--- title: "Qwopus3.8-27B-Coder-Flash-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/f511e3a8-5414-410e-899a-9620123ffcc1" updated: "2026-11-12" --- # Qwopus3.8-27B-Coder-Flash-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. 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 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by Jackrong ## 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. ```sh 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 ## README ````markdown # 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. ```table 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 ```table 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. ```sh 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. ```flow 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 - Model: https://huggingface.co/Jackrong/Qwopus3.8-27B-Coder-Flash-GGUF - Files: https://huggingface.co/Jackrong/Qwopus3.8-27B-Coder-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-Coder-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-Coder-Flash-GGUF on Hugging Face](https://huggingface.co/Jackrong/Qwopus3.8-27B-Coder-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-Coder-Flash-GGUF (this page) | 29,608 | 72 | 27.3B | apache-2.0 | 2026-11-07 | | [Jackrong\_Qwopus3.8-27B-Coder-Flash-GGUF](https://mindket.com/p/37dd8ad1-d0f9-4209-9a57-af0b1359c4c3) | 3,306 | 5 | 27.3B | apache-2.0 | 2026-11-07 | | [Qwopus3.8-27B-Coder-Flash](https://mindket.com/p/707a024a-e68e-4eb8-8e2a-54e878fe17ee) | 1,496 | 34 | 27.8B | apache-2.0 | 2026-11-07 | | [Qwopus3.8-27B-Flash-GGUF](https://mindket.com/p/509fda01-c99c-450a-8d3e-2444ec0c6af7) | 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 | | [OvisOCR2](https://mindket.com/p/49bb76fb-b54e-4ee4-b5aa-7971994ca65e) | 28,050 | 504 | 853M | apache-2.0 | 2026-10-22 | 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: 2026-11-07 - First published: 2026-10-28 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Jackrong/Qwopus3.8-27B-Coder-Flash-GGUF on Hugging Face](https://huggingface.co/Jackrong/Qwopus3.8-27B-Coder-Flash-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/f511e3a8-5414-410e-899a-9620123ffcc1) - [Its page on Mindket](https://mindket.com/p/f511e3a8-5414-410e-899a-9620123ffcc1)