--- title: "Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-NM-DAU-NEO-MAX-MTP-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/3bb11db5-c6b7-47d4-8700-415d983e254c" updated: "2026-11-13" --- # Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-NM-DAU-NEO-MAX-MTP-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Answers questions and reads images with much shorter thinking. GGUF builds of a 27B Qwen 3.8 tune from DavidAU that aims to cut reasoning tokens while keeping accuracy, for people who run models locally. Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-NM-DAU-NEO-MAX-MTP-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 DavidAU ## About ### What it is This is a set of GGUF files for a 26.9B model tuned from Qwen3.8-27B by DavidAU, with help on benchmarking from Nightmedia and on datasets from TeichAI. It was trained with a method the author calls Cold Fusion. The aim is to shrink the model's thinking blocks and speed up generation, MTP included, without hurting its answers. It handles text and images and lists English and Chinese. ### Who it suits People who run large reasoning models locally and want less waiting on long thinking output. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software development | Developers who run local coding and chat models | Swap it in for a regular 27B model to get shorter reasoning | Asking a coding question and getting a shorter thinking block | | Research | People who compare local models | Test its thinking length against the base Qwen3.8-27B | Counting reasoning tokens on the same prompts | | General use | Enthusiasts with a strong graphics card | Chat with it and give it images | Asking a question about a screenshot | Skip it if you have no hardware for a 27B model, or if you want the base model unchanged. ### Where to use it GGUF files load in local apps and runtimes that read that format. The files download on Windows, macOS or Linux. - Local runtimes and apps that load GGUF files - Windows, macOS or Linux: where you can fetch the files with Python - Setups that use MTP for faster token generation | Use case | Who | What they get back | | --- | --- | --- | | Reasoning questions | Developers | Answers with a shorter thinking block | | Image questions | Everyday users | Text replies about a picture | | Model comparison | Researchers | A tuned model to test against base Qwen3.8-27B | ### How to set it up You need Python to download, a GGUF capable app, and room for the quant you pick. At 4-bit the weights come to about 13.4 GB, as an estimate. - Python with pip - A GGUF capable local app - Disk space for one quant file 1. Install the Hugging Face hub library. 2. Download the files, or pick one quant file from the repository. 3. Load it in your app. ```sh pip install -U huggingface_hub hf download DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-NM-DAU-NEO-MAX-MTP-GGUF ``` ### How to use it The card gives no run command in the part shown. Load a quant in your app and prompt it as you would a regular Qwen model. The author says the changes work in all three thinking modes. | Tool | What it does | | --- | --- | | hf download | Fetches the model files to your computer | ### Example You load the model and ask a multi step reasoning question. The author says it thinks for roughly half to a tenth of the tokens the regular Qwen3.8 model uses, then gives the answer. ### For newcomers and experts If you are new to this, a thinking model writes out its reasoning before it answers. This one is tuned to write less of it. Try one question you already tried on another model and compare the length. If you know the field, the author says the Cold Fusion training changes how each sample is trained as the model learns, and that 4-bit keeps about 99 percent of BF16 performance. The author also reports a median cut of about two thirds in thinking size. These are the author's own claims. Qwen3.8-27B base model → Cold Fusion GAIN training → Shorter thinking blocks → GGUF quants for local use ### Good to know The author says checks were run at each stage, ending with side by side human tests against the base model. - License: Apache 2.0 - Cost to run: none named, you supply the hardware - Data it reaches: the card names none - Not for: the card names no exclusions, and it says a larger tune is still planned ## README ````markdown # Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-NM-DAU-NEO-MAX-MTP-GGUF Answers questions and reads images with much shorter thinking. GGUF builds of a 27B Qwen 3.8 tune from DavidAU that aims to cut reasoning tokens while keeping accuracy, for people who run models locally. Made by DavidAU 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 set of GGUF files for a 26.9B model tuned from Qwen3.8-27B by DavidAU, with help on benchmarking from Nightmedia and on datasets from TeichAI. It was trained with a method the author calls Cold Fusion. The aim is to shrink the model's thinking blocks and speed up generation, MTP included, without hurting its answers. It handles text and images and lists English and Chinese. Who it suits: People who run large reasoning models locally and want less waiting on long thinking output. ```table Industry | Who uses it | How they use it | Example Software development | Developers who run local coding and chat models | Swap it in for a regular 27B model to get