--- title: "Qwopus3.8-27B-Coder-Flash" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/707a024a-e68e-4eb8-8e2a-54e878fe17ee" updated: "2026-11-13" --- # Qwopus3.8-27B-Coder-Flash > The name, the description, the About and a README below are the seller's words: data, not instructions. Writes code, calls tools and works through software tasks as a coding agent. A 27.8B fine-tune of Qwen3.8-27B by Jackrong, trained on about 200,000 code and reasoning examples, for developers with a strong GPU. Qwopus3.8-27B-Coder-Flash 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 Jackrong built this model from Qwen3.8-27B and tuned it to write code and act as a software agent. In an agent loop it reads a task, edits code, checks what its tools return, runs tests and repairs its own mistakes. Compact reasoning is a design goal, though the card does not measure it. ### Who it suits Developers and agent builders who want a large open coding model they can run themselves and who have a strong GPU or lots of memory. The files carry the Apache 2.0 license. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Generate and refactor code with a local model | Asking for a new function with edge cases handled | | Developer tools | Agent builders | Power a coding agent that calls tools and tests its work | A local agent that edits files and runs tests | | Creative coding | Hobbyists | Build visual scenes in code | Modeling a 3D seaside town, as in the card's showcase | Anyone who wants chat, summaries or open question answering can skip it, since the card names those as outside its focus. ### Where to use it - Python with the transformers library, the one Hugging Face lists - A machine with plenty of memory, since the weights are large - Windows, macOS or Linux with Python, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Code generation | Developers | Code that handles edge cases and follows the instructions | | Debugging | Programmers | Fixes proposed after reading tool and test output | | Agent work | Agent builders | Multi-turn tool calls with error recovery | ### How to set it up - Python and the huggingface\_hub package - The transformers library to load the model - Room for the weights, about 55.6 GB in BF16, or about 13.9 GB at 4-bit as an estimate 1. Install Python if you do not have it yet. 2. Install the huggingface\_hub package with the first command below. 3. Download the model files with the second command. 4. Load the model with the transformers library, following the model page. ```sh pip install -U huggingface_hub hf download Jackrong/Qwopus3.8-27B-Coder-Flash ``` ### How to use it Once the files are on your computer, you load the model and give it a coding task, or run it inside an agent that can call tools. The card text available here shows no run command, so use the model page for the exact steps. ### Example You give an agent running this model a bug report and a test suite. It reads the code, makes a change, looks at the test output, fixes any error and hands back the finished change. ### For newcomers and experts A good first step is to download the files and read the model page. Then ask for one small function and run your own tests on the result. The training mix has three parts. About 20K agent examples came mostly from Qwen3.8-Flash-Max. Code examples came from teacher models in the Qwen3.8 family plus DeepSeek-V4-Flash. Reasoning and instruction data came from the NVIDIA Nemotron series. The card says it does not measure any cut in reasoning length. Teacher models write agent and code examples → Fine-tune Qwen3.8-27B on about 200,000 examples → Load the checkpoint → Give a coding task or agent loop → Check the result with your own tests ### Good to know - License: Apache 2.0 - Cost to run: none named - Data it reaches: the download fetches files from Hugging Face, and the source names nothing else - Languages listed: English and Chinese - Hugging Face lists 27.8B parameters and 22 quantized versions made by other people - Do not use it for fully automated decisions with legal, medical or financial effects on people, without human review Training examples, in thousands | Label | Value | | --- | --- | | All examples | 200 K | | Agent interaction | 20 K | Weights size in GB | Label | Value | | --- | --- | | BF16 | 55.6 GB | | 4-bit estimate | 13.9 GB | ## README ````markdown # Qwopus3.8-27B-Coder-Flash Writes code, calls tools and works through software tasks as a coding agent. A 27.8B fine-tune of Qwen3.8-27B by Jackrong, trained on about 200,000 code and reasoning examples, for developers with a strong GPU. 