--- title: "Qwopus3.8-27B-Flash-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/509fda01-c99c-450a-8d3e-2444ec0c6af7" updated: "2026-11-13" --- # Qwopus3.8-27B-Flash-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Cuts reasoning time and cost for long agent tasks, as a GGUF build of a Qwen3.8-27B fine-tune. Made for people who run agent loops on their own machine and want faster turns. Qwopus3.8-27B-Flash-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 Jackrong ## About ### What it is This repository holds GGUF files of Qwopus3.8-27B-Flash, a fine-tune of Qwen/Qwen3.8-27B by Jackrong. It aims to keep general ability while lowering the cost and delay of reasoning. That matters most for agents, which call a model many times in a loop. ### Who it suits It suits people who run coding or tool-using agents locally and feel the wait that long reasoning adds to each turn. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Agent builders | Run a loop of reading, thinking, tool calls and edits on a local model | A coding agent that edits and tests files | | AI hobbyists | Local model users | Pick a model tuned for quicker replies | Chat with less waiting between turns | | Research | ML researchers | Study the trade-off between reasoning length and accuracy | Compare it with the base model on your own set | | Operations | Platform teams | Serve a model where speed and memory are tight | An internal assistant with shorter answers | If you need the top benchmark score over speed, you can skip it, since the card reports a lower MMLU-Pro score than the base. ### Where to use it - Windows, macOS or Linux, where you download the files with Python - A program that loads GGUF files - Agent tools that call a local model in a loop | Use case | Who | What they get back | | --- | --- | --- | | Agent loops | Agent builders | Faster turns and less runaway reasoning, per the card | | Coding help | Developers | Code written by a model tuned for efficient completion | | Local chat | Hobbyists | Replies in English, Chinese, Spanish, Russian or Japanese | ### How to set it up You need Python with pip and a program that loads GGUF files. - Python and pip - A GGUF runner of your choice - Disk space for the files, which you should check in the repository's file list 1. Install the Hugging Face client with pip. 2. Download the repository with the hf command. 3. Load one GGUF file in your runner. The text shown here gives no run commands. ```sh pip install -U huggingface_hub hf download Jackrong/Qwopus3.8-27B-Flash-GGUF ``` ### How to use it Once the model is loaded, you chat with it or let an agent tool call it in a loop. The card describes that loop as read, think, call a tool, observe, edit and test. ### Example You point a coding agent at the model and ask it to fix a failing test. The agent reads the code, thinks, runs a tool, looks at the result and edits the file. The card says this should finish with less wasted reasoning. ### For newcomers and experts An agent calls the model again and again, so a few extra seconds per turn add up. The card gives an example where five seconds more per turn makes a 50-turn task wait 250 seconds longer. The first small thing to try is the download command above. Training had two stages. Stage 1 began with about 1.5 million teacher examples, kept the best 10% after cleaning, and scored them on question, reasoning and answer. Stage 2 reinforces reasoning with NVIDIA NeMo-RL and GSPO. The card is open about the trade-off, a lower MMLU-Pro mixed-set score than the base. Qwen3.8-27B base model → Stage 1: filtered supervised fine-tuning → Stage 2: NeMo-RL and GSPO reinforcement → GGUF files for local runners ### Good to know - Card figures: the card reports 12.8% faster decoding and an 80.7% MTP acceptance rate. Treat them as the author's numbers. - Known issue: in some Python coding tasks, the model may write code with wrong indentation. The card says a fix is in progress. - Training data: the card says the mix includes agent trajectories and trace data derived from closed models, and its examples are not disclosed. - Size check: the card describes a 27B model, while Hugging Face's record lists 461M parameters, so check the file sizes before you download. - License: Apache 2.0, as the License line says. - Cost to run: no API key, account or paid service is named. It runs on your own computer. - Data it reaches: the prompts you give your runner, and Hugging Face for the download. - Not for: the card names no forbidden uses. It lists the indentation issue and the lower MMLU-Pro score as limits. ## README ````markdown # Qwopus3.8-27B-Flash-GGUF Cuts reasoning time and cost for long agent tasks, as a GGUF build of a Qwen3.8-27B fine-tune. Made for people who run agent loops on their own machine and want faster turns. Made by Jackrong 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 files of Qwopus3.8-27B-Flash, a fine-tune of Qwen/Qwen3.8-27B by Jackrong. It aims to keep general ability while lowering the cost and delay of reasoning. That matters most for agents, which call a model many times in a loop. Who it suits: It suits people who run coding or tool-using agents locally and feel the wait that long reasoning adds to each turn. ```table Industry | Who uses it | How they use it | Example Software | Agent builders | Run a loop of reading, thinking, tool calls and edits on a local model | A coding agent that edits and tests files AI hobbyists | Local model users | Pick a model tuned for quicker replies | Chat with less waiting between turns Research | ML researchers | Study the trade-off between reasoning length and accuracy | Compare it with the base model on your own set Operations | Platform teams | Serve