--- title: "Qwen3.8-Flash-Next-DFlash2-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/f0d509d4-8e38-4f36-8052-59c52155132e" updated: "2026-11-13" --- # Qwen3.8-Flash-Next-DFlash2-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. GGUF draft model that speeds up Qwen3.8-Flash-Next in llama.cpp through speculative decoding, in 334 MiB and 622 MiB files. It is not a standalone model, and it suits people who run that target locally. Qwen3.8-Flash-Next-DFlash2-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 nazeshinjite ## About ### What it is This repository holds GGUF conversions of DFlash 2, a small draft model for Qwen3.8-Flash-Next. It is not a standalone language model. It proposes tokens in advance, and the main Qwen3.8-Flash-Next model checks them, which speeds up generation in llama.cpp. The check is lossless, so greedy output matches the main model exactly. It has 603M parameters. ### Who it suits It suits people who already run Qwen3.8-Flash-Next with llama.cpp and want faster decoding without changing the answers. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Local AI | Mac users running llama.cpp | Add the drafter to speed up decoding | List and code prompts decoded faster on an Apple M5 Max | | Software | Developers | Serve a local model with llama-server | Start the server with a target file and the drafter file | | Research | Inference researchers | Study block-diffusion drafting | Compare it with the model's built-in draft head | If you do not run Qwen3.8-Flash-Next, you can skip it, because the drafter works only with that target. ### Where to use it It works inside llama.cpp, through its llama-server program, on a build at version 0.8.0 or newer. The card measured it on an Apple M5 Max with Metal. - llama.cpp, version 0.8.0 or newer - A Qwen3.8-Flash-Next GGUF file as the main model - Windows, macOS or Linux with the huggingface\_hub package, for the download | Use case | Who | What they get back | | --- | --- | --- | | Faster code answers | Developers | 95.1 tokens per second against 80.5 with the model's own draft head | | Faster list answers | Local users | 127.0 tokens per second against 91.4 | | Image prompts | Local users | Drafting that keeps working when you add a vision file | ### How to set it up You need llama.cpp at version 0.8.0 or newer, a Qwen3.8-Flash-Next GGUF as the main model, and this drafter file. The card says earlier builds abort on the first request. - llama.cpp, version 0.8.0 or newer - A Qwen3.8-Flash-Next GGUF main model - Python and the Hugging Face hub package, for the download 1. Install the Hugging Face hub package. 2. Download the drafter files. 3. Start llama-server with the main model and the drafter. ```sh pip install -U huggingface_hub hf download nazeshinjite/Qwen3.8-Flash-Next-DFlash2-GGUF ``` ### How to use it Start llama-server with the main model and the drafter, and turn on DFlash drafting. The card recommends a draft length of 5 and a probability cutoff of 0.75. Add the vision file option for image prompts. ```bash llama-server \ -m Qwen3.8-Flash-Next-UD-Q5_K_XL-00001-of-00006.gguf \ -md Qwen3.8-Flash-Next-DFlash2-Q4_0.gguf \ --spec-type draft-dflash --spec-draft-n-max 5 --spec-draft-p-min 0.75 \ -c 131072 -np 1 -ub 1024 -ngl 99 -fa on \ --jinja --reasoning-format deepseek \ --temp 1.0 --top-p 0.95 --top-k 20 --min-p 0.0 --presence-penalty 0.0 ``` | Tool | What it does | | --- | --- | | -m | Names the main model file | | -md | Names the drafter file | | --spec-type draft-dflash | Turns on DFlash drafting | | --spec-draft-n-max and --spec-draft-p-min | Set the recommended draft length of 5 and cutoff of 0.75 | | Q4\_0 file, 334 MiB | The recommended drafter | | Q8\_0 file, 622 MiB | A larger drafter with the same acceptance and speed | ### Example You start llama-server with the Q5 main model and the Q4\_0 drafter, then ask it to write code. The card's test on an Apple M5 Max shows code decoding at 95.1 tokens per second against 80.5 with the built-in draft head. The text it writes matches the main model alone. ### For newcomers and experts If you are new to this, picture a fast helper that guesses the next words while the main model checks them. A first small step is to start llama-server with the card's command and ask for code or a list, where the card shows the biggest gains. If you know the field, DFlash 2 is a