Instructions to use mudler/Qwopus3.6-35B-A3B-v1-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-v1-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-v1-APEX-GGUF
# Run inference directly in the terminal:
llama cli -hf mudler/Qwopus3.6-35B-A3B-v1-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-v1-APEX-GGUF
# Run inference directly in the terminal:
llama cli -hf mudler/Qwopus3.6-35B-A3B-v1-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/Qwopus3.6-35B-A3B-v1-APEX-GGUF
# Run inference directly in the terminal:
./llama-cli -hf mudler/Qwopus3.6-35B-A3B-v1-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/Qwopus3.6-35B-A3B-v1-APEX-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mudler/Qwopus3.6-35B-A3B-v1-APEX-GGUF

Use Docker

docker model run hf.co/mudler/Qwopus3.6-35B-A3B-v1-APEX-GGUF

LM Studio
Jan
Ollama
How to use mudler/Qwopus3.6-35B-A3B-v1-APEX-GGUF with Ollama:
```
ollama run hf.co/mudler/Qwopus3.6-35B-A3B-v1-APEX-GGUF
```
Unsloth Desktop

How to use mudler/Qwopus3.6-35B-A3B-v1-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-v1-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-v1-APEX-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mudler/Qwopus3.6-35B-A3B-v1-APEX-GGUF with Docker Model Runner:
```
docker model run hf.co/mudler/Qwopus3.6-35B-A3B-v1-APEX-GGUF
```

Lemonade

How to use mudler/Qwopus3.6-35B-A3B-v1-APEX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mudler/Qwopus3.6-35B-A3B-v1-APEX-GGUF

Run and chat with the model

lemonade run user.Qwopus3.6-35B-A3B-v1-APEX-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use mudler/Qwopus3.6-35B-A3B-v1-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-v1-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-v1-APEX-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mudler/Qwopus3.6-35B-A3B-v1-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-v1-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-v1-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"

Qwopus3.6-35B-A3B-v1-APEX-GGUF / README.md

mudler

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	---
	license: apache-2.0
	base_model: Jackrong/Qwopus3.6-35B-A3B-v1
	tags:
	- gguf
	- quantized
	- apex
	- moe
	- mixture-of-experts
	- qwen3.5
	- reasoning
	- chain-of-thought
	---

	# Qwopus 3.6 35B-A3B v1 APEX GGUF

	APEX (Adaptive Precision for EXpert Models) quantizations of [Jackrong/Qwopus3.6-35B-A3B-v1](https://huggingface.co/Jackrong/Qwopus3.6-35B-A3B-v1).

	Brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team \| [APEX Project](https://github.com/mudler/apex-quant)

	## Available Files

	\| File \| Profile \| Size \| Best For \|
	\|------\|---------\|------\|----------\|
	\| Qwopus3.6-35B-A3B-v1-APEX-I-Quality.gguf \| I-Quality \| 23 GB \| Highest quality with imatrix \|
	\| Qwopus3.6-35B-A3B-v1-APEX-Quality.gguf \| Quality \| 23 GB \| Highest quality standard \|
	\| Qwopus3.6-35B-A3B-v1-APEX-I-Balanced.gguf \| I-Balanced \| 25 GB \| Best overall quality/size ratio \|
	\| Qwopus3.6-35B-A3B-v1-APEX-Balanced.gguf \| Balanced \| 25 GB \| General purpose \|
	\| Qwopus3.6-35B-A3B-v1-APEX-I-Compact.gguf \| I-Compact \| 17 GB \| Consumer GPUs, best quality/size \|
	\| Qwopus3.6-35B-A3B-v1-APEX-Compact.gguf \| Compact \| 17 GB \| Consumer GPUs \|
	\| Qwopus3.6-35B-A3B-v1-APEX-I-Mini.gguf \| I-Mini \| 14 GB \| Smallest viable, 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, agentic traces, Wikipedia).

	See the [APEX project](https://github.com/mudler/apex-quant) for full details.

	## Architecture

	- Base Model: [Jackrong/Qwopus3.6-35B-A3B-v1](https://huggingface.co/Jackrong/Qwopus3.6-35B-A3B-v1)
	- Architecture: Qwen3.5-MoE 35B-A3B
	- Layers: 40
	- Experts: 256 routed (8 active per token)
	- Total Parameters: ~35B
	- Active Parameters: ~3B per token
	- APEX Config: 6+6 symmetric edge gradient across 40 layers
	- Calibration: v1.3 diverse dataset (chat, code, reasoning, tool-calling, multilingual)

	## Run with LocalAI

	```bash
	local-ai run mudler/Qwopus3.6-35B-A3B-v1-APEX-GGUF@Qwopus3.6-35B-A3B-v1-APEX-I-Balanced.gguf
	```

	## Credits

	APEX is brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team. Developed through human-driven, AI-assisted research. Built on [llama.cpp](https://github.com/ggerganov/llama.cpp).