Jackrong_Qwopus3.8-27B-Coder-Flash-GGUF

Jackrong_Qwopus3.8-27B-Coder-Flash-GGUF

GGUF quantizations of Jackrong's Qwopus3.8-27B-Coder-Flash, made by bartowski with an importance matrix, in sizes from 17 GB to 55 GB. For running a 27B model locally with llama.cpp.

Made by bartowski 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 quantizations of Qwopus3.8-27B-Coder-Flash, a 27B model by Jackrong. Bartowski made the files with llama.cpp and an importance matrix. Several sizes let you trade file size against quality. The model name suggests coding, though the page does not say so.

Who it suits: People who run large language models on their own computer with llama.cpp or a tool built on it. The page lists English and Chinese as languages, and image input works when you add a separate mmproj file.

Industry | Who uses it | How they use it | Example
Software | Developers | Run a 27B model locally instead of calling a cloud service | Load a mid-size quant and ask it to review a function
Local AI | Enthusiasts | Pick the quant that fits their memory | Choose the file the card suggests when unsure
Research | Model evaluators | Compare quant sizes against quality | Test a large quant against a smaller one on the same prompts
Multimodal work | Tool builders | Use text and image input through the mmproj file | Add the projector file and send a screenshot

You can skip it if you want a hosted chat service or have little memory. Even the smallest file listed is about 17 GB.

Where to use it:

• llama.cpp and tools built on it, on Windows, macOS and Linux
• Python with the Hugging Face tool, for downloading files
• Setups that use speculative decoding, which the card lists as supported through MTP

Use case | Who | What they get back
Local chat | Enthusiasts | Text replies from a 27B model on your machine
Image questions | Developers | Text answers about an image, with the mmproj file added
Faster generation | Power users | Speculative decoding through MTP

How to set it up: Pick one file rather than the whole repository, since the full set is far larger than any one file. Check your memory against the size column below.

• Python and the Hugging Face tool for downloading
• A llama.cpp based program to load the file
• Memory for the quant you choose, from about 17 GB for the card's default pick

1. Install the Hugging Face hub tool.
2. Choose a quant from the file list.
3. Download that file. The command below fetches the whole repository, so narrow it to one file.
4. Load the file in llama.cpp or a similar tool.

pip install -U huggingface_hub
hf download bartowski/Jackrong_Qwopus3.8-27B-Coder-Flash-GGUF

How to use it: The card has its own How to run section, which is cut off in the part shown here. It does give the prompt format. The system message sets the reasoning effort to the highest level, and the assistant turn opens with a think tag.

<|im_start|>system
Reasoning effort is set to xhigh. Please think carefully through the task, validate key assumptions, consider plausible alternatives, and prioritize correctness, consistency, and clarity in the final answer.

{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
<think>

Quant | Size and role
bf16 | 54.66 GB, the full-precision weights, split into parts
Q8_0 | 29.12 GB, the largest quant, with top quality by the card's account
Q6_K_L | 24.96 GB, a larger Q6_K roughly halfway to Q8_0 in size
Q6_K | 23.86 GB, near-perfect quality and a recommended pick
Q6_K_S | 22.86 GB, slightly smaller than Q6_K and also recommended
Q5_K_M | 20.92 GB, high quality and recommended
Q4_K_M | 17.44 GB, the card's pick when you are unsure

Example: You download the Q4_K_M file, load it in a llama.cpp based tool with the prompt format above, and ask for a short function that reverses a string. The model thinks first, then replies with code and an explanation.

For newcomers and experts: A quant is a smaller copy of a model with the numbers stored in fewer bits. If you are unsure, start with Q4_K_M, which the card suggests as a balance of size and speed. Go up to Q6_K if you have memory to spare.

The quants use an importance matrix, and the card notes support for speculative decoding with MTP and for image input with an mmproj file. The source checkpoint is listed at about 28B parameters. Bartowski made the files from the original model by Jackrong.

Pick a quant that fits your memory
Download that one file
Load it in llama.cpp or a similar tool
Send a prompt in the format shown

Title: File size in GB
bf16: 54.66 GB
Q8_0: 29.12 GB
Q6_K: 23.86 GB
Q5_K_M: 20.92 GB
Q4_K_M: 17.44 GB

Good to know:

• The file list shown is cut off, so more quants exist than the ones above.
• The weights at 4-bit are estimated at about 13.7 GB by the page's own record.
• A separate mmproj file is needed for image input.
• License: apache-2.0.
• Cost to run: none named, you use your own hardware.
• Data it reaches: files downloaded from Hugging Face, and the text or images you give it.
• Not for: the card names no limits.

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: 3,306
• Likes: 5
• License: apache-2.0
• Languages: en, zh
• Based on: Jackrong/Qwopus3.8-27B-Coder-Flash
• Tags: transformers, gguf, image-text-to-text, vision, multimodal, conversational, qwen3_5, qwopus3_8, vllm, fine-tuned, coding, code-generation

Links

• Model: https://huggingface.co/bartowski/Jackrong_Qwopus3.8-27B-Coder-Flash-GGUF
• Files: https://huggingface.co/bartowski/Jackrong_Qwopus3.8-27B-Coder-Flash-GGUF/tree/main

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download bartowski/Jackrong_Qwopus3.8-27B-Coder-Flash-GGUF

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