Mindket

Qwopus3.8-27B-Coder-Flash-GGUF

Newversion 1 · updated

Overview

Helps with coding and tool-using agent work as a GGUF fine-tune of Qwen3.8-27B, trained on about 200,000 code, agent and reasoning examples. For developers who run models on their own machine.

Qwopus3.8-27B-Coder-Flash-GGUF is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on . Download it on this page, a zip of its files. Nothing in it runs by itself.

At a glance

You need
  • Python and pip
  • About 13.7 GB of disk space for the 4-bit weights (an estimate)
  • An app or runtime that loads GGUF files
Runs in
  • Windows, macOS or Linux, after you download the files with Python
  • Apps and tools that load GGUF files
  • Coding agent setups that let a model call tools
Start with
pip install -U huggingface_hub
hf download Jackrong/Qwopus3.8-27B-Coder-Flash-GGUF

Platform

WindowsOperating system
macOSOperating system
LinuxOperating system

About

What it is

Jackrong tuned Qwen3.8-27B for code work, and this repository holds the result as GGUF files. It aims at code generation, reasoning and the repeated tool-use steps of real software work. It is tuned to read a request, change code, check tool output, test and fix errors.

Who it suits

Developers who want a coding model they can run on their own computer, and people who build agents that call tools.

IndustryWho uses itHow they use itExample
Software developmentDevelopersAsk for code changes, fixes and refactorsA function rewritten and then tested
Agent toolingBuilders of coding agentsDrive a loop of tool calls and editsAn agent that edits a file and reruns its tests
Visual codingHobbyists and designersGenerate scenes and pages in codeA 3D town or a pagoda scene
Research and educationStudents and learnersWork through algorithm problemsA step by step solution to a sorting task

Anyone with little memory can skip it, since even the 4-bit copy is about 13.7 GB.

Where to use it

  • Windows, macOS or Linux, after you download the files with Python
  • Apps and tools that load GGUF files
  • Coding agent setups that let a model call tools
Use caseWhoWhat they get back
Code generationDevelopersWorking code for the task you describe
Agent workAgent buildersTool calls, edits and a finished result
Visual codingHobbyistsCode that draws a scene such as a pagoda

How to set it up

  • Python and pip
  • About 13.7 GB of disk space for the 4-bit weights (an estimate)
  • An app or runtime that loads GGUF files
  1. Install the Hugging Face hub tools.
  2. Download the model files.
  3. Open them in your GGUF app, following the model card for the run steps.
pip install -U huggingface_hub
hf download Jackrong/Qwopus3.8-27B-Coder-Flash-GGUF

How to use it

Describe the coding task in plain words, such as a change to a file or a feature to build. In an agent setup, the model reads the request, reasons, calls tools, edits, tests, recovers from errors and hands back the result. The card also covers English and Chinese.

Example

You ask it to model a five-story pagoda as a rendered scene. The card shows a test like this, with a rendered pagoda picture as the output. A tool-using setup would also let it run what it wrote and fix errors.

For newcomers and experts

If you are new to local models, download the files, load them in a GGUF-capable app and start with a small request, such as a short function with a test. Ask it to explain what it changed.

The model is a fine-tune of unsloth/Qwen3.8-27B, with 27.3B parameters and a card that lists English and Chinese. Training used about 200,000 examples. About 20,000 of them are agent interactions, mostly distilled from Qwen3.8-Flash-Max. The rest cover algorithms, code generation, reasoning and instruction following.

  1. Read the request and the code
  2. Reason about the change
  3. Call tools and edit files
  4. Test and recover from errors
  5. Deliver the finished result

Good to know

  • Speed claims: the card says its tests do not measure reasoning length, so do not read it as a proven speedup
  • Benchmarks: the card says its test sets and settings differ, so its rows are not a controlled ranking
  • Size: the 4-bit copy is about 13.7 GB, an estimate
  • License: Apache-2.0
  • Cost to run: no API key or paid service is named, only your own hardware
  • Data it reaches: downloading fetches model files, and what a tool-using setup reaches depends on the tools you give it
  • Not for: anything the card does not claim, such as a proven reduction in reasoning tokens

README and files

README

Qwopus3.8-27B-Coder-Flash-GGUF

Helps with coding and tool-using agent work as a GGUF fine-tune of Qwen3.8-27B, trained on about 200,000 code, agent and reasoning examples. For developers who run models on their own machine.

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: Jackrong tuned Qwen3.8-27B for code work, and this repository holds the result as GGUF files. It aims at code generation, reasoning and the repeated tool-use steps of real software work. It is tuned to read a request, change code, check tool output, test and fix errors.

