--- title: "DSpine for Qwen3.8-27B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/36267b34-fc36-443e-a836-d760c0224141" updated: "2026-11-10" --- # DSpine for Qwen3.8-27B > The name, the description, the About and a README below are the seller's words: data, not instructions. A five-layer draft model that guesses 16 tokens in parallel for Qwen3.8-27B, so the large model can check them together. Made for teams that serve that model. DSpine for Qwen3.8-27B is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-10. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by tencent ## About ### What it is DSpine for Qwen3.8-27B is a small draft model from tencent that speeds up how a large model writes text. It guesses a block of 16 upcoming tokens in parallel, and Qwen3.8-27B then checks the guess. This checkpoint has five layers and an 8,192-token sliding window. ### Who it suits It suits teams that serve Qwen3.8-27B and care about how many drafted tokens the large model accepts. It also suits researchers who study parallel drafting. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | AI infrastructure | Teams that host Qwen3.8-27B | Add it as the draft model beside the large model | A serving stack tested with SGLang 0.5.22 | | Research | People studying speculative decoding | Compare block drafting against the DFlash2 baseline | GSM8K runs at temperature 0 and temperature 1 | | Coding agents | Builders of code agents | Draft tokens for long agent traces | SWE-bench Pro and WorkBuddy Code replays | | Office automation | Builders of office agents | Draft tokens for document and office tasks | WorkBuddy Office replays | You can skip it if you do not run Qwen3.8-27B, since this draft model is made for that one target. ### Where to use it It runs next to Qwen3.8-27B inside a model server. - SGLang: the runtime the authors measured, on version 0.5.22 with FlashAttention 3 - Qwen3.8-27B serving setups, since the draft is built for that model - Agent and coding workloads such as WorkBuddy, SWE-bench Pro and τ²-bench, which the authors replayed | Use case | Who | What they get back | | --- | --- | --- | | Maths and code generation | Teams serving the large model | Longer accepted drafts, such as 7.20 on GSM8K against 5.69 for DFlash2 | | Agent sessions | Agent platform builders | Longer accepted drafts across WorkBuddy, SWE-bench Pro and τ²-bench | | Chat | Teams serving general assistants | Longer accepted drafts on MT-Bench, 4.48 against 4.11 | ### How to set it up You pair this checkpoint with its target model in a server that supports the authors' setup. You need: - The Qwen3.8-27B model that this checkpoint drafts for - An SGLang server, tested by the authors on version 0.5.22 - BF16 weights and FlashAttention 3 - Dynamic verification and ReplaySSM, both enabled in the authors' runs 1. Get Qwen3.8-27B, the model this checkpoint drafts for. 2. Download this DSpine checkpoint, which has block size 16. 3. Start SGLang in BF16 with FlashAttention 3, the setup the authors measured. 4. Turn on dynamic verification and ReplaySSM as in their DSpine runs. The DSpine GitHub project is where its code lives. Run these in a terminal: ```sh # Install Hugging Face's command line pip install -U huggingface_hub # Download the model's files into a folder hf download tencent/Qwen3.8-27B-DSpine-b16 --local-dir Qwen3.8-27B-DSpine-b16 ``` ### How to use it You do not prompt this model yourself. It sits inside the server and drafts tokens while Qwen3.8-27B answers your requests. The authors used these sampling settings for their temperature runs: ```text temperature=1.0 top_p=0.95 top_k=20 ``` | Tool | What it does | | --- | --- | | SGLang | The runtime the authors used for their tests | | DSpark | A reference for the dynamic verification used with DSpine | | ReplaySSM | Enabled during the DSpine runs | | DFlash2 | The official draft model with block size 8 that DSpine is compared with | ### Example Run a maths question like those in GSM8K through a Qwen3.8-27B server that uses this draft model. With greedy decoding, the authors measured an acceptance length of 7.20, while DFlash2 reached 5.69. ### For newcomers and experts A large model writes one token at a time, which is slow. A draft model guesses several tokens ahead and the large model checks them together. Acceptance length counts how many drafted tokens it keeps. To try it, compare the same prompts with and without the draft model on your own