Sharp-Spark-X2.5-4B-GGUF

Sharp-Spark-X2.5-4B-GGUF

Compressed GGUF copies of the 4B Spark-X2.5 coding model, tuned for long context on GPUs with about 6 GB of memory. Made for developers who want a local coder on small hardware.

Made by peculiar-ragdoll 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: Sharp-Spark-X2.5-4B-GGUF is a set of compressed GGUF files of the Spark-X2.5-4B model from XHToken. That base model has about 4B parameters and is designed for long context. The maker built the importance matrix from security and systems code and added its own chat template. The goal is a local coding model that runs on a small GPU.

Who it suits: Developers with a small graphics card and 16 GB of RAM or less, who want a coding model on their own machine.

Industry | Who uses it | How they use it | Example
Software | Solo developers | Run a coding helper locally on a 6 GB GPU | Ask it to explain or fix a function
Security | Security engineers | Use a model tuned on security and systems code | Review a snippet for risky patterns
Education | Students | Practice coding with a model that fits a modest laptop | Ask for help with a small program
Software | Teams with long files | Feed long context into a small model | Paste a large file and ask about it

If you have more than 16 GB of RAM, the card points to a larger mixture-of-experts coder from the same maker instead.

Where to use it:

• Windows, macOS or Linux, with Python for the download step
• Programs that can load GGUF model files
• GPUs with about 4 to 6 GB of video memory, by quant tier

Use case | Who | What they get back
Local coding help | Developers | Code suggestions and explanations without a cloud service
Long-context work | Developers | A model that keeps usable speed with long prompts, per the card
Small-GPU runs | Hobbyists | A quant sized to a 4, 5 or 6 GB card

How to set it up:

• Python and pip on your computer
• A GPU with about 4 to 6 GB of video memory for the quants listed
• A program that loads GGUF files

1. Install the Hugging Face hub package.
2. Download the repository with the hf command.
3. Pick one quant file, Q6_K_XL when your card has 6 GB or more.
4. Open the model card for the run steps.

pip install -U huggingface_hub
hf download peculiar-ragdoll/Sharp-Spark-X2.5-4B-GGUF

How to use it: Load the quant file in your GGUF-capable program and ask coding questions. The card recommends Q6_K_XL when you can and calls Q4 a fallback. Pick by your video memory.

Tier | Size and card advice
Q4_K_XL | 2.67 GB, for a 4 GB GPU or more context room
Q5_K_XL | 3.24 GB, for a 5 GB GPU
Q6_K_XL | 3.61 GB, for a 6 GB or larger GPU, validated on coding tests

Example: You load the Q6_K_XL file and ask it to find the bug in a function you paste in. It replies with a suggested fix and a short explanation, as any coding model would.

For newcomers and experts: If you are new, download the Q6_K_XL file if your graphics card has 6 GB, then try one short coding question before a long one. A smaller quant loses more quality in a model this size, so the card advises against going below Q4.

If you know the field, the base model uses hybrid attention. Only 9 of its 36 layers keep full attention and the rest use a 512-token window, so the cache grows slowly. The card reports 42 tokens per second decode at 131,072 context. It also reports a tie with the upstream Q8 on a 100-question MMLU-Pro subset.

Spark-X2.5-4B base model
Importance matrix over security and code text
Per-tensor bit allocation
Q4, Q5 or Q6 GGUF files

Good to know:

• License: Apache 2.0
• Cost to run: none named
• Data it reaches: the files you download to your computer
• Not for: quants below Q4, which the card says hurt small models
• Size: Q4_K_XL is 2.67 GB and Q6_K_XL is 3.61 GB

On Hugging Face, 13 November 2026

• Task: text generation
• Library: gguf
• Parameters: 4.1B
• Weights: about 2.1 GB at 4-bit (estimate)
• Formats: GGUF
• Downloads in the last 30 days: 875,346
• Likes: 164
• License: apache-2.0
• Based on: XHToken/Spark-X2.5-4B
• Tags: gguf, llama.cpp, imatrix, spark2_5, long-context, coding, sharp-template, token-efficient, efficient-thinking, text-generation, conversational

Links

• Model: https://huggingface.co/peculiar-ragdoll/Sharp-Spark-X2.5-4B-GGUF
• Files: https://huggingface.co/peculiar-ragdoll/Sharp-Spark-X2.5-4B-GGUF/tree/main

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download peculiar-ragdoll/Sharp-Spark-X2.5-4B-GGUF

Then follow the usage in its model card. Nothing in this kit runs by itself.