--- title: "Qwopus3.8-27B-Flash-V2-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/f4542d06-f1ba-46a9-8148-58a34e8911b6" updated: "2026-11-13" --- # Qwopus3.8-27B-Flash-V2-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Trims wasted reasoning in long agent tasks, as a GGUF build of the second Flash fine-tune of Qwen3.8-27B with better Python formatting. For people who run agent loops locally. Qwopus3.8-27B-Flash-V2-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-V2, a second round of post-training on Jackrong's Flash model, which sits on Qwen/Qwen3.8-27B. It keeps the goal of the first Flash release: finish demanding tasks with less wasted reasoning. V2 uses different rewards and reinforcement learning methods. ### Who it suits It suits people who run coding or tool-using agents on their own hardware and want shorter reasoning per 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 quick, clean completions | Chat with less waiting between turns | | Research | ML researchers | Study how new rewards change reasoning length | Compare V2 with the first Flash release | | Operations | Platform teams | Serve a model where speed and memory are limited | An internal helper with concise answers | If you need the longest possible reasoning traces, you can skip it, since the aim is shorter ones. ### 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 | Quicker turns with less runaway reasoning | | Python coding | Developers | Code with cleaner formatting, which V2 aims to improve | | 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-V2-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 connect a coding agent and ask it to add a small feature and run its tests. The agent reads the code, thinks, calls tools and edits. The card says V2 should finish with cleaner Python formatting. ### For newcomers and experts An agent calls the model many times, so slow turns pile up. The card gives an example where five extra seconds per turn adds 250 seconds to a 50-turn task. The first small thing to try is the download command above. V2 starts from the first Flash model and applies one more round of post-training with new rewards and different reinforcement learning methods. It inherits the earlier supervised stage, which began with about 1.5 million teacher examples and kept the best 10%. The card stresses that a shorter reasoning trace only helps if the task still finishes correctly. Qwen3.8-27B base model → First Flash release with SFT and reinforcement → New round with different rewards → GGUF files for local runners ### Good to know - Lineage: V2 inherits the data preparation and reasoning reinforcement of the first Flash release. - Training data: the card says part of the inherited mix is agent trajectories and rebuilt traces from closed models, Claude and GPT among them. - Trade-off: the card says efficiency is a trade-off and points to its own benchmark sections for the measured scope. - 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 part of the card shown here names no forbidden uses. ## README ````markdown # Qwopus3.8-27B-Flash-V2-GGUF Trims wasted reasoning in long agent tasks, as a GGUF build of the second Flash fine-tune of Qwen3.8-27B with better Python formatting. For people who run agent loops locally. 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-V2, a second round of post-training on Jackrong's Flash model, which sits on Qwen/Qwen3.8-27B. It keeps the goal of the first Flash release: finish demanding tasks with less wasted reasoning. V2 uses different rewards and reinforcement learning methods. Who it suits: It suits people who run coding or tool-using agents on their own hardware and want shorter reasoning per 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 quick, clean completions | Chat with less waiting between turns Research | ML researchers | Study how new rewards change reasoning length | Compare V2 with the first Flash release Operations | Platform teams | Serve a model where speed and memory are limited | An internal helper with concise answers ``` If you need the longest possible reasoning traces, you can skip it, since the aim is shorter ones. 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 | Quicker turns with less runaway reasoning Python coding | Developers | Code with cleaner formatting, which V2 aims to improve 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-V2-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 connect a coding agent and ask it to add a small feature and run its tests. The agent reads the code, thinks, calls tools and edits. The card says V2 should finish with cleaner Python formatting. For newcomers and experts: An agent calls the model many times, so slow turns pile up. The card gives an example where five extra seconds per turn adds 250 seconds to a 50-turn task. The first small thing to try is the download command above. V2 starts from the first Flash model and applies one more round of post-training with new rewards and different reinforcement learning methods. It inherits the earlier supervised stage, which began with about 1.5 million teacher examples and kept the best 10%. The card stresses that a shorter reasoning trace only helps if the task still finishes correctly. ```flow Qwen3.8-27B base model First Flash release with SFT and reinforcement New round with different rewards GGUF files for local runners ``` Good to know: - Lineage: V2 inherits the data preparation and reasoning reinforcement of the first Flash release. - Training data: the card says part of the inherited mix is agent trajectories and rebuilt traces from closed models, Claude and GPT among them. - Trade-off: the card says efficiency is a trade-off and points to its own benchmark sections for the measured scope. - 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 part of the card shown here names no forbidden uses. ## 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: 897,675 - Likes: 75 - License: apache-2.0 - Languages: en, zh, es, ru, ja - Based on: Jackrong/Qwopus3.8-27B-Flash - Tags: transformers, gguf, image-text-to-text, vision, multimodal, unsloth, conversational, text-generation, qwen3_5, qwen3, qwen, 27b ## Links - Model: https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF - Files: https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-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-V2-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-V2-GGUF on Hugging Face](https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-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-V2-GGUF (this page) | 897,675 | 75 | 461M | apache-2.0 | 2026-09-21 | | [Qwopus3.8-27B-Flash-GGUF](https://mindket.com/p/509fda01-c99c-450a-8d3e-2444ec0c6af7) | 2,153 | 248 | 461M | apache-2.0 | 2026-11-07 | | [Qwen3.8-27B-pi](https://mindket.com/p/8adc35eb-a424-4e6e-99cc-f4ae26715a1a) | 1,857 | 87 | 27.8B | apache-2.0 | 2026-09-30 | | [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 | | [Qwen3.6-27B-GGUF](https://mindket.com/p/91bfefb7-49da-47e4-a52a-f80091e27806) | 227,770 | 978 | 26.9B | apache-2.0 | 2026-04-22 | 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: 2026-09-21 - First published: 2026-09-19 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Jackrong/Qwopus3.8-27B-Flash-V2-GGUF on Hugging Face](https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/f4542d06-f1ba-46a9-8148-58a34e8911b6) - [Its page on Mindket](https://mindket.com/p/f4542d06-f1ba-46a9-8148-58a34e8911b6)