Sharp-Spark-X2.6-4B-GGUF

Sharp-Spark-X2.6-4B-GGUF

Runs a 4B coding model with long context on a graphics card with 6 GB of memory. These GGUF quants of Spark-X2.6-4B come in three sizes and use a coding-weighted importance matrix.

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-X2.6-4B-GGUF is a set of GGUF quantized copies of XHToken/Spark-X2.6-4B, a dense 4B model with long context. It targets coding and fits on small graphics cards.

The quants carry an importance matrix weighted toward cyber and coding material. Bits are assigned tensor by tensor, and the files come with a Sharp-Spark chat template. It succeeds Sharp-Spark-X2.5-4B with the same recipe and new weights.

Who it suits: People who want a local coding model on a small GPU with 16 GB of RAM or less.

Industry | Who uses it | How they use it | Example
Software | Developers with modest hardware | Run a coding model locally | Ask for a fix to a failing function
Security | People who work with cyber topics | Use a model whose quant data leans toward them | Draft a script that parses logs
Research | Agent and benchmark testers | Compare small models on coding tasks | Check solve counts on a task set
Hobby | Local model enthusiasts | Try long-context chats on a 6 GB card | Paste a long file and ask about it

Skip it if you have more than 16 GB of regular RAM, since the card points those users to a larger TielCoder model with partial GPU offloading.

Where to use it:

• Windows, macOS or Linux with Python, to download the files
• A local runner that reads GGUF files
• A GPU with 4, 5 or 6 GB of video memory, depending on the quant

Use case | Who | What they get back
Local coding help | Developers | Code and explanations without a hosted service
Long-context work | People with big files | A 4B model that keeps usable speed at six-figure context
Agent experiments | Agent builders | A small model scored on a software-engineering task set

How to set it up:

• Python and pip to run the download commands
• Enough GPU memory for the quant you choose
• A local runner that can open GGUF files

1. Pick a quant from the table below, with Q6_K_XL preferred when it fits.
2. Install the Hugging Face tools.
3. Download the files from this repository.
4. Open the chosen file in your runner and apply the Sharp-Spark chat template.

pip install -U huggingface_hub
hf download peculiar-ragdoll/Sharp-Spark-X2.6-4B-GGUF

Title: Quant tiers
Quant | Size | Pick it for
Q4_K_XL | 2.68 GB | A 4 GB GPU, or more room for context
Q5_K_XL | 3.25 GB | A 5 GB GPU, a step up from Q4
Q6_K_XL | 3.61 GB | A GPU with 6 GB or more, and the tier that was benchmarked

How to use it: Load the file in your runner and chat or code with it. The source shows no run command, so follow your runner for the exact steps.

Example: You paste a long source file into the chat and ask what a function does. The authors say the model keeps usable speed at six-figure context, and it replies with a text explanation.

For newcomers and experts: If you are new to local models, start with the Q6_K_XL file if your card has 6 GB or more. Load it, send one short coding question, and see how quickly it answers.

If you know the field, Spark uses hybrid attention. Only nine of the 36 layers keep full attention, while 27 look back across a window of 512 tokens, so the cache grows slowly. The card gives about 36 KB per token at f16 and 19 KB at q8_0.

Choose a quant for your GPU memory
Download the GGUF file
Load it with the Sharp-Spark template
Send a coding prompt

Good to know:

• The Q4 and Q5 tiers use the same recipe but were not benchmarked, and the card warns that small models suffer below Q6
• On MMLU-Pro, version 2.6 scored 71.7 percent against 71.3 for the stock Q8_0 while using fewer tokens per question
• On the 17-task software-engineering set it averaged 8.7 solves per seed, and the stock Q8_0 averaged 9.0
• Add up all the effort per solved problem and the stock Q8_0 comes out quicker, with 46 minutes against 57 for this build
• The long-context speed figures were measured on version 2.5 of the same architecture

Title: Decode speed of version 2.5 at two context lengths
Short context of 512 tokens: 111 tok/s
Context of 131,072 tokens: 42 tok/s

• License: Apache 2.0
• Cost to run: none named
• Data it reaches: the files you download and the prompts you give it, with no other access named
• Not for: people with more than 16 GB of RAM, whom the card points to a larger TielCoder model

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: 5,044
• Likes: 23
• License: apache-2.0
• Based on: XHToken/Spark-X2.6-4B
• 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-X2.6-4B-GGUF
• Files: https://huggingface.co/peculiar-ragdoll/Sharp-Spark-X2.6-4B-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-X2.6-4B-GGUF

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