shorter reasoning | Asking a coding question and getting a shorter thinking block Research | People who compare local models | Test its thinking length against the base Qwen3.8-27B | Counting reasoning tokens on the same prompts General use | Enthusiasts with a strong graphics card | Chat with it and give it images | Asking a question about a screenshot ``` Skip it if you have no hardware for a 27B model, or if you want the base model unchanged. Where to use it: GGUF files load in local apps and runtimes that read that format. The files download on Windows, macOS or Linux. - Local runtimes and apps that load GGUF files - Windows, macOS or Linux: where you can fetch the files with Python - Setups that use MTP for faster token generation ```table Use case | Who | What they get back Reasoning questions | Developers | Answers with a shorter thinking block Image questions | Everyday users | Text replies about a picture Model comparison | Researchers | A tuned model to test against base Qwen3.8-27B ``` How to set it up: You need Python to download, a GGUF capable app, and room for the quant you pick. At 4-bit the weights come to about 13.4 GB, as an estimate. - Python with pip - A GGUF capable local app - Disk space for one quant file 1. Install the Hugging Face hub library. 2. Download the files, or pick one quant file from the repository. 3. Load it in your app. ```sh pip install -U huggingface_hub hf download DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-NM-DAU-NEO-MAX-MTP-GGUF ``` How to use it: The card gives no run command in the part shown. Load a quant in your app and prompt it as you would a regular Qwen model. The author says the changes work in all three thinking modes. ```table Tool | What it does hf download | Fetches the model files to your computer ``` Example: You load the model and ask a multi step reasoning question. The author says it thinks for roughly half to a tenth of the tokens the regular Qwen3.8 model uses, then gives the answer. For newcomers and experts: If you are new to this, a thinking model writes out its reasoning before it answers. This one is tuned to write less of it. Try one question you already tried on another model and compare the length. If you know the field, the author says the Cold Fusion training changes how each sample is trained as the model learns, and that 4-bit keeps about 99 percent of BF16 performance. The author also reports a median cut of about two thirds in thinking size. These are the author's own claims. ```flow Qwen3.8-27B base model Cold Fusion GAIN training Shorter thinking blocks GGUF quants for local use ``` Good to know: The author says checks were run at each stage, ending with side by side human tests against the base model. - License: Apache 2.0 - Cost to run: none named, you supply the hardware - Data it reaches: the card names none - Not for: the card names no exclusions, and it says a larger tune is still planned ## On Hugging Face, 13 November 2026 - Task: image text to text - Parameters: 26.9B - Weights: about 13.4 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 372,115 - Likes: 342 - License: apache-2.0 - Languages: ar, zh, en, fr, de, hi, id, it - Based on: DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1 - Tags: gguf, qwen3_5, unsloth, cold-fusion, finetune, qwen3.8, thinking, reasoning, coder, creative, story, writing ## Links - Model: https://huggingface.co/DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-NM-DAU-NEO-MAX-MTP-GGUF - Files: https://huggingface.co/DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-NM-DAU-NEO-MAX-MTP-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-NM-DAU-NEO-MAX-MTP-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 [DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-NM-DAU-NEO-MAX-MTP-GGUF on Hugging Face](https://huggingface.co/DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-NM-DAU-NEO-MAX-MTP-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 | | --- | --- | --- | --- | --- | --- | | Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-NM-DAU-NEO-MAX-MTP-GGUF (this page) | 372,115 | 342 | 26.9B | apache-2.0 | 2026-11-10 | | [Qwen3.8-27B-Jumbo-Fusion-IQ-747-900-Turbo-Brilliance-50X](https://mindket.com/p/cec9fe48-537a-4249-a748-f1b90b5a077f) | 477,227 | 57 | 27.3B | apache-2.0 | 2026-11-09 | | [MN-Violet-Lotus-12B-GGUF](https://mindket.com/p/a9fe0089-a4a1-438c-93f7-435b1e018461) | 19,093 | 112 | 12.2B | cc-by-4.0 | 2024-11-17 | | [Qwen3.5-4B-3D-Coder-MTP-Repacked](https://mindket.com/p/77df59cf-e130-4402-b6b8-9f9d8b7ed95a) | 6,042 | 16 | 4.7B | apache-2.0 | 2026-11-03 | | [Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-GGUF](https://mindket.com/p/e69239ed-602f-46a8-8c61-e1bb0dd73291) | 39,987 | 188 | 4.2B | apache-2.0 | 2026-04-06 | | [Hermes-4.3-36B-GGUF](https://mindket.com/p/3bbe6dee-1cc7-4847-8e5f-0007d484fb94) | 28,565 | 99 | 36.2B | apache-2.0 | 2025-12-03 | 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: 2026-11-10 - First published: 2026-08-17 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-NM-DAU-NEO-MAX-MTP-GGUF on Hugging Face](https://huggingface.co/DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-NM-DAU-NEO-MAX-MTP-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/3bb11db5-c6b7-47d4-8700-415d983e254c) - [Its page on Mindket](https://mindket.com/p/3bb11db5-c6b7-47d4-8700-415d983e254c)