Instructions to use mudler/Qwen3.6-35B-A3B-APEX-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mudler/Qwen3.6-35B-A3B-APEX-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mudler/Qwen3.6-35B-A3B-APEX-GGUF",
	filename="Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-APEX-GGUF
# Run inference directly in the terminal:
llama-cli -hf mudler/Qwen3.6-35B-A3B-APEX-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mudler/Qwen3.6-35B-A3B-APEX-GGUF
# Run inference directly in the terminal:
llama-cli -hf mudler/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-APEX-GGUF
# Run inference directly in the terminal:
./llama-cli -hf mudler/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-APEX-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mudler/Qwen3.6-35B-A3B-APEX-GGUF

Use Docker

docker model run hf.co/mudler/Qwen3.6-35B-A3B-APEX-GGUF

LM Studio
Jan
Ollama
How to use mudler/Qwen3.6-35B-A3B-APEX-GGUF with Ollama:
```
ollama run hf.co/mudler/Qwen3.6-35B-A3B-APEX-GGUF
```

Unsloth Studio new

How to use mudler/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-APEX-GGUF to start chatting

Pi new

How to use mudler/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-APEX-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mudler/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-APEX-GGUF

Run Hermes

hermes

Docker Model Runner
How to use mudler/Qwen3.6-35B-A3B-APEX-GGUF with Docker Model Runner:
```
docker model run hf.co/mudler/Qwen3.6-35B-A3B-APEX-GGUF
```

Lemonade

How to use mudler/Qwen3.6-35B-A3B-APEX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mudler/Qwen3.6-35B-A3B-APEX-GGUF

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-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.

Qwen 3.6 35B-A3B APEX GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of Qwen/Qwen3.6-35B-A3B.

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

Benchmark Results

All benchmarks run with llama.cpp b8797 on NVIDIA GB10 (122 GB VRAM). Perplexity and KL divergence measured on wikitext-2. HellaSwag zero-shot (400 tasks). KL divergence computed against BF16 reference logits.

APEX vs Baselines (unsloth UD quants)

Model	Size	PPL ↓	KL mean ↓	KL median ↓	KL max ↓	HellaSwag ↑
BF16 (reference)	65 GB	6.722	—	—	—	—
Q8_0	35 GB	6.720	0.0059	0.0022	9.72	82.5%
UD-Q5_K_XL	25 GB	6.725	0.0083	0.0030	9.06	82.8%
UD-Q5_K_S	24 GB	6.728	0.0095	0.0035	8.72	82.8%
APEX I-Balanced	24 GB	6.727	0.0103	0.0041	4.53	83.0%
APEX Balanced	24 GB	6.726	0.0117	0.0047	14.14	83.0%
APEX I-Quality	22 GB	6.735	0.0141	0.0054	5.69	82.5%
APEX Quality	22 GB	6.753	0.0155	0.0060	13.01	82.8%
UD-Q4_K_XL	21 GB	6.735	0.0134	0.0050	5.14	82.3%
UD-Q4_K_M	21 GB	6.736	0.0138	0.0054	7.86	83.3%
APEX I-Compact	17 GB	6.857	0.0451	0.0182	8.76	83.5%
APEX Compact	17 GB	6.862	0.0614	0.0261	17.58	83.3%
UD-Q3_K_M	16 GB	6.883	0.0435	0.0163	9.37	82.8%
APEX I-Mini	14 GB	7.238	0.0999	0.0414	9.21	82.8%

Highlights

APEX I-Balanced (24 GB) achieves the lowest KL max (4.53) of any quant tested — even lower than Q8_0 (9.72). The imatrix dramatically reduces worst-case divergence while matching UD-Q5_K_S on perplexity.
At 17 GB, APEX I-Compact beats UD-Q3_K_M (16 GB) on PPL (6.857 vs 6.883) and HellaSwag (83.5% vs 82.8%).
imatrix consistently halves KL max: I-Balanced 4.53 vs Balanced 14.14, I-Quality 5.69 vs Quality 13.01.
APEX I-Mini (14 GB) delivers usable quality (PPL 7.24, HellaSwag 82.8%) in the smallest package.

Available Files

File	Profile	Size	Best For
Qwen3.6-35B-A3B-APEX-I-Balanced.gguf	I-Balanced	24 GB	Best overall — lowest KL max of any quant
Qwen3.6-35B-A3B-APEX-I-Quality.gguf	I-Quality	22 GB	Highest quality with imatrix, 2 GB smaller
Qwen3.6-35B-A3B-APEX-Quality.gguf	Quality	22 GB	Highest quality standard
Qwen3.6-35B-A3B-APEX-Balanced.gguf	Balanced	24 GB	General purpose
Qwen3.6-35B-A3B-APEX-I-Compact.gguf	I-Compact	17 GB	Consumer GPUs, beats UD-Q3_K_M quality
Qwen3.6-35B-A3B-APEX-Compact.gguf	Compact	17 GB	Consumer GPUs
Qwen3.6-35B-A3B-APEX-I-Mini.gguf	I-Mini	14 GB	Smallest viable, fastest inference
mmproj.gguf	Vision projector	~1 GB	Required for image understanding

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, agentic traces, Wikipedia).

The key insight: in MoE models, expert FFN tensors make up the bulk of model weight but only ~8/256 experts activate per token. APEX compresses middle-layer experts more aggressively while preserving edge layers (first/last 5) and keeping attention, SSM/Mamba, and shared expert tensors at higher precision.

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

Architecture

Model: Qwen 3.6 35B-A3B (Qwen/Qwen3.6-35B-A3B)
Layers: 40
Experts: 256 routed + shared (8 active per token)
Total Parameters: ~35B
Active Parameters: ~3B per token
Attention: Hybrid (full attention every 4th layer, linear/Mamba otherwise)
Vision: Built-in vision encoder (mmproj included)
APEX Config: 5+5 symmetric edge gradient across 40 layers
Calibration: v1.3 diverse dataset (chat, code, reasoning, multilingual, tool-calling, Wikipedia)
llama.cpp: Built with b8797

Run with LocalAI

local-ai run mudler/Qwen3.6-35B-A3B-APEX-GGUF@Qwen3.6-35B-A3B-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: 225,509

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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Model tree for mudler/Qwen3.6-35B-A3B-APEX-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

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APEX Quants (GGUF)

Collection

MoE models quantized with the APEX Quantization technique ( https://github.com/mudler/apex-quant ) • 28 items • Updated 6 days ago • 94