a model where speed and memory are tight | An internal assistant with shorter answers ``` If you need the top benchmark score over speed, you can skip it, since the card reports a lower MMLU-Pro score than the base. Where to use it: - Windows, macOS or Linux, where you download the files with Python - A program that loads GGUF files - Agent tools that call a local model in a loop ```table Use case | Who | What they get back Agent loops | Agent builders | Faster turns and less runaway reasoning, per the card Coding help | Developers | Code written by a model tuned for efficient completion Local chat | Hobbyists | Replies in English, Chinese, Spanish, Russian or Japanese ``` How to set it up: You need Python with pip and a program that loads GGUF files. - Python and pip - A GGUF runner of your choice - Disk space for the files, which you should check in the repository's file list 1. Install the Hugging Face client with pip. 2. Download the repository with the hf command. 3. Load one GGUF file in your runner. The text shown here gives no run commands. ```sh pip install -U huggingface_hub hf download Jackrong/Qwopus3.8-27B-Flash-GGUF ``` How to use it: Once the model is loaded, you chat with it or let an agent tool call it in a loop. The card describes that loop as read, think, call a tool, observe, edit and test. Example: You point a coding agent at the model and ask it to fix a failing test. The agent reads the code, thinks, runs a tool, looks at the result and edits the file. The card says this should finish with less wasted reasoning. For newcomers and experts: An agent calls the model again and again, so a few extra seconds per turn add up. The card gives an example where five seconds more per turn makes a 50-turn task wait 250 seconds longer. The first small thing to try is the download command above. Training had two stages. Stage 1 began with about 1.5 million teacher examples, kept the best 10% after cleaning, and scored them on question, reasoning and answer. Stage 2 reinforces reasoning with NVIDIA NeMo-RL and GSPO. The card is open about the trade-off, a lower MMLU-Pro mixed-set score than the base. ```flow Qwen3.8-27B base model Stage 1: filtered supervised fine-tuning Stage 2: NeMo-RL and GSPO reinforcement GGUF files for local runners ``` Good to know: - Card figures: the card reports 12.8% faster decoding and an 80.7% MTP acceptance rate. Treat them as the author's numbers. - Known issue: in some Python coding tasks, the model may write code with wrong indentation. The card says a fix is in progress. - Training data: the card says the mix includes agent trajectories and trace data derived from closed models, and its examples are not disclosed. - Size check: the card describes a 27B model, while Hugging Face's record lists 461M parameters, so check the file sizes before you download. - License: Apache 2.0, as the License line says. - Cost to run: no API key, account or paid service is named. It runs on your own computer. - Data it reaches: the prompts you give your runner, and Hugging Face for the download. - Not for: the card names no forbidden uses. It lists the indentation issue and the lower MMLU-Pro score as limits. ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 461M - Weights: about 230 MB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 2,153 - Likes: 248 - License: apache-2.0 - Languages: en, zh, es, ru, ja - Based on: Qwen/Qwen3.8-27B - Quantized versions: 2 - Tags: transformers, gguf, llama.cpp, image-text-to-text, vision, multimodal, unsloth, conversational, text-generation, qwen3_8, qwen3, qwen ## Links - Model: https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-GGUF - Files: https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Jackrong/Qwopus3.8-27B-Flash-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 [Jackrong/Qwopus3.8-27B-Flash-GGUF on Hugging Face](https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-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 | | --- | --- | --- | --- | --- | --- | | Qwopus3.8-27B-Flash-GGUF (this page) | 2,153 | 248 | 461M | apache-2.0 | 2026-11-07 | | [Qwopus3.8-27B-Flash-V2-GGUF](https://mindket.com/p/f4542d06-f1ba-46a9-8148-58a34e8911b6) | 897,675 | 75 | 461M | apache-2.0 | 2026-09-21 | | [Qwen3-14B-GGUF](https://mindket.com/p/7c183f21-1165-41d5-8e54-bd9521dbea0a) | 75,244 | 148 | 14.8B | apache-2.0 | 2025-06-08 | | [Qwen3-30B-A3B-GGUF by unsloth](https://mindket.com/p/7f9e4803-e508-4446-89f0-cc5cc0837876) | 53,664 | 294 | 30.5B | apache-2.0 | 2025-06-08 | | [Qwopus3.8-27B-Coder-Flash-GGUF](https://mindket.com/p/f511e3a8-5414-410e-899a-9620123ffcc1) | 29,608 | 72 | 27.3B | apache-2.0 | 2026-11-07 | | [Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF](https://mindket.com/p/2b5092f0-78de-42d0-ae72-417592fa56e9) | 17,179 | 296 | 34.7B | apache-2.0 | 2026-04-19 | 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. - [Hacker News · kennywinker · 2026-09-08](https://news.ycombinator.com/item?id=49613575): Benchmarking Qwen3.8 27B quantizations: 4-bit holds up, 1-bit collapses: Another one to try: https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-GGUF Runs the 3bit model faster than the 2bit one runs on my old-ass card. Can’t vouch for its intelligence yet, but i suspect whatever loss in smarts it takes is made up for by the extra resolution. ## 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: 2026-11-07 - First published: 2026-09-04 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Jackrong/Qwopus3.8-27B-Flash-GGUF on Hugging Face](https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/509fda01-c99c-450a-8d3e-2444ec0c6af7) - [Its page on Mindket](https://mindket.com/p/509fda01-c99c-450a-8d3e-2444ec0c6af7)