Spark-X2.6-4B

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.

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.

pip install transformers==4.57.1

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.

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:

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.