block-diffusion drafter with 5 layers and a block size of 8. In one pass it drafts a full block, retains the strongest candidates for every slot, and a compact selector chooses a single consistent path through them. On disk it is about 6x smaller than the MTP head, but its runtime memory cost is about the same because of a 2.9 GiB compute buffer. Drafter proposes a block of tokens → Selector picks one coherent path → Main model checks the whole path → Accepted tokens are kept as is → Output matches the main model ### Good to know On prose prompts the card shows a small slowdown, 51.9 tokens per second against 53.8. The draft length and probability cutoff are fixed when the server starts, so changing them means a restart. Decode speed in tokens per second, MTP head against DFlash 2 | Label | Value | | --- | --- | | Code with MTP head | 80.5 t/s | | Code with DFlash 2 | 95.1 t/s | | List with MTP head | 91.4 t/s | | List with DFlash 2 | 127.0 t/s | | Geomean with MTP head | 75.1 t/s | | Geomean with DFlash 2 | 87.9 t/s | - License: Apache-2.0 - Cost to run: no API key or paid service is named, and the drafter files are 334 MiB and 622 MiB - Data it reaches: the download comes from Hugging Face, and the model works on your prompts on your own machine - Not for: standalone use, or llama.cpp builds older than version 0.8.0 ## README ````markdown # Qwen3.8-Flash-Next-DFlash2-GGUF GGUF draft model that speeds up Qwen3.8-Flash-Next in llama.cpp through speculative decoding, in 334 MiB and 622 MiB files. It is not a standalone model, and it suits people who run that target locally. Made by nazeshinjite 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 repository holds GGUF conversions of DFlash 2, a small draft model for Qwen3.8-Flash-Next. It is not a standalone language model. It proposes tokens in advance, and the main Qwen3.8-Flash-Next model checks them, which speeds up generation in llama.cpp. The check is lossless, so greedy output matches the main model exactly. It has 603M parameters. Who it suits: It suits people who already run Qwen3.8-Flash-Next with llama.cpp and want faster decoding without changing the answers. ```table Industry | Who uses it | How they use it | Example Local AI | Mac users running llama.cpp | Add the drafter to speed up decoding | List and code prompts decoded faster on an Apple M5 Max Software | Developers | Serve a local model with llama-server | Start the server with a target file and the drafter file Research | Inference researchers | Study block-diffusion drafting | Compare it with the model's built-in draft head ``` If you do not run Qwen3.8-Flash-Next, you can skip it, because the drafter works only with that target. Where to use it: It works inside llama.cpp, through its llama-server program, on a build at version 0.8.0 or newer. The card measured it on an Apple M5 Max with Metal. - llama.cpp, version 0.8.0 or newer - A Qwen3.8-Flash-Next GGUF file as the main model - Windows, macOS or Linux with the huggingface_hub package, for the download ```table Use case | Who | What they get back Faster code answers | Developers | 95.1 tokens per second against 80.5 with the model's own draft head Faster list answers | Local users | 127.0 tokens per second against 91.4 Image prompts | Local users | Drafting that keeps working when you add a vision file ``` How to set it up: You need llama.cpp at version 0.8.0 or newer, a Qwen3.8-Flash-Next GGUF as the main model, and this drafter file. The card says earlier builds abort on the first request. - llama.cpp, version 0.8.0 or newer - A Qwen3.8-Flash-Next GGUF main model - Python and the Hugging Face hub package, for the download 1. Install the Hugging Face hub package. 2. Download the drafter files. 