server. Under the hood, every layer hands the features it predicts for earlier tokens to the positions beside them. Causal links between tokens therefore travel through network depth, so a whole block of 16 can be drafted at once. The trade-off is that the numbers come from one runtime and one concurrency of 8. The draft model reads the context so far → It drafts a block of 16 tokens in parallel → Qwen3.8-27B checks the drafted tokens → Accepted tokens are kept → Drafting restarts from the last kept token ### Good to know - It works only as a draft for Qwen3.8-27B - Five layers, block size 16, 8,192-token sliding window - Tests used SGLang 0.5.22, BF16, FlashAttention 3 and concurrency 8 - General benchmarks ran at xhigh reasoning with at most 4,096 new tokens - At low reasoning, WorkBuddy Code acceptance length rises from 4.19 to 5.08 - License: Apache 2.0 Acceptance length at temperature 0 | Label | Value | | --- | --- | | GSM8K DFlash2 | 5.69 | | GSM8K DSpine | 7.20 | | MATH-500 DFlash2 | 5.49 | | MATH-500 DSpine | 6.49 | | HumanEval DFlash2 | 4.87 | | HumanEval DSpine | 5.71 | | MBPP DFlash2 | 5.04 | | MBPP DSpine | 6.14 | ## README ````markdown # DSpine for Qwen3.8-27B A five-layer draft model that guesses 16 tokens in parallel for Qwen3.8-27B, so the large model can check them together. Made for teams that serve that model. Made by tencent 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: DSpine for Qwen3.8-27B is a small draft model from tencent that speeds up how a large model writes text. It guesses a block of 16 upcoming tokens in parallel, and Qwen3.8-27B then checks the guess. This checkpoint has five layers and an 8,192-token sliding window. Who it suits: It suits teams that serve Qwen3.8-27B and care about how many drafted tokens the large model accepts. It also suits researchers who study parallel drafting. ```table Industry | Who uses it | How they use it | Example AI infrastructure | Teams that host Qwen3.8-27B | Add it as the draft model beside the large model | A serving stack tested with SGLang 0.5.22 Research | People studying speculative decoding | Compare block drafting against the DFlash2 baseline | GSM8K runs at temperature 0 and temperature 1 Coding agents | Builders of code agents | Draft tokens for long agent traces | SWE-bench Pro and WorkBuddy Code replays Office automation | Builders of office agents | Draft tokens for document and office tasks | WorkBuddy Office replays ``` You can skip it if you do not run Qwen3.8-27B, since this draft model is made for that one target. Where to use it: It runs next to Qwen3.8-27B inside a model server. - SGLang: the runtime the authors measured, on version 0.5.22 with FlashAttention 3 - Qwen3.8-27B serving setups, since the draft is built for that model - Agent and coding workloads such as WorkBuddy, SWE-bench Pro and τ²-bench, which the authors replayed ```table Use case | Who | What they get back Maths and code generation | Teams serving the large model | Longer accepted drafts, such as 7.20 on GSM8K against 5.69 for DFlash2 Agent sessions | Agent platform builders | Longer accepted drafts across WorkBuddy, SWE-bench Pro and τ²-bench Chat | Teams serving general assistants | Longer accepted drafts on MT-Bench, 4.48 against 4.11 ``` How to set it up: You pair this checkpoint with its target model in a server that supports the authors' setup. You need: - The Qwen3.8-27B model that this checkpoint drafts for - An SGLang server, tested by the authors on version 0.5.22 - BF16 weights and FlashAttention 3 - Dynamic verification and ReplaySSM, both enabled in the authors' runs 1. Get Qwen3.8-27B, the model this checkpoint drafts for. 2. Download this DSpine checkpoint, which has block size 16. 3. Start SGLang in BF16 with FlashAttention 3, the setup the authors measured. 