Spark-X2.6-1.7B-GGUF

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.

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.

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.

pip install transformers==4.57.1

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.

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

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:

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.