--- title: "Spark-X2.6-4B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/1f30e23c-ac63-4f34-b227-130e4306e0f2" updated: "2026-11-12" --- # Spark-X2.6-4B > The name, the description, the About and a README below are the seller's words: data, not instructions. A 4.1B language model for coding, agent work and long context in English and Chinese, with three thinking levels. For developers building agents that run on their own devices. Spark-X2.6-4B 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 is a compact general-purpose language model with 4.1B parameters, made for on-device use. It keeps the architecture of Spark-X2.5-4B and gains its improvements from better post-training. It works in English and Chinese, and it has three thinking levels: none, low and high. ### Who it suits It suits developers who want a small model for local agents, coding help and long documents, without sending data to a server. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Run a local coding and agent assistant | A helper that plans steps and calls tools on a laptop | | Product teams | App makers | Ship an on-device assistant | A chat feature that works without a cloud model | | Global teams | Content staff | Work in English and Chinese with one model | Draft and check text in both languages | | AI research | Researchers | Study the cost of thinking at different levels | Compare scores and output length at high and low effort | If you need a huge model for the hardest tasks, you can skip it. ### Where to use it - Python with the transformers library, pinned to version 4.57.1 - Laptops, desktops and other local machines, as the card targets on-device deployment - Windows, macOS or Linux | Use case | Who | What they get back | | --- | --- | --- | | Coding help | Developers | Code and explanations at the thinking level you choose | | Agent tasks | Agent builders | Tool-use and multi-step planning from a small model | | Long-context reading | Analysts | Answers drawn from long inputs | ### How to set it up You need these first: - Python and pip - Transformers at version 4.57.1 - About 8.2 GB of disk for the BF16 weights, or about 2.1 GB for a 4-bit estimate 1. Install the exact transformers version the card names. 2. Download the model files, or let transformers fetch them on first load. 3. Load the tokenizer and model with the Python code below. ```sh pip install transformers==4.57.1 ``` ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "XHToken/Spark-X2.6-4B" 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?"}] ``` ### How to use it Put your question into a list of messages, pass it through the tokenizer's chat template, and generate. The card's example turns thinking on in the template. Choose none, low or high thinking to trade answer quality against speed and cost. | Tool | What it does | | --- | --- | | enable\_thinking=True | Turns thinking on in the chat template example | | Thinking levels | None, low or high, so you balance quality, latency and cost | | trust\_remote\_code=True | Lets the code that ships with the model run when it loads | | device\_map="auto" | Places the model on the devices you have | ### Example You ask, "Write a function that checks whether a string is a palindrome." With thinking on, the model reasons first and then returns the function. With thinking at none, it answers faster with less reasoning. ### For newcomers and experts This is a small model that you can run on your own machine. A good first step is the short Python example above, with thinking on, and then the same question with thinking off. The authors say the gains come from post-training alone, with the architecture unchanged from the earlier release. They report that it matches or beats some models of about 9B parameters, and that its inference speed is on par with Qwen3.5-4B and Gemma-E4B. You write messages for the model → The chat template adds the thinking setting → The model reasons, then answers → You read the text it returns ### Good to know Lower thinking effort costs a little accuracy and saves output length, by the card's numbers. Average score drop from high to low effort | Label | Value | | --- | --- | | Spark-X2.6-4B | 1.7 points | | Spark-X2.6-1.7B | 4.0 points | - Output length: switching from high to low effort shortens average output by 22.0% for the 4B model and 37.3% for the 1.7B model - Claims: the comparisons with other models are the authors' own, shown as charts on the card - Other versions: 63 quantized and 23 fine-tuned versions made by other people are listed - License: Apache 2.0 - Cost to run: none named, you supply the hardware - Data it reaches: the prompts you give it ## README ````markdown # Spark-X2.6-4B A 4.1B language model for coding, agent work and long context in English and Chinese, with three thinking levels. For developers building agents that run on their own devices. 