--- title: "Spark-X2.6-1.7B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/427232ce-5988-41f7-985c-029c680c8947" updated: "2026-11-12" --- # Spark-X2.6-1.7B > The name, the description, the About and a README below are the seller's words: data, not instructions. Generates text with a compact 1.7B model for on-device use, with three adjustable thinking levels. Suits developers who want coding, agent and long-context help on local hardware. Spark-X2.6-1.7B 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 is a compact language model for English and Chinese, built to run on local devices. The series began with a 4B release, and this 1.7B model is the next one. It keeps the architecture of Spark-X2.5-1.7B and gets its gains from better post-training in coding, agent work, long contexts, multilingual use and reasoning. ### Who it suits Developers who want a small, fast model on a laptop or an edge device, and who like to set how much the model thinks. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Run a small coding and agent model on local hardware | Add a local assistant to a desktop tool | | Mobile and edge | Device builders | Put an on-device language model in an app | Answer questions without a server | | Education | Learners | Try a modern small model on a normal computer | Ask it a question in Chinese or English | If you need the strongest answers and have a big GPU, you can skip it. ### Where to use it - Hugging Face Transformers, with a pinned version - A llama.cpp build, which the card says its authors optimized for speed - Windows, macOS or Linux | Use case | Who | What they get back | | --- | --- | --- | | Coding help | Developers | Code and fixes from a small model | | Agent tasks | Agent builders | Multi-step planning and tool use | | Long-context reading | Analysts | Answers drawn from a long input | ### How to set it up - Python with pip - A fixed Transformers version, 4.57.1 - About 3.4 GB for BF16 weights, or about 854 MB at 4-bit by Hugging Face's estimate 1. Install the Hugging Face hub package. 2. Install the Transformers version the card names. 3. Download the model and load it with remote code enabled. ```sh pip install -U huggingface_hub hf download XHToken/Spark-X2.6-1.7B ``` ```sh pip install transformers==4.57.1 ``` ### How to use it Load the tokenizer and the model, then send a chat message through the chat template. The card lets you choose one of three thinking levels, none, low or high, to trade quality against speed and cost. ```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", ) ``` | Tool | What it does | | --- | --- | | AutoTokenizer | Loads the tokenizer and applies the chat template | | AutoModelForCausalLM | Loads the model and generates replies | | Thinking level | Chooses none, low or high reasoning effort | ### Example You ask it a short factual question in a chat message. The model replies in text, and with a lower thinking level the reply is shorter and quicker. ### For newcomers and experts If you are new, load the model with the snippet above and send one short question. Try the low thinking level when speed matters. The card compares thinking levels directly. Going from high to low lowers the average score by 4.0 points and cuts the average output length by 37.3 percent. The model reuses the Spark-X2.5-1.7B architecture, so the changes come from post-training rather than from new layers. Load the tokenizer and model → Build chat messages → Pick a thinking level → Generate and read the reply ### Good to know - Loading uses trust\_remote\_code, so review the repo code first - The card pins Transformers 4.57.1 - License: Apache 2.0 - Cost to run: none named, you supply the hardware - Data it reaches: your prompts and the files downloaded from Hugging Face - Not for: Transformers versions other than the one the card names Switching from high to low reasoning effort | Label | Value | | --- | --- | | Average score drop | 4.0 points | | Average output length drop | 37.3 percent | ## README ````markdown # Spark-X2.6-1.7B Generates text with a compact 1.7B model for on-device use, with three adjustable thinking levels. Suits developers who want coding, agent and long-context help on local hardware. 