Instructions to use mudler/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-APEX-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf mudler/Qwen3.5-35B-A3B-APEX-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mudler/Qwen3.5-35B-A3B-APEX-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf mudler/Qwen3.5-35B-A3B-APEX-GGUF:F16

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/Qwen3.5-35B-A3B-APEX-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf mudler/Qwen3.5-35B-A3B-APEX-GGUF:F16

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/Qwen3.5-35B-A3B-APEX-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mudler/Qwen3.5-35B-A3B-APEX-GGUF:F16

Use Docker

docker model run hf.co/mudler/Qwen3.5-35B-A3B-APEX-GGUF:F16

LM Studio
Jan

vLLM

How to use mudler/Qwen3.5-35B-A3B-APEX-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mudler/Qwen3.5-35B-A3B-APEX-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mudler/Qwen3.5-35B-A3B-APEX-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mudler/Qwen3.5-35B-A3B-APEX-GGUF:F16

Ollama
How to use mudler/Qwen3.5-35B-A3B-APEX-GGUF with Ollama:
```
ollama run hf.co/mudler/Qwen3.5-35B-A3B-APEX-GGUF:F16
```
Unsloth Desktop

How to use mudler/Qwen3.5-35B-A3B-APEX-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mudler/Qwen3.5-35B-A3B-APEX-GGUF:F16

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/Qwen3.5-35B-A3B-APEX-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mudler/Qwen3.5-35B-A3B-APEX-GGUF with Docker Model Runner:
```
docker model run hf.co/mudler/Qwen3.5-35B-A3B-APEX-GGUF:F16
```

Lemonade

How to use mudler/Qwen3.5-35B-A3B-APEX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mudler/Qwen3.5-35B-A3B-APEX-GGUF:F16

Run and chat with the model

lemonade run user.Qwen3.5-35B-A3B-APEX-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use mudler/Qwen3.5-35B-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 serve -hf mudler/Qwen3.5-35B-A3B-APEX-GGUF:F16

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/Qwen3.5-35B-A3B-APEX-GGUF:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mudler/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-APEX-GGUF:F16

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/Qwen3.5-35B-A3B-APEX-GGUF:F16" \
  --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"

mudler commited on Aug 17

Commit

eb83a48

verified ·

1 Parent(s): b261626

Banner: replace em dash with a comma

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README.md +2 -2

README.md CHANGED Viewed

@@ -23,13 +23,13 @@ pipeline_tag: text-generation
 <!-- apex-banner-v2 -->
 <div style="background-color: #f59e0b; color: white; padding: 20px; border-radius: 10px; text-align: center; margin: 20px 0;">
 <h2 style="color: white; margin: 0 0 10px 0;">⚡ Each donation = another big MoE quantized</h2>
-<p style="font-size: 18px; margin: 0 0 15px 0;">I host <b>25+ free APEX MoE quantizations</b> as independent research. My only local hardware is an <b>NVIDIA DGX Spark</b> (122 GB unified memory) — enough for ~30-50B-class MoEs, but <b>bigger ones (200B+) require rented compute</b> on H100/H200/Blackwell, typically $20-100 per quant.<br>If APEX quants are useful to you, your support directly funds those bigger runs.</p>
 <p style="font-size: 20px; margin: 0;">
 <a href="https://www.patreon.com/cw/mudler" style="color: white; text-decoration: underline;">🎉 Patreon (Monthly)</a> &nbsp;|&nbsp;
 <a href="https://www.buymeacoffee.com/mudler" style="color: white; text-decoration: underline;">☕ Buy Me a Coffee</a> &nbsp;|&nbsp;
 <a href="https://github.com/sponsors/mudler" style="color: white; text-decoration: underline;">⭐ GitHub Sponsors</a>
 </p>
-<p style="font-size: 14px; margin: 10px 0 0 0; opacity: 0.9;">💚 Big thanks to Hugging Face for generously donating additional storage — much appreciated.</p>
 </div>
 # Qwen3.5-35B-A3B APEX GGUF -- A Novel MoE-Aware Mixed-Precision Quantization Technique

 <!-- apex-banner-v2 -->
 <div style="background-color: #f59e0b; color: white; padding: 20px; border-radius: 10px; text-align: center; margin: 20px 0;">
 <h2 style="color: white; margin: 0 0 10px 0