--- title: "Spark-X2.6-1.7B-DSpark-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/2f8fbdb6-3f33-47c4-911e-1169631bdc55" updated: "2026-11-13" --- # Spark-X2.6-1.7B-DSpark-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. A small draft model that speeds up Spark-X2.6-1.7B through speculative decoding, guessing up to seven tokens per step. For people who run that model locally on a llama.cpp build that supports it. Spark-X2.6-1.7B-DSpark-GGUF is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by XHToken ## About ### What it is This is a small draft model in BF16 GGUF format, made to speed up Spark-X2.6-1.7B through speculative decoding. The small model guesses up to seven tokens at once, and the main model checks the guesses together. That means fewer one-by-one steps for the main model. The main model can be in BF16 or in a quantized GGUF. ### Who it suits People who already run Spark-X2.6-1.7B locally and want faster replies, and researchers who study speculative decoding. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers with a local model | Pair the draft with the main model in a server | Serve a faster local chat endpoint | | Local AI | Hobbyists with an NVIDIA GPU | Raise tokens per second on one card | Run a math or coding prompt at higher speed | | Research | Inference researchers | Study how many drafted tokens get accepted | Compare acceptance in thinking and non-thinking modes | You can skip it if you do not run Spark-X2.6-1.7B, since the draft only helps that one target model. ### Where to use it - XHToken's llama.cpp fork, which serves an OpenAI-compatible chat API - Any tool that can call an OpenAI-compatible chat endpoint - Linux with a Bash shell and an NVIDIA GPU, which is what the card's commands target | Use case | Who | What they get back | | --- | --- | --- | | Faster local chat | App developers | The same style of reply, produced with fewer main-model steps | | Benchmarking | Researchers | Acceptance length and tokens per second with and without the draft | | On-device agents | Builders of small agents | Quicker generation from a 1.7B model | ### How to set it up You need the fork of llama.cpp, a build with CUDA, the main model file and this draft file. The card says support in the main llama.cpp project is still in progress. The sample steps assume a Linux machine with an NVIDIA card. - Git, CMake and a CUDA toolchain - An NVIDIA GPU - Python and pip, to download the files - The main model, Spark-X2.6-1.7B-GGUF, as well as this draft 1. Clone XHToken's llama.cpp fork and move into the folder. 2. Build llama-server with CUDA on, using the commands below. 3. Download both model files into a models folder. ```bash cd llama.cpp cmake -S . -B build-cuda -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=ON cmake --build build-cuda --target llama-server -j8 ``` To fetch this repository's files yourself, Hugging Face gives these steps: ```sh pip install -U huggingface_hub hf download XHToken/Spark-X2.6-1.7B-DSpark-GGUF ``` ### How to use it Start llama-server with the main model and this draft, then send chat requests to its OpenAI-compatible route on port 8080. The card names the files Spark-target.gguf and Spark-draft.gguf in its command. ```bash set -o pipefail ./build-cuda/bin/llama-server \ -m models/Spark-target.gguf \ -md models/Spark-draft.gguf \ --alias spark-dspark \ --spec-type draft-dspark \ --spec-draft-sampling greedy \ --spec-draft-n-max 7 \ --spec-draft-p-min 0 \ -ngl 999 -ngld 999 \ -ctk f16 -ctv f16 \ -ctkd f16 -ctvd f16 \ -fa on \ -c 8192 -np 1 \ -b 2048 -ub 512 -t 8 \ --jinja \ --host 127.0.0.1 --port 8080 \ 2>&1 | tee dspark-server.log ``` | Option | What it does | | --- | --- | | -m | Loads the main Spark-X2.6-1.7B model | | -md | Loads this draft model | | --spec-type draft-dspark | Turns on DSpark drafting | | --spec-draft-n-max 7 | Lets the draft propose up to seven tokens per pass | | --alias spark-dspark | Sets the model name that requests use | ### Example With the server running, you send a chat request with the model name spark-dspark and one user message, "你好". You set temperature 1.0, top\_p 0.95 and max\_tokens 512, with streaming off. The server returns a normal chat completion. The card shows no sample reply. ### For newcomers and experts If you are new, remember that this file is a helper and cannot chat on its own. Run it only next to the main Spark model, and start with the one server command above. For experts, the draft has 5 sliding-window layers with a 512-token window and a