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 language model for English and Chinese, built to run on local devices. The series began with a 4B release, and this 1.7B model is the next one. It keeps the architecture of Spark-X2.5-1.7B and gets its gains from better post-training in coding, agent work, long contexts, multilingual use and reasoning. Who it suits: Developers who want a small, fast model on a laptop or an edge device, and who like to set how much the model thinks. ```table Industry | Who uses it | How they use it | Example Software | Developers | Run a small coding and agent model on local hardware | Add a local assistant to a desktop tool Mobile and edge | Device builders | Put an on-device language model in an app | Answer questions without a server Education | Learners | Try a modern small model on a normal computer | Ask it a question in Chinese or English ``` If you need the strongest answers and have a big GPU, you can skip it. Where to use it: - Hugging Face Transformers, with a pinned version - A llama.cpp build, which the card says its authors optimized for speed - Windows, macOS or Linux ```table Use case | Who | What they get back Coding help | Developers | Code and fixes from a small model Agent tasks | Agent builders | Multi-step planning and tool use Long-context reading | Analysts | Answers drawn from a long input ``` How to set it up: - Python with pip - A fixed Transformers version, 4.57.1 - About 3.4 GB for BF16 weights, or about 854 MB at 4-bit by Hugging Face's estimate 1. Install the Hugging Face hub package. 2. Install the Transformers version the card names. 3. Download the model and load it with remote code enabled. ```sh pip install -U huggingface_hub hf download XHToken/Spark-X2.6-1.7B ``` ```sh pip install transformers==4.57.1 ``` How to use it: Load the tokenizer and the model, then send a chat message through the chat template. The card lets you choose one of three thinking levels, none, low or high, to trade quality against speed and cost. ```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", ) ``` ```table Tool | What it does AutoTokenizer | Loads the tokenizer and applies the chat template AutoModelForCausalLM | Loads the model and generates replies Thinking level | Chooses none, low or high reasoning effort ``` Example: You ask it a short factual question in a chat message. The model replies in text, and with a lower thinking level the reply is shorter and quicker. For newcomers and experts: If you are new, load the model with the snippet above and send one short question. Try the low thinking level when speed matters. The card compares thinking levels directly. Going from high to low lowers the average score by 4.0 points and cuts the average output length by 37.3 percent. The model reuses the Spark-X2.5-1.7B architecture, so the changes come from post-training rather than from new layers. ```flow Load the tokenizer and model Build chat messages Pick a thinking level Generate and read the reply ``` Good to know: - Loading uses trust_remote_code, so review the repo code first - The card pins Transformers 4.57.1 - License: Apache 2.0 - Cost to run: none named, you supply the hardware - Data it reaches: your prompts and the files downloaded from Hugging Face - Not for: Transformers versions other than the one the card names ```chart Title: Switching from high to low reasoning effort Average score drop: 4.0 points Average output length drop: 37.3 percent ``` ## On Hugging Face, 13 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: 5,095 - Likes: 84 - License: apache-2.0 - Languages: en, zh - Based on: XHToken/Spark-X2.6-1.7B-Base - Quantized versions: 16 - Fine-tunes: 4 - Tags: transformers, safetensors, spark2_5, text-generation, sparkx2_5, conversational ## Links - Model: https://huggingface.co/XHToken/Spark-X2.6-1.7B - Files: https://huggingface.co/XHToken/Spark-X2.6-1.7B/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 ``` 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 on Hugging Face](https://huggingface.co/XHToken/Spark-X2.6-1.7B) ## 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 (this page) | 5,095 | 84 | 1.7B | apache-2.0 | 2026-11-06 | | [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-X2.6-4B-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 | | [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. ## 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 · Sung Kim · 2026-10-28](https://bsky.app/profile/sungkim.bsky.social/post/3myvjqm2njk2c): SparkLLM's Spark-X2.6-1.7B (open-weight) - Post-trained, Base, GGUF, INT8, INT4 \& FP8 on HF. Model: huggingface.co/XHToken/Spar... - [Bluesky · SparkLLM · 2026-11-01](https://bsky.app/profile/sparkllm.bsky.social/post/3mzawowzfyn26): 769 tok/s on RTX 4090\! Spark-X2.6-1.7B DSpark (GSM8K, Q4\_K\_M, llama.cpp). Up to 2.43x speedup (BF16, GSM8K). Like \& download: https://modelscope.cn/models/XHToken/Spark-X2.6-1.7B-DSpark-GGUF https://huggingface.co/XHToken/Spark-X2.6-1.7B-DSpark-GGUF - [Bluesky · SparkLLM · 2026-10-27](https://bsky.app/profile/sparkllm.bsky.social/post/3myuj7ud7m62n): Spark-X2.6-1.7B is here\! Stronger coding, agents and long-context capabilities through post-training; none/low/high thinking budgets. Explore the model formats, try it locally, and leave a Like on Hugging Face\! https://huggingface.co/XHToken/Spark-X2.6-1.7B ## 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 on Hugging Face](https://huggingface.co/XHToken/Spark-X2.6-1.7B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/427232ce-5988-41f7-985c-029c680c8947) - [Its page on Mindket](https://mindket.com/p/427232ce-5988-41f7-985c-029c680c8947)