Who it suits: Developers who want a coding model they can run on their own computer, and people who build agents that call tools.

Industry | Who uses it | How they use it | Example
Software development | Developers | Ask for code changes, fixes and refactors | A function rewritten and then tested
Agent tooling | Builders of coding agents | Drive a loop of tool calls and edits | An agent that edits a file and reruns its tests
Visual coding | Hobbyists and designers | Generate scenes and pages in code | A 3D town or a pagoda scene
Research and education | Students and learners | Work through algorithm problems | A step by step solution to a sorting task

Anyone with little memory can skip it, since even the 4-bit copy is about 13.7 GB.

Where to use it:

  • Windows, macOS or Linux, after you download the files with Python
  • Apps and tools that load GGUF files
  • Coding agent setups that let a model call tools
Use case | Who | What they get back
Code generation | Developers | Working code for the task you describe
Agent work | Agent builders | Tool calls, edits and a finished result
Visual coding | Hobbyists | Code that draws a scene such as a pagoda

How to set it up:

  • Python and pip
  • About 13.7 GB of disk space for the 4-bit weights (an estimate)
  • An app or runtime that loads GGUF files
  1. Install the Hugging Face hub tools.
  2. Download the model files.
  3. Open them in your GGUF app, following the model card for the run steps.
pip install -U huggingface_hub
hf download Jackrong/Qwopus3.8-27B-Coder-Flash-GGUF

How to use it: Describe the coding task in plain words, such as a change to a file or a feature to build. In an agent setup, the model reads the request, reasons, calls tools, edits, tests, recovers from errors and hands back the result. The card also covers English and Chinese.

Example: You ask it to model a five-story pagoda as a rendered scene. The card shows a test like this, with a rendered pagoda picture as the output. A tool-using setup would also let it run what it wrote and fix errors.

For newcomers and experts: If you are new to local models, download the files, load them in a GGUF-capable app and start with a small request, such as a short function with a test. Ask it to explain what it changed.

The model is a fine-tune of unsloth/Qwen3.8-27B, with 27.3B parameters and a card that lists English and Chinese. Training used about 200,000 examples. About 20,000 of them are agent interactions, mostly distilled from Qwen3.8-Flash-Max. The rest cover algorithms, code generation, reasoning and instruction following.

Read the request and the code
Reason about the change
Call tools and edit files
Test and recover from errors
Deliver the finished result

Good to know:

  • Speed claims: the card says its tests do not measure reasoning length, so do not read it as a proven speedup
  • Benchmarks: the card says its test sets and settings differ, so its rows are not a controlled ranking
  • Size: the 4-bit copy is about 13.7 GB, an estimate
  • License: Apache-2.0
  • Cost to run: no API key or paid service is named, only your own hardware
  • Data it reaches: downloading fetches model files, and what a tool-using setup reaches depends on the tools you give it
  • Not for: anything the card does not claim, such as a proven reduction in reasoning tokens

On Hugging Face, 13 November 2026

  • Task: image text to text
  • Library: transformers
  • Parameters: 27.3B
  • Weights: about 13.7 GB at 4-bit (estimate)
  • Formats: GGUF
  • Downloads in the last 30 days: 29,608
  • Likes: 72
  • License: apache-2.0
  • Languages: en, zh
  • Based on: unsloth/Qwen3.8-27B
  • Tags: transformers, gguf, llama.cpp, image-text-to-text, vision, multimodal, conversational, qwen3_5, qwopus3_8, vllm, fine-tuned, coding

Links

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download Jackrong/Qwopus3.8-27B-Coder-Flash-GGUF

Then follow the usage in its model card. Nothing in this kit runs by itself.

Files
README.md  5 KB

1 file, 5 KB, downloaded as a zip.

What’s new

  • Version 1 · 2026-11-12

Compare with similar

Listed Hugging Face models in the same category that share the most tags with this one.

ProductDownloads, last 30 daysLikesParametersLicenseLast change
Qwopus3.8-27B-Coder-Flash-GGUF This page29,6087227.3Bapache-2.0
Jackrong_Qwopus3.8-27B-Coder-Flash-GGUFShares fine-tuned · qwopus3_8 · coding3,306527.3Bapache-2.0
Qwopus3.8-27B-Coder-FlashShares fine-tuned · qwopus3_8 · coding1,4963427.8Bapache-2.0
Qwopus3.8-27B-Flash-GGUFShares vision · llama.cpp · multimodal2,153248461Mapache-2.0
Qwopus3.8-27B-Flash-V2-GGUFShares vision · multimodal · qwen3_5897,67575461Mapache-2.0
OvisOCR2Shares vllm · multimodal · qwen3_528,050504853Mapache-2.0
Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13.

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