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 is a compact general-purpose language model with 4.1B parameters, made for on-device use. It keeps the architecture of Spark-X2.5-4B and gains its improvements from better post-training. It works in English and Chinese, and it has three thinking levels: none, low and high. Who it suits: It suits developers who want a small model for local agents, coding help and long documents, without sending data to a server. ```table Industry | Who uses it | How they use it | Example Software | Developers | Run a local coding and agent assistant | A helper that plans steps and calls tools on a laptop Product teams | App makers | Ship an on-device assistant | A chat feature that works without a cloud model Global teams | Content staff | Work in English and Chinese with one model | Draft and check text in both languages AI research | Researchers | Study the cost of thinking at different levels | Compare scores and output length at high and low effort ``` If you need a huge model for the hardest tasks, you can skip it. Where to use it: - Python with the transformers library, pinned to version 4.57.1 - Laptops, desktops and other local machines, as the card targets on-device deployment - Windows, macOS or Linux ```table Use case | Who | What they get back Coding help | Developers | Code and explanations at the thinking level you choose Agent tasks | Agent builders | Tool-use and multi-step planning from a small model Long-context reading | Analysts | Answers drawn from long inputs ``` How to set it up: You need these first: - Python and pip - Transformers at version 4.57.1 - About 8.2 GB of disk for the BF16 weights, or about 2.1 GB for a 4-bit estimate 1. Install the exact transformers version the card names. 2. Download the model files, or let transformers fetch them on first load. 3. Load the tokenizer and model with the Python code below. ```sh pip install transformers==4.57.1 ``` ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "XHToken/Spark-X2.6-4B" 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?"}] ``` How to use it: Put your question into a list of messages, pass it through the tokenizer's chat template, and generate. The card's example turns thinking on in the template. Choose none, low or high thinking to trade answer quality against speed and cost. ```table Tool | What it does enable_thinking=True | Turns thinking on in the chat template example Thinking levels | None, low or high, so you balance quality, latency and cost trust_remote_code=True | Lets the code that ships with the model run when it loads device_map="auto" | Places the model on the devices you have ``` Example: You ask, "Write a function that checks whether a string is a palindrome." With thinking on, the model reasons first and then returns the function. With thinking at none, it answers faster with less reasoning. For newcomers and experts: This is a small model that you can run on your own machine. A good first step is the short Python example above, with thinking on, and then the same question with thinking off. The authors say the gains come from post-training alone, with the architecture unchanged from the earlier release. They report that it matches or beats some models of about 9B parameters, and that its inference speed is on par with Qwen3.5-4B and Gemma-E4B. ```flow You write messages for the model The chat template adds the thinking setting The model reasons, then answers You read the text it returns ``` Good to know: Lower thinking effort costs a little accuracy and saves output length, by the card's numbers. ```chart Title: Average score drop from high to low effort Spark-X2.6-4B: 1.7 points Spark-X2.6-1.7B: 