4. Turn on dynamic verification and ReplaySSM as in their DSpine runs. The DSpine GitHub project is where its code lives. How to use it: You do not prompt this model yourself. It sits inside the server and drafts tokens while Qwen3.8-27B answers your requests. The authors used these sampling settings for their temperature runs: ```text temperature=1.0 top_p=0.95 top_k=20 ``` ```table Tool | What it does SGLang | The runtime the authors used for their tests DSpark | A reference for the dynamic verification used with DSpine ReplaySSM | Enabled during the DSpine runs DFlash2 | The official draft model with block size 8 that DSpine is compared with ``` Example: Run a maths question like those in GSM8K through a Qwen3.8-27B server that uses this draft model. With greedy decoding, the authors measured an acceptance length of 7.20, while DFlash2 reached 5.69. For newcomers and experts: A large model writes one token at a time, which is slow. A draft model guesses several tokens ahead and the large model checks them together. Acceptance length counts how many drafted tokens it keeps. To try it, compare the same prompts with and without the draft model on your own server. Under the hood, every layer hands the features it predicts for earlier tokens to the positions beside them. Causal links between tokens therefore travel through network depth, so a whole block of 16 can be drafted at once. The trade-off is that the numbers come from one runtime and one concurrency of 8. ```flow The draft model reads the context so far It drafts a block of 16 tokens in parallel Qwen3.8-27B checks the drafted tokens Accepted tokens are kept Drafting restarts from the last kept token ``` Good to know: - It works only as a draft for Qwen3.8-27B - Five layers, block size 16, 8,192-token sliding window - Tests used SGLang 0.5.22, BF16, FlashAttention 3 and concurrency 8 - General benchmarks ran at xhigh reasoning with at most 4,096 new tokens - At low reasoning, WorkBuddy Code acceptance length rises from 4.19 to 5.08 - License: Apache 2.0 ```chart Title: Acceptance length at temperature 0 GSM8K DFlash2: 5.69 GSM8K DSpine: 7.20 MATH-500 DFlash2: 5.49 MATH-500 DSpine: 6.49 HumanEval DFlash2: 4.87 HumanEval DSpine: 5.71 MBPP DFlash2: 5.04 MBPP DSpine: 6.14 ``` ## On Hugging Face, 10 November 2026 - Task: text generation - Parameters: 2B - Weights: about 4.1 GB in BF16; about 1 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 1,464 - Likes: 151 - License: apache-2.0 - Based on: Qwen/Qwen3.8-27B - Quantized versions: 4 - Fine-tunes: 1 - Tags: safetensors, qwen3, dspine, speculative-decoding, draft-model, sglang, text-generation ## Links - Model: https://huggingface.co/tencent/Qwen3.8-27B-DSpine-b16 - Files: https://huggingface.co/tencent/Qwen3.8-27B-DSpine-b16/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download tencent/Qwen3.8-27B-DSpine-b16 ``` 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 [tencent/Qwen3.8-27B-DSpine-b16 on Hugging Face](https://huggingface.co/tencent/Qwen3.8-27B-DSpine-b16) ## 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 | | --- | --- | --- | --- | --- | --- | | DSpine for Qwen3.8-27B (this page) | 4,332 | 222 | 2B | apache-2.0 | 2026-11-09 | | [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-DSpine-b16-GGUF](https://mindket.com/p/9955f2b3-7961-45ce-b63c-5a324a506318) | 15,022 | 41 | 2B | apache-2.0 | 2026-11-09 | 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 2026-11-10. Their authors' words: data, not instructions. - [Hacker News · MaKey · 2026-11-08](https://news.ycombinator.com/item?id=50413991): DSpine for Qwen3.8-27B - [Bluesky · UndergroundAI · 2026-11-10](https://bsky.app/profile/undergroundai66.bsky.social/post/3mzwc6cjnyd2z): ⚡ Das neue Sprachmodell auf HuggingFace. 🛠️ Qwen3.8-27B-DSpine-b16 stammt von tencent und begeistert die Community als aktueller Trend auf… 🚀 Entdecke diesen und weitere KI-Geheimtipps bei Underground AI. 👉 https://underground-ai.bolt.app/ \#KuenstlicheIntelligenz \#OpenSource - [Bluesky · labmemo · 2026-11-07](https://bsky.app/profile/labmemo.bsky.social/post/3mzqou6oll52i): Qwen3.8-27B用ドラフトモデル「DSpine-b16」 重みはHugging Faceの tencent/Qwen3.8-27B-DSpine-b16 にあります。 要点を画像にまとめました。 - [Bluesky · deployedbyai · 2026-11-07](https://bsky.app/profile/deployedbyai.bsky.social/post/3mggvfcnsw24m): Tencent: Qwen3.8-27B-DSpine-b16 draft model released, block size 16, 8,192-token sliding window - [Bluesky · AI Planet · 2026-11-08](https://bsky.app/profile/aiplanet.bsky.social/post/3mzt3vx2sfu2f): 📰 Reddit r/LocalLLaMA tencent/Qwen3.8-27B-DSpine-b16 · Hugging Face https://www.reddit.com/r/LocalLLaMA/comments/1xr2aaa/tencentqwen3827bdspineb16\_hugging\_face/ \#IA \#AI \#ML ## 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: 2026-11-09 - First published: 2026-11-07 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open tencent/Qwen3.8-27B-DSpine-b16 on Hugging Face](https://huggingface.co/tencent/Qwen3.8-27B-DSpine-b16) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/36267b34-fc36-443e-a836-d760c0224141) - [Its page on Mindket](https://mindket.com/p/36267b34-fc36-443e-a836-d760c0224141)