Qwen3.8-Flash-Coder APEX-I-MiniPlus V2.1 GGUF

Qwen3.8-Flash-Coder APEX-I-MiniPlus V2.1 GGUF

An English-only coding model in GGUF, a 42.6B mixture-of-experts quant of about 21.8 GB, for code completion, refactoring and agentic tool calls in llama.cpp or LM Studio.

Made by IsValorum 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 is a GGUF quantization of an experimental coding model, made by IsValorum from Jab1718's qwen3.8-flash-coder-85gb-bf16. That upstream model is an experimental pruned slice, with 352 of its 512 routed experts removed using English Python and SWE-bench data.

The result is a coding specialist for English only. The file is about 21.77 GB at 3.45 bits per weight, against 85.30 GB for the uncompressed BF16 version.

Who it suits: Developers who want a local English coding model that fits in a 21 to 22 GB file, including people with a 24GB workstation. It also suits anyone comparing quantization recipes for mixture-of-experts models.

Industry | Who uses it | How they use it | Example
Software | Developers | Complete and refactor code in English | Ask it to tidy a Python function
Agent building | Agent developers | Run a local model for agentic tool calls | Point a coding agent at a llama-server instance
Research | Quantization researchers | Compare a tensor-by-tensor quant with the BF16 baseline | Read the perplexity table for the three releases
Hardware enthusiasts | Workstation owners | Fit a 42.6B model in 21 to 22 GB | Try it with some layers offloaded to system RAM

People who need other languages or general chat can skip it, since the card says output breaks down outside English coding.

Where to use it:

• Stock llama.cpp, through llama-server
• LM Studio
• Windows, macOS or Linux with Python, for downloading the files

The card says it does not work with Strata Engine, because of its 160-expert layout.

Use case | Who | What they get back
Code completion in English | Developers | Suggested code for Python and similar tasks
Refactoring | Developers | Cleaner versions of code you paste in
Agentic tool calling | Agent builders | Tool calls from a local coding model

How to set it up: Make room for a file of about 21.77 GB, plus memory for the model to run.

• Python and pip, for the Hugging Face download tools
• Stock llama.cpp with llama-server, or LM Studio
• About 21 to 22 GB of free disk and enough RAM or VRAM to load it

1. Install the huggingface_hub package.
2. Download the files with the hf command.
3. Open the GGUF file in LM Studio, or serve it with llama-server.
4. Read the card's recommended settings before you start.

pip install -U huggingface_hub
hf download IsValorum/Qwen3.8-Flash-Coder-APEX-I-MiniPlus-V2.1-GGUF

How to use it: Give it coding tasks in English, such as completing, refactoring or explaining code. The card's contents list names recommended generation parameters, a repeat penalty advisory for coding and a chat template for agentic use with a reasoning effort setting. Those sections are cut off here, so read them on the model page.

Tool | What it does
llama-server | Serves the GGUF file from stock llama.cpp
LM Studio | Loads the GGUF file in a desktop app
hf download | Fetches the files from Hugging Face

Example: You load the file in LM Studio and ask for "Refactor this Python function so it no longer uses nested loops." The model answers with code. According to the card, the same request in Spanish or German would come back as broken text.

For newcomers and experts: Think of this as a coding assistant squeezed into one big file. It only works well in English and only for programming. A good first try is to download it, load it in LM Studio and ask for a small Python change.

Underneath, the upstream model has 48 hybrid layers, 36 linear SSM DeltaNet and 12 sparse full attention, with 160 experts left after pruning. The quantization assigns precision tensor by tensor to hold reasoning quality and routing. The trade-off is a pruned model with no recovery fine-tune yet.

Start from Qwen3.8 Flash Coder
Prune 352 of 512 routed experts
Quantize tensor by tensor to GGUF
Run in llama-server or LM Studio

Good to know: The card compares three releases on WikiText-2 perplexity, where lower is better. The MiniPlus file sits within 0.17% of the BF16 reference.

Title: File size in GB
BF16 reference: 85.30 GB
APEX-I-MiniPlus V2.1: 21.77 GB
APEX-I-NanoPlus: 18.34 GB

Title: WikiText-2 perplexity, lower is better
BF16 reference: 30.0975
APEX-I-MiniPlus V2.1: 30.1495
APEX-I-NanoPlus: 34.4199

• Warning: the card marks this an experimental pre-release, and its pruned experts hurt general chat and other languages
• Size: 42.6B parameters in GGUF format, English listed as the language
• License: Apache 2.0
• Cost to run: no API key or paid service is named, though the author asks for optional donations
• Data it reaches: the files you download from Hugging Face and the prompts you give your local runner
• Not for: languages other than English, general chit-chat, and Strata Engine

On Hugging Face, 13 November 2026

• Library: gguf
• Parameters: 42.6B
• Weights: about 21.3 GB at 4-bit (estimate)
• Formats: GGUF
• Downloads in the last 30 days: 1,585
• Likes: 8
• License: apache-2.0
• Languages: en
• Based on: Jab1718/qwen3.8-flash-coder-85gb-bf16
• Tags: gguf, qwen3.8, llama.cpp, quantized, quantization, apex, apex-quant, apex-i-miniplus, v2.1, qwen, qwen4, qwen4-exp

Links

• Model: https://huggingface.co/IsValorum/Qwen3.8-Flash-Coder-APEX-I-MiniPlus-V2.1-GGUF
• Files: https://huggingface.co/IsValorum/Qwen3.8-Flash-Coder-APEX-I-MiniPlus-V2.1-GGUF/tree/main

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download IsValorum/Qwen3.8-Flash-Coder-APEX-I-MiniPlus-V2.1-GGUF

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