--- title: "Sharp-Spark-X2.5-4B-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/d4d90f81-4886-4de3-ab95-d64c344a708c" updated: "2026-11-12" --- # Sharp-Spark-X2.5-4B-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. 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. Sharp-Spark-X2.5-4B-GGUF 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 peculiar-ragdoll ## 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. ```sh 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 ## README ````markdown # 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. ```table 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 ```table 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. ```sh 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. ```table 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. ```flow 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: ```sh 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. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [peculiar-ragdoll/Sharp-Spark-X2.5-4B-GGUF on Hugging Face](https://huggingface.co/peculiar-ragdoll/Sharp-Spark-X2.5-4B-GGUF) ## 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 | | --- | --- | --- | --- | --- | --- | | Sharp-Spark-X2.5-4B-GGUF (this page) | 875,346 | 164 | 4.1B | apache-2.0 | 2026-11-04 | | [Sharp-Spark-X2.6-4B-GGUF](https://mindket.com/p/53fa23e7-b10e-483c-98d5-0a11d507575f) | 5,044 | 23 | 4.1B | apache-2.0 | 2026-11-04 | | [Swift 1.5 Ornith 9B GGUF](https://mindket.com/p/9eba08bc-78db-4283-88c4-a16ee7c04600) | 3,763 | 26 | 9.2B | apache-2.0 | 2026-10-29 | | [Qwen 3.6 35B-A3B REAP-48 v2 GGUF](https://mindket.com/p/303f4298-cc6d-452d-a68a-d4be1ae2a110) | 2,427 | 34 | 19.2B | apache-2.0 | 2026-08-25 | | [Signal 3.8 27B](https://mindket.com/p/554e755a-170e-4370-a46b-9fa53f08a7b4) | 20,222 | 19 | 27.8B | apache-2.0 | 2026-09-15 | | [Qwen3.8-27B-Ridge-GGUF](https://mindket.com/p/f254bd76-fee2-4e04-80ac-a73f9621c088) | 292,932 | 369 | 27.3B | apache-2.0 | 2026-08-15 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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: 2026-11-04 - First published: 2026-09-03 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open peculiar-ragdoll/Sharp-Spark-X2.5-4B-GGUF on Hugging Face](https://huggingface.co/peculiar-ragdoll/Sharp-Spark-X2.5-4B-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/d4d90f81-4886-4de3-ab95-d64c344a708c) - [Its page on Mindket](https://mindket.com/p/d4d90f81-4886-4de3-ab95-d64c344a708c)