Instructions to use mudler/Qwen3-Coder-30B-APEX-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mudler/Qwen3-Coder-30B-APEX-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mudler/Qwen3-Coder-30B-APEX-GGUF",
	filename="Qwen3-Coder-30B-APEX-Balanced.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use mudler/Qwen3-Coder-30B-APEX-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mudler/Qwen3-Coder-30B-APEX-GGUF
# Run inference directly in the terminal:
llama-cli -hf mudler/Qwen3-Coder-30B-APEX-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mudler/Qwen3-Coder-30B-APEX-GGUF
# Run inference directly in the terminal:
llama-cli -hf mudler/Qwen3-Coder-30B-APEX-GGUF

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf mudler/Qwen3-Coder-30B-APEX-GGUF
# Run inference directly in the terminal:
./llama-cli -hf mudler/Qwen3-Coder-30B-APEX-GGUF

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf mudler/Qwen3-Coder-30B-APEX-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mudler/Qwen3-Coder-30B-APEX-GGUF

Use Docker

docker model run hf.co/mudler/Qwen3-Coder-30B-APEX-GGUF

LM Studio
Jan
Ollama
How to use mudler/Qwen3-Coder-30B-APEX-GGUF with Ollama:
```
ollama run hf.co/mudler/Qwen3-Coder-30B-APEX-GGUF
```

Unsloth Studio new

How to use mudler/Qwen3-Coder-30B-APEX-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for mudler/Qwen3-Coder-30B-APEX-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for mudler/Qwen3-Coder-30B-APEX-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mudler/Qwen3-Coder-30B-APEX-GGUF to start chatting

Pi new

How to use mudler/Qwen3-Coder-30B-APEX-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf mudler/Qwen3-Coder-30B-APEX-GGUF

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mudler/Qwen3-Coder-30B-APEX-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mudler/Qwen3-Coder-30B-APEX-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf mudler/Qwen3-Coder-30B-APEX-GGUF

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default mudler/Qwen3-Coder-30B-APEX-GGUF

Run Hermes

hermes

Docker Model Runner
How to use mudler/Qwen3-Coder-30B-APEX-GGUF with Docker Model Runner:
```
docker model run hf.co/mudler/Qwen3-Coder-30B-APEX-GGUF
```

Lemonade

How to use mudler/Qwen3-Coder-30B-APEX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mudler/Qwen3-Coder-30B-APEX-GGUF

Run and chat with the model

lemonade run user.Qwen3-Coder-30B-APEX-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

⚡ Each donation = another big MoE quantized

I host 25+ free APEX MoE quantizations as independent research. My only local hardware is an NVIDIA DGX Spark (122 GB unified memory) — enough for ~30-50B-class MoEs, but bigger ones (200B+) require rented compute on H100/H200/Blackwell, typically $20-100 per quant.
If APEX quants are useful to you, your support directly funds those bigger runs.

🎉 Patreon (Monthly) | ☕ Buy Me a Coffee | ⭐ GitHub Sponsors

💚 Big thanks to Hugging Face for generously donating additional storage — much appreciated.

Qwen3-Coder-30B-A3B APEX GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of Qwen3-Coder-30B-A3B-Instruct.

Brought to you by the LocalAI team | APEX Project | Technical Report

Benchmark Results

All measurements on NVIDIA DGX Spark (GB10, 128 GB VRAM). Perplexity on wikitext-2-raw, context 2048. Accuracy benchmarks via llama.cpp (400 tasks).

Configuration	Size (GB)	Perplexity	KL mean	HellaSwag	Winogrande	MMLU	ARC	TruthfulQA	tg128 (t/s)
Q8_0	30.3	9.537	0.0031	75.8%	68.0%	39.6%	45.8%	30.0%	57.1
APEX I-Balanced	20.8	9.516	0.0074	76.5%	68.3%	40.2%	46.2%	30.4%	68.5
APEX I-Quality	18.1	9.535	0.0108	75.3%	68.5%	39.8%	44.8%	30.5%	74.1
APEX Quality	18.1	9.560	0.0117	75.5%	68.0%	40.1%	44.5%	31.8%	73.7
APEX Balanced	20.5	9.563	0.0083	75.5%	68.5%	39.6%	45.2%	30.5%	68.1
Unsloth Q5_K_S	19.6	9.513	0.0119	75.3%	68.5%	39.8%	45.2%	30.2%	72.2
Unsloth UD-Q4_K_XL	16.5	9.676	0.0246	76.3%	67.0%	39.7%	47.5%	30.5%	82.3
APEX I-Compact	13.8	9.667	0.0418	76.3%	68.8%	39.0%	44.1%	29.0%	84.5
APEX Compact	13.8	9.765	0.0492	75.0%	67.0%	39.1%	45.8%	30.4%	83.8
APEX Mini	11.3	9.838	0.0862	73.5%	68.8%	39.0%	44.1%	31.0%	91.4

Highlights

APEX I-Balanced beats Q8_0 in PPL (9.516 vs 9.537), HellaSwag (76.5% vs 75.8%), MMLU (40.2% vs 39.6%), and ARC (46.2% vs 45.8%) while being 31% smaller and 20% faster.
APEX I-Compact matches UD-Q4_K_XL quality at 16% less size (13.8 vs 16.5 GB) with higher Winogrande (68.8% vs 67.0%).
APEX Mini (11.3 GB) delivers 91.4 t/s -- fastest of any configuration -- while maintaining viable quality for coding tasks.

Available Files

File	Profile	Size	Best For
Qwen3-Coder-30B-APEX-I-Balanced.gguf	I-Balanced	20.8 GB	Best overall -- beats Q8_0 quality
Qwen3-Coder-30B-APEX-I-Quality.gguf	I-Quality	18.1 GB	Best accuracy with imatrix
Qwen3-Coder-30B-APEX-Quality.gguf	Quality	18.1 GB	Lowest perplexity at this size
Qwen3-Coder-30B-APEX-Balanced.gguf	Balanced	20.5 GB	General purpose, low KL
Qwen3-Coder-30B-APEX-I-Compact.gguf	I-Compact	13.8 GB	Consumer GPUs, best quality at size
Qwen3-Coder-30B-APEX-Compact.gguf	Compact	13.8 GB	Consumer 24 GB GPUs
Qwen3-Coder-30B-APEX-Mini.gguf	Mini	11.3 GB	16 GB VRAM, fastest inference

What is APEX?

APEX is a quantization strategy for Mixture-of-Experts (MoE) models. It classifies tensors by role (routed expert, shared expert, attention) and applies a layer-wise precision gradient -- edge layers get higher precision, middle layers get more aggressive compression. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling -- no Wikipedia).

See the APEX project for full details, technical report, and scripts.

Run with LocalAI

local-ai run mudler/Qwen3-Coder-30B-APEX-GGUF@Qwen3-Coder-30B-APEX-I-Balanced.gguf

Credits

APEX is brought to you by the LocalAI team. Developed through human-driven, AI-assisted research. Built on llama.cpp.

Downloads last month: 4,999

GGUF

Model size

31B params

Architecture

qwen3moe

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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Model tree for mudler/Qwen3-Coder-30B-APEX-GGUF

Base model

Qwen/Qwen3-Coder-30B-A3B-Instruct

Quantized

(141)

this model

Collection including mudler/Qwen3-Coder-30B-APEX-GGUF

APEX Quants (GGUF)

Collection

MoE models quantized with the APEX Quantization technique ( https://github.com/mudler/apex-quant ) • 34 items • Updated 3 days ago • 96