hidden size of 2048. It reads hidden states from target layers 2, 8, 14, 19 and 25 and drafts 7 tokens per forward pass. The acceptance length counts one extra target token, so 8 is the top value. The speed-up shrinks when the main model is quantized. Prompt reaches the server → Draft model guesses up to 7 tokens → Main model checks them together → Accepted tokens are returned ### Good to know Average tokens per second on an RTX 4090, from the card's own tests | Label | Value | | --- | --- | | BF16 alone | 220 tok/s | | BF16 with DSpark | 463 tok/s | | Q4\_K\_M alone | 424 tok/s | | Q4\_K\_M with DSpark | 650 tok/s | - Results: the speed-ups come from the model authors' own tests on an RTX 4090 with llama.cpp and an Apple M5 Pro with MLX - Throughput: on the RTX 4090 with a BF16 target, the average went from 220 to 463 tokens per second - Languages: English and Chinese - Draft size: 345M parameters - License: Apache 2.0 - Cost to run: no API key or paid service named - Data it reaches: downloads files from Hugging Face and the llama.cpp fork, and the sample server listens on 127.0.0.1 only - Not for: use on its own, or with a different main model than Spark-X2.6-1.7B ## README ````markdown # Spark-X2.6-1.7B-DSpark-GGUF A small draft model that speeds up Spark-X2.6-1.7B through speculative decoding, guessing up to seven tokens per step. For people who run that model locally on a llama.cpp build that supports it. 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: This is a small draft model in BF16 GGUF format, made to speed up Spark-X2.6-1.7B through speculative decoding. The small model guesses up to seven tokens at once, and the main model checks the guesses together. That means fewer one-by-one steps for the main model. The main model can be in BF16 or in a quantized GGUF. Who it suits: People who already run Spark-X2.6-1.7B locally and want faster replies, and researchers who study speculative decoding. ```table Industry | Who uses it | How they use it | Example Software | Developers with a local model | Pair the draft with the main model in a server | Serve a faster local chat endpoint Local AI | Hobbyists with an NVIDIA GPU | Raise tokens per second on one card | Run a math or coding prompt at higher speed Research | Inference researchers | Study how many drafted tokens get accepted | Compare acceptance in thinking and non-thinking modes ``` You can skip it if you do not run Spark-X2.6-1.7B, since the draft only helps that one target model. Where to use it: - XHToken's llama.cpp fork, which serves an OpenAI-compatible chat API - Any tool that can call an OpenAI-compatible chat endpoint - Linux with a Bash shell and an NVIDIA GPU, which is what the card's commands target ```table Use case | Who | What they get back Faster local chat | App developers | The same style of reply, produced with fewer main-model steps Benchmarking | Researchers | Acceptance length and tokens per second with and without the draft On-device agents | Builders of small agents | Quicker generation from a 1.7B model ``` How to set it up: You need the fork of llama.cpp, a build with CUDA, the main model file and this draft file. The card says support in the main llama.cpp project is still in progress. The sample steps assume a Linux machine with an NVIDIA card. - Git, CMake and a CUDA toolchain - An NVIDIA GPU - Python and pip, to download the files - The main model, Spark-X2.6-1.7B-GGUF, as well as this draft 1. Clone XHToken's llama.cpp fork and move into the folder. 2. Build llama-server with CUDA on, using the commands below. 3. Download both model files into a models folder. ```bash cd llama.cpp cmake -S . -B build-cuda -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=ON cmake --build build-cuda --target llama-server -j8 ``` To fetch this repository's files yourself, Hugging Face gives these steps: ```sh pip install -U huggingface_hub hf download XHToken/Spark-X2.6-1.7B-DSpark-GGUF ``` How to use it: Start llama-server with the main model and this draft, then send chat requests to its OpenAI-compatible route on port 8080. The card names the files Spark-target.gguf and Spark-draft.gguf in its command. ```bash set -o pipefail ./build-cuda/bin/llama-server \ -m models/Spark-target.gguf \ -md models/Spark-draft.gguf \ --alias spark-dspark \ --spec-type draft-dspark \ --spec-draft-sampling greedy \ --spec-draft-n-max 7 \ --spec-draft-p-min 0 \ -ngl 999 -ngld 999 \ -ctk f16 -ctv f16 \ -ctkd f16 -ctvd f16 \ -fa on \ -c 8192 -np 1 \ -b 2048 -ub 512 -t 8 \ --jinja \ --host 127.0.0.1 --port 8080 \ 2>&1 | tee dspark-server.log ``` ```table Option | What it does -m | Loads the main Spark-X2.6-1.7B model -md | Loads this draft model --spec-type draft-dspark | Turns on DSpark drafting --spec-draft-n-max 7 | Lets the draft propose up to seven tokens per pass --alias spark-dspark | Sets the model name that requests use ``` Example: With the server running, you send a chat request with the model name spark-dspark and one user message, "你好". You set temperature 1.0, top_p 0.95 and max_tokens 512, with streaming off. The server returns a normal chat completion. The card shows no sample reply. For newcomers and experts: If you are new, remember that this file is a helper and cannot chat on its own. Run it only next to the main Spark model, and start with the one server command above. For experts, the draft has 5 sliding-window layers with a 512-token window and a hidden size of 2048. It reads hidden states from target layers 2, 8, 14, 19 and 25 and drafts 7 tokens per forward pass. The acceptance length counts one extra target token, so 8 is the top value. The speed-up shrinks when the main model is quantized. ```flow Prompt reaches the server Draft model guesses up to 7 tokens Main model checks them together Accepted tokens are returned ``` Good to know: ```chart Title: Average tokens per second on an RTX 4090, from the card's own tests BF16 alone: 220 tok/s BF16 with DSpark: 463 tok/s Q4_K_M alone: 424 tok/s Q4_K_M with DSpark: 650 tok/s ``` - Results: the speed-ups come from the model authors' own tests on an RTX 4090 with llama.cpp and an Apple M5 Pro with MLX - Throughput: on the RTX 4090 with a BF16 target, the average went from 220 to 463 tokens per second - Languages: English and Chinese - Draft size: 345M parameters - License: Apache 2.0 - Cost to run: no API key or paid service named - Data it reaches: downloads files from Hugging Face and the llama.cpp fork, and the sample server listens on 127.0.0.1 only - Not for: use on its own, or with a different main model than Spark-X2.6-1.7B ## On Hugging Face, 13 November 2026 - Task: text generation - Library: gguf - Parameters: 345M - Weights: about 173 MB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 969 - Likes: 10 - License: apache-2.0 - Languages: en, zh - Based on: XHToken/Spark-X2.6-1.7B - Tags: gguf, sparkx2_6, dspark, speculative-decoding, draft-model, llama-cpp, text-generation, conversational ## Links - Model: https://huggingface.co/XHToken/Spark-X2.6-1.7B-DSpark-GGUF - Files: https://huggingface.co/XHToken/Spark-X2.6-1.7B-DSpark-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download XHToken/Spark-X2.6-1.7B-DSpark-GGUF ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [XHToken/Spark-X2.6-1.7B-DSpark-GGUF on Hugging Face](https://huggingface.co/XHToken/Spark-X2.6-1.7B-DSpark-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 | | --- | --- | --- | --- | --- | --- | | Spark-X2.6-1.7B-DSpark-GGUF (this page) | 969 | 10 | 345M | apache-2.0 | 2026-11-01 | | [Spark-X2.6-4B-DSpark-GGUF](https://mindket.com/p/4a3bfb05-4f36-41ba-9c15-57a392aba4ca) | 4,008 | 29 | 624M | apache-2.0 | 2026-11-01 | | [MiniCPM-OCR 1.0 DSpark](https://mindket.com/p/f1502294-5394-450f-8715-c1de2343fc68) | 12,549 | 14 | 297M | apache-2.0 | 2026-11-09 | | [Qwen3.8-27B-DFlash2-GGUF by z-lab](https://mindket.com/p/a7a65cb5-6162-4ab9-9b95-df77f2a95e62) | 506,737 | 162 | 1.9B | apache-2.0 | 2026-08-24 | | [Qwen3.8-27B-DSpine-b16-GGUF](https://mindket.com/p/9955f2b3-7961-45ce-b63c-5a324a506318) | 15,022 | 41 | 2B | apache-2.0 | 2026-11-09 | | [Qwen3.8-Flash-Next-DFlash2-GGUF](https://mindket.com/p/f0d509d4-8e38-4f36-8052-59c52155132e) | 3,960 | 4 | 603M | apache-2.0 | 2026-10-31 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · SparkLLM · 2026-11-01](https://bsky.app/profile/sparkllm.bsky.social/post/3mzawowzfyn26): 769 tok/s on RTX 4090\! Spark-X2.6-1.7B DSpark (GSM8K, Q4\_K\_M, llama.cpp). Up to 2.43x speedup (BF16, GSM8K). Like \& download: https://modelscope.cn/models/XHToken/Spark-X2.6-1.7B-DSpark-GGUF https://huggingface.co/XHToken/Spark-X2.6-1.7B-DSpark-GGUF ## 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: 2026-11-01 - First published: 2026-10-30 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open XHToken/Spark-X2.6-1.7B-DSpark-GGUF on Hugging Face](https://huggingface.co/XHToken/Spark-X2.6-1.7B-DSpark-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/2f8fbdb6-3f33-47c4-911e-1169631bdc55) - [Its page on Mindket](https://mindket.com/p/2f8fbdb6-3f33-47c4-911e-1169631bdc55)