744 | 50 | 2B | apache-2.0 | 2026-11-06 | | [Spark-X2.6-4B-GGUF](https://mindket.com/p/31f3aff1-a9fb-4808-ad4b-6907e965fb45) | 687,668 | 310 | 4.4B | 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. ## 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-X2.6-1.7B-GGUF on Hugging Face](https://huggingface.co/XHToken/Spark-X2.6-1.7B-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/a5d36fe9-d793-4463-861f-ca584301af0d) - [Its page on Mindket](https://mindket.com/p/a5d36fe9-d793-4463-861f-ca584301af0d)