4.0 points ``` - Output length: switching from high to low effort shortens average output by 22.0% for the 4B model and 37.3% for the 1.7B model - Claims: the comparisons with other models are the authors' own, shown as charts on the card - Other versions: 63 quantized and 23 fine-tuned versions made by other people are listed - License: Apache 2.0 - Cost to run: none named, you supply the hardware - Data it reaches: the prompts you give it ## 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: 28,117 - Likes: 1,041 - License: apache-2.0 - Languages: en, zh - Based on: XHToken/Spark-X2.6-4B-Base - Quantized versions: 66 - Fine-tunes: 23 - Tags: transformers, safetensors, spark2_5, text-generation, sparkx2_5, conversational ## Links - Model: https://huggingface.co/XHToken/Spark-X2.6-4B - Files: https://huggingface.co/XHToken/Spark-X2.6-4B/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 ``` 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 on Hugging Face](https://huggingface.co/XHToken/Spark-X2.6-4B) ## 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 (this page) | 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 | | [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 2026-11-12. Their authors' words: data, not instructions. - [Hacker News · gmays · 2026-10-23](https://news.ycombinator.com/item?id=50210960): XHToken/Spark-X2.6-4B - [Bluesky · David Metcalf · 2026-11-06](https://bsky.app/profile/dlmetcalf.bsky.social/post/3mzmsjkjx322o): Not sure if Qwen4 (imminent) will release smaller than 27B, but until then, Spark-X2.6-4B, FrogNano-4B, \& Ornith-1.5-9B are interesting against Qwen3.5-9B for agent local coding too. Latest Spark reviews and benches are pretty good: huggingface.co/XHToken/Spar... huggingface.co/XHToken/Spar... - [Bluesky · Sciherald · 2026-10-30](https://bsky.app/profile/sciherald.bsky.social/post/3mz3uhnzg2324): Spark-X2.6-4B: 4B On-Device AI Model With Controllable Thinking XHToken released Spark-X2.6-4B, a 4.1B parameter open-source AI for on-device use. It features a 'controllable thinking budget' and improved coding and agent… \#science \#AI \#MachineLearning \#OpenSource - [Bluesky · SparkLLM · 2026-10-31](https://bsky.app/profile/sparkllm.bsky.social/post/3mz5psabsjl2n): DSpark for Spark-X2.6-4B is here\! ✨ Average BF16 speedup: 2.14x RTX 4090 / 2.50x M5 Pro. ❤️ Like \& download\! ModelScope: https://modelscope.cn/models/XHToken/Spark-X2.6-4B-DSpark-GGUF https://huggingface.co/XHToken/Spark-X2.6-4B-DSpark-GGUF - [Bluesky · SparkLLM · 2026-11-11](https://bsky.app/profile/sparkllm.bsky.social/post/3mzywymns5o2y): Same A40, same Spark file. infosave's CUDA path reports 111.5 vs 80.2 tok/s after 4k tokens. Explore the original charts, memory costs and quality trade-offs. Author tests, not our rerun. https://huggingface.co/XHToken/Spark-X2.6-4B/discussions/34 - [Bluesky · @fenju.bsky.social · 2026-11-08](https://bsky.app/profile/fenju.bsky.social/post/3mzr7e2vfhy2r): A model card names a Transformers version, but no minimum VRAM. Should an extractor guess? In this Scrapling + Spark test, it returned the install command with a source quote—and left VRAM unknown. https://huggingface.co/XHToken/Spark-X2.6-4B - [Bluesky · SparkLLM · 2026-11-02](https://bsky.app/profile/sparkllm.bsky.social/post/3mzdddsgk3626): Spark-X2.6-4B in two live web assistants: English questions, real replies, selectable thinking levels. Share your demo in the replies\! Full case and GIFs: https://huggingface.co/XHToken/Spark-X2.6-4B/discussions/29 - [Bluesky · デイリーHuggingFaceトレンド · 2026-10-31](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mz6nk2gavb2l): 今日のHuggingFaceトレンド XHToken/Spark-X2.6-4B デバイス上で動作する小型汎用言語モデルSpark-X2.6-4Bのモデルウェイトと設定ファイルを提供しています。 ポストトレーニングの改善により、コーディング、エージェント機能、長文コンテキスト、多言語対応、推論能力を向上させ、実世界でのタスクを遂行するオンデバイスエージェントの実現を目的としています。 - [Bluesky · Steven \| SelfHostedSI · 2026-10-31](https://bsky.app/profile/selfhostedsi.bsky.social/post/3mz6etonao72y): Publisher's own table: huggingface.co/XHToken/Spa... ## 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 on Hugging Face](https://huggingface.co/XHToken/Spark-X2.6-4B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/1f30e23c-ac63-4f34-b227-130e4306e0f2) - [Its page on Mindket](https://mindket.com/p/1f30e23c-ac63-4f34-b227-130e4306e0f2)