Instructions to use mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF
# Run inference directly in the terminal:
llama cli -hf mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF
# Run inference directly in the terminal:
llama cli -hf mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF
# Run inference directly in the terminal:
./build/bin/llama cli -hf mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF

Use Docker

docker model run hf.co/mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF

Llama new
LM Studio
Jan
Ollama
How to use mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF with Ollama:
```
ollama run hf.co/mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF
```
Unsloth Desktop

How to use mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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/Qwopus3.6-35B-A3B-Coder-APEX-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF with Docker Model Runner:
```
docker model run hf.co/mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF
```

Lemonade

How to use mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF

Run and chat with the model

lemonade run user.Qwopus3.6-35B-A3B-Coder-APEX-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mudler/Qwopus3.6-35B-A3B-Coder-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/Qwopus3.6-35B-A3B-Coder-APEX-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

⚡ Each donation = another big MoE quantized

I host 30+ 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

Qwopus3.6-35B-A3B-Coder — APEX GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of Jackrong/Qwopus3.6-35B-A3B-Coder — a Qwen3.6-35B-A3B MoE tuned for coding.

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

This model ships an MTP head — for self-speculative decoding out of the box, see the MTP-bundled repo: mudler/Qwopus3.6-35B-A3B-Coder-APEX-MTP-GGUF.

Available Files

File	Profile	Best For
Qwopus3.6-35B-A3B-Coder-APEX-I-Balanced.gguf	I-Balanced	Best overall — imatrix-enhanced
Qwopus3.6-35B-A3B-Coder-APEX-I-Quality.gguf	I-Quality	Highest quality with imatrix
Qwopus3.6-35B-A3B-Coder-APEX-Quality.gguf	Quality	Highest quality (no imatrix)
Qwopus3.6-35B-A3B-Coder-APEX-Balanced.gguf	Balanced	General purpose
Qwopus3.6-35B-A3B-Coder-APEX-I-Compact.gguf	I-Compact	Consumer GPUs, imatrix-enhanced
Qwopus3.6-35B-A3B-Coder-APEX-Compact.gguf	Compact	Consumer GPUs
Qwopus3.6-35B-A3B-Coder-APEX-I-Mini.gguf	I-Mini	Smallest viable, fastest inference
mmproj.gguf	Vision projector	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 (first/last 5) get higher precision, middle layers get more aggressive compression. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling, agentic traces, Wikipedia).

See the APEX project for full details.

Architecture

Model: Qwopus3.6-35B-A3B-Coder (Qwen3_5MoeForConditionalGeneration, Qwen3.6-35B-A3B base)
Layers: 40 · Experts: 256 routed + 1 shared (8 active) · Total/Active: ~35B / ~3B
Attention: Hybrid (full attention every 4th layer, linear otherwise)
Vision: Built-in vision encoder (mmproj included)
Calibration: v1.3 diverse dataset

Run with LocalAI

local-ai run mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF@Qwopus3.6-35B-A3B-Coder-APEX-I-Balanced.gguf

Credits

APEX is brought to you by the LocalAI team. Built on llama.cpp. Base model by Jackrong.

Downloads last month: 4,947

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Finetuned

unsloth/Qwen3.6-35B-A3B

Adapter

Jackrong/Qwopus3.6-35B-A3B-v1

Adapter

Jackrong/Qwopus3.6-35B-A3B-Coder

Quantized

(21)

this model

Collection including mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF

APEX Quants (GGUF)

Collection

MoE models quantized with the APEX Quantization technique ( https://github.com/mudler/apex-quant ) • 54 items • Updated 12 days ago • 153