--- title: "Sharp-Spark-X2.6-4B-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/53fa23e7-b10e-483c-98d5-0a11d507575f" updated: "2026-11-12" --- # Sharp-Spark-X2.6-4B-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. 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. Sharp-Spark-X2.6-4B-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-X2.6-4B-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. ```bash pip install -U huggingface_hub hf download peculiar-ragdoll/Sharp-Spark-X2.6-4B-GGUF ``` 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 Decode speed of version 2.5 at two context lengths | Label | Value | | --- | --- | | 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 ## README ````markdown # 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. ```table 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 ```table 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. ```bash pip install -U huggingface_hub hf download peculiar-ragdoll/Sharp-Spark-X2.6-4B-GGUF ``` ```table 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. ```flow 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 ```chart 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: ```sh 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. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [peculiar-ragdoll/Sharp-Spark-X2.6-4B-GGUF on Hugging Face](https://huggingface.co/peculiar-ragdoll/Sharp-Spark-X2.6-4B-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-X2.6-4B-GGUF (this page) | 5,044 | 23 | 4.1B | apache-2.0 | 2026-11-04 | | [Sharp-Spark-X2.5-4B-GGUF](https://mindket.com/p/d4d90f81-4886-4de3-ab95-d64c344a708c) | 875,346 | 164 | 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: 2026-11-03 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open peculiar-ragdoll/Sharp-Spark-X2.6-4B-GGUF on Hugging Face](https://huggingface.co/peculiar-ragdoll/Sharp-Spark-X2.6-4B-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/53fa23e7-b10e-483c-98d5-0a11d507575f) - [Its page on Mindket](https://mindket.com/p/53fa23e7-b10e-483c-98d5-0a11d507575f)