Qwen3.8-27B Fable-5 Coding Distilled GGUF

Qwen3.8-27B Fable-5 Coding Distilled GGUF

GGUF quantized files of a 27.3B coding-distilled Qwen3.8 model, from 2-bit to 8-bit with an optional multi-modal supplement. They suit developers who run a coding model locally and need a size that fits their memory.

Made by mradermacher 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 quantized versions of Qwen3.8-27B-Fable-5-Coding-Distilled, a 27.3 billion parameter model published by khazarai. The files were made by mradermacher and come in many sizes, so you can trade quality for memory. The name points to a coding focus. The card itself says nothing about how the model was trained or how it performs. Read the base model's page before you rely on it. Two small supplement files are marked as multi-modal.

Who it suits: It suits developers and hobbyists who want a large coding-minded model on their own machine, in a file that fits the memory they have.

Title: Who might use it
Industry | Who uses it | How they use it | Example
Software | Developers | Run a coding model locally, without a hosted service | Ask it to explain or draft a function
Local AI | Hobbyists | Fit a large model onto a home computer | Pick a mid-size build for their memory
Research | Model evaluators | See how quality changes with size | Compare the 2-bit and 8-bit files on the same prompts

If you want the original full-precision weights, you can skip these files and go to the base model.

Where to use it: GGUF files load in tools that support the GGUF format, such as llama.cpp based apps. The card does not name a specific tool, so check that yours handles this model.

• A GGUF-capable runner on Windows, macOS or Linux
• Python with pip, to download the files with the Hugging Face tools
• A machine with memory for the build you choose

Title: Jobs it does
Use case | Who | What they get back
Code help | Developers | Drafts and explanations from a model on their own hardware
Size comparison | Tinkerers | A way to see the quality and memory trade-off
Multi-modal tests | Experimenters | Image input, if the supplement file works with your tool

How to set it up: You need Python with pip to fetch the files and enough memory for the build you pick. The main files in the card run from 11.0 GB to 29.1 GB. The card gives no run commands, so follow your runner's own guide for GGUF files.

• Python with pip
• A GGUF-capable runner
• Memory for the build you choose, from 11.0 GB to 29.1 GB

1. Install the Hugging Face download tool.
2. Download the repository, or only the file you want.
3. Load the file in your runner.

pip install -U huggingface_hub
hf download mradermacher/Qwen3.8-27B-Fable-5-Coding-Distilled-GGUF

How to use it: Pick a build from the card's table and load that file in your runner. The card marks the 4-bit builds Q4_K_S and Q4_K_M as fast and recommended, and Q8_0 as the best quality. Multi-part files may need joining, and the card points to another author's guide for that. The multi-modal supplement files are separate and sit alongside the main file in tools that support them.

Title: Builds and sizes
Build | Size in GB | Notes
Q2_K | 11.0 | Smallest main file
Q3_K_M | 13.6 | Lower quality
Q4_K_S | 15.9 | Fast, recommended
Q4_K_M | 16.9 | Fast, recommended
Q6_K | 22.5 | Higher quality
Q8_0 | 29.1 | Fast, best quality
mmproj-Q8_0 | 0.7 | Multi-modal supplement

Example: You have about 20 GB of memory to spare, so you choose Q4_K_M at 16.9 GB. You download that single file and load it in your GGUF runner. You ask it to draft a small function, then try Q5_K_M at 19.6 GB if you want more quality.

For newcomers and experts: If you are new, quantizing shrinks a model so it fits in less memory, at some cost in quality. Start with Q4_K_M, since the card recommends it, and move up or down by what your machine can hold.

If you know the field, these are static quants. The card says weighted imatrix quants of the same model sit in a separate repository, and that IQ quants are often preferable to non-IQ quants of similar size. The IQ4_XS file here is 15.5 GB.

You choose a build by memory
You download the GGUF file
Your runner loads it
You prompt the model locally

Good to know: Bigger files keep more quality and need more memory. The card sorts the table by size and says that size is not always the same as quality.

Title: File size by build, GB
Q2_K: 11.0
Q3_K_M: 13.6
Q4_K_M: 16.9
Q5_K_M: 19.6
Q6_K: 22.5
Q8_0: 29.1

• License: Apache 2.0
• Cost to run: none named
• Data it reaches: the model files from Hugging Face, and the prompts you give it
• Not for: anyone who needs full-precision weights, or anyone who needs the card to explain how the model was distilled, which it does not

On Hugging Face, 13 November 2026

• Library: transformers
• Parameters: 27.3B
• Weights: about 13.7 GB at 4-bit (estimate)
• Formats: GGUF
• Downloads in the last 30 days: 894,772
• Likes: 34
• License: apache-2.0
• Languages: en
• Based on: khazarai/Qwen3.8-27B-Fable-5-Coding-Distilled
• Tags: transformers, gguf, coding, agentic-coding, reasoning, distilled, unsloth, conversational

Links

• Model: https://huggingface.co/mradermacher/Qwen3.8-27B-Fable-5-Coding-Distilled-GGUF
• Files: https://huggingface.co/mradermacher/Qwen3.8-27B-Fable-5-Coding-Distilled-GGUF/tree/main

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download mradermacher/Qwen3.8-27B-Fable-5-Coding-Distilled-GGUF

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