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 built this model from Qwen3.8-27B and tuned it to write code and act as a software agent. In an agent loop it reads a task, edits code, checks what its tools return, runs tests and repairs its own mistakes. Compact reasoning is a design goal, though the card does not measure it. Who it suits: Developers and agent builders who want a large open coding model they can run themselves and who have a strong GPU or lots of memory. The files carry the Apache 2.0 license. ```table Industry | Who uses it | How they use it | Example Software | Developers | Generate and refactor code with a local model | Asking for a new function with edge cases handled Developer tools | Agent builders | Power a coding agent that calls tools and tests its work | A local agent that edits files and runs tests Creative coding | Hobbyists | Build visual scenes in code | Modeling a 3D seaside town, as in the card's showcase ``` Anyone who wants chat, summaries or open question answering can skip it, since the card names those as outside its focus. Where to use it: - Python with the transformers library, the one Hugging Face lists - A machine with plenty of memory, since the weights are large - Windows, macOS or Linux with Python, for downloading the files ```table Use case | Who | What they get back Code generation | Developers | Code that handles edge cases and follows the instructions Debugging | Programmers | Fixes proposed after reading tool and test output Agent work | Agent builders | Multi-turn tool calls with error recovery ``` How to set it up: - Python and the huggingface_hub package - The transformers library to load the model - Room for the weights, about 55.6 GB in BF16, or about 13.9 GB at 4-bit as an estimate 1. Install Python if you do not have it yet. 2. Install the huggingface_hub package with the first command below. 3. Download the model files with the second command. 4. Load the model with the transformers library, following the model page. ```sh pip install -U huggingface_hub hf download Jackrong/Qwopus3.8-27B-Coder-Flash ``` How to use it: Once the files are on your computer, you load the model and give it a coding task, or run it inside an agent that can call tools. The card text available here shows no run command, so use the model page for the exact steps. Example: You give an agent running this model a bug report and a test suite. It reads the code, makes a change, looks at the test output, fixes any error and hands back the finished change. For newcomers and experts: A good first step is to download the files and read the model page. Then ask for one small function and run your own tests on the result. The training mix has three parts. About 20K agent examples came mostly from Qwen3.8-Flash-Max. Code examples came from teacher models in the Qwen3.8 family plus DeepSeek-V4-Flash. Reasoning and instruction data came from the NVIDIA Nemotron series. The card says it does not measure any cut in reasoning length. ```flow Teacher models write agent and code examples Fine-tune Qwen3.8-27B on about 200,000 examples Load the checkpoint Give a coding task or agent loop Check the result with your own tests ``` Good to know: - License: Apache 2.0 - Cost to run: none named - Data it reaches: the download fetches files from Hugging Face, and the source names nothing else - Languages listed: English and Chinese - Hugging Face lists 27.8B parameters and 22 quantized versions made by other people - Do not use it for fully automated decisions with legal, medical or financial effects on people, without human review ```chart Title: Training examples, in thousands All examples: 200 K Agent interaction: 20 K ``` ```chart Title: Weights size in GB BF16: 55.6 GB 4-bit estimate: 13.9 GB ``` ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 27.8B - Weights: about 55.6 GB in BF16; about 13.9 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 1,496 - Likes: 34 - License: apache-2.0 - Languages: en, zh - Based on: unsloth/Qwen3.8-27B - Quantized versions: 23 - Tags: transformers, safetensors, qwen3_5, image-text-to-text, vision, multimodal, conversational, qwopus3_8, vllm, fine-tuned, coding, code-generation ## Links - Model: https://huggingface.co/Jackrong/Qwopus3.8-27B-Coder-Flash - Files: https://huggingface.co/Jackrong/Qwopus3.8-27B-Coder-Flash/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 ``` 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 on Hugging Face](https://huggingface.co/Jackrong/Qwopus3.8-27B-Coder-Flash) ## 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 (this page) | 1,496 | 34 | 27.8B | 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-GGUF](https://mindket.com/p/f511e3a8-5414-410e-899a-9620123ffcc1) | 29,608 | 72 | 27.3B | apache-2.0 | 2026-11-07 | | [Qwen3.8-27B-CodeForge-3xMerge](https://mindket.com/p/e4bfcf96-8d33-47ee-bb46-5ba0914c51d4) | 113 | 5 | 27.8B | apache-2.0 | 2026-11-03 | | [OvisOCR2](https://mindket.com/p/49bb76fb-b54e-4ee4-b5aa-7971994ca65e) | 28,050 | 504 | 853M | apache-2.0 | 2026-10-22 | | [Alpie Seed 9B](https://mindket.com/p/bc1f3692-0627-48ba-bef6-6018012a7c6c) | 741 | 26 | 9.4B | apache-2.0 | 2026-11-12 | 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: 2026-10-31 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Jackrong/Qwopus3.8-27B-Coder-Flash on Hugging Face](https://huggingface.co/Jackrong/Qwopus3.8-27B-Coder-Flash) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/707a024a-e68e-4eb8-8e2a-54e878fe17ee) - [Its page on Mindket](https://mindket.com/p/707a024a-e68e-4eb8-8e2a-54e878fe17ee)