3. Start llama-server with the main model and the drafter. ```sh pip install -U huggingface_hub hf download nazeshinjite/Qwen3.8-Flash-Next-DFlash2-GGUF ``` How to use it: Start llama-server with the main model and the drafter, and turn on DFlash drafting. The card recommends a draft length of 5 and a probability cutoff of 0.75. Add the vision file option for image prompts. ```bash llama-server \ -m Qwen3.8-Flash-Next-UD-Q5_K_XL-00001-of-00006.gguf \ -md Qwen3.8-Flash-Next-DFlash2-Q4_0.gguf \ --spec-type draft-dflash --spec-draft-n-max 5 --spec-draft-p-min 0.75 \ -c 131072 -np 1 -ub 1024 -ngl 99 -fa on \ --jinja --reasoning-format deepseek \ --temp 1.0 --top-p 0.95 --top-k 20 --min-p 0.0 --presence-penalty 0.0 ``` ```table Tool | What it does -m | Names the main model file -md | Names the drafter file --spec-type draft-dflash | Turns on DFlash drafting --spec-draft-n-max and --spec-draft-p-min | Set the recommended draft length of 5 and cutoff of 0.75 Q4_0 file, 334 MiB | The recommended drafter Q8_0 file, 622 MiB | A larger drafter with the same acceptance and speed ``` Example: You start llama-server with the Q5 main model and the Q4_0 drafter, then ask it to write code. The card's test on an Apple M5 Max shows code decoding at 95.1 tokens per second against 80.5 with the built-in draft head. The text it writes matches the main model alone. For newcomers and experts: If you are new to this, picture a fast helper that guesses the next words while the main model checks them. A first small step is to start llama-server with the card's command and ask for code or a list, where the card shows the biggest gains. If you know the field, DFlash 2 is a block-diffusion drafter with 5 layers and a block size of 8. In one pass it drafts a full block, retains the strongest candidates for every slot, and a compact selector chooses a single consistent path through them. On disk it is about 6x smaller than the MTP head, but its runtime memory cost is about the same because of a 2.9 GiB compute buffer. ```flow Drafter proposes a block of tokens Selector picks one coherent path Main model checks the whole path Accepted tokens are kept as is Output matches the main model ``` Good to know: On prose prompts the card shows a small slowdown, 51.9 tokens per second against 53.8. The draft length and probability cutoff are fixed when the server starts, so changing them means a restart. ```chart Title: Decode speed in tokens per second, MTP head against DFlash 2 Code with MTP head: 80.5 t/s Code with DFlash 2: 95.1 t/s List with MTP head: 91.4 t/s List with DFlash 2: 127.0 t/s Geomean with MTP head: 75.1 t/s Geomean with DFlash 2: 87.9 t/s ``` - License: Apache-2.0 - Cost to run: no API key or paid service is named, and the drafter files are 334 MiB and 622 MiB - Data it reaches: the download comes from Hugging Face, and the model works on your prompts on your own machine - Not for: standalone use, or llama.cpp builds older than version 0.8.0 ## On Hugging Face, 13 November 2026 - Task: text generation - Library: gguf - Parameters: 603M - Weights: about 302 MB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 3,960 - Likes: 4 - License: apache-2.0 - Based on: incoai/Qwen3.8-Flash-Next-DFlash2 - Tags: gguf, dflash2, speculative-decoding, block-diffusion, draft-model, llama.cpp, text-generation, conversational ## Links - Model: https://huggingface.co/nazeshinjite/Qwen3.8-Flash-Next-DFlash2-GGUF - Files: https://huggingface.co/nazeshinjite/Qwen3.8-Flash-Next-DFlash2-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download nazeshinjite/Qwen3.8-Flash-Next-DFlash2-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 [nazeshinjite/Qwen3.8-Flash-Next-DFlash2-GGUF on Hugging Face](https://huggingface.co/nazeshinjite/Qwen3.8-Flash-Next-DFlash2-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 | | --- | --- | --- | --- | --- | --- | | Qwen3.8-Flash-Next-DFlash2-GGUF (this page) | 3,960 | 4 | 603M | apache-2.0 | 2026-10-31 | | [Qwen3.8-27B-DFlash2 by incoai](https://mindket.com/p/916f5a21-666c-4741-94ab-62684c958ac0) | 429,580 | 301 | 1.9B | apache-2.0 | 2026-09-17 | | [Qwen3.8-Flash-Next-DFlash2](https://mindket.com/p/395037c6-711b-471c-8552-e9f88ed25ab4) | 58,226 | 84 | 603M | apache-2.0 | 2026-10-19 | | [Qwen3.8-27B-DFlash2](https://mindket.com/p/92c81d30-6c87-4d00-8e96-3e1241439f5d) | 46,660 | 335 | 1.9B | apache-2.0 | 2026-08-19 | | [Flint-DFlash2](https://mindket.com/p/097e9887-da16-4838-9d9a-ac148ae4398b) | 1,651 | 9 | 403M | apache-2.0 | 2026-10-26 | | [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 | 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: 2026-10-31 - First published: 2026-10-29 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open nazeshinjite/Qwen3.8-Flash-Next-DFlash2-GGUF on Hugging Face](https://huggingface.co/nazeshinjite/Qwen3.8-Flash-Next-DFlash2-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/f0d509d4-8e38-4f36-8052-59c52155132e) - [Its page on Mindket](https://mindket.com/p/f0d509d4-8e38-4f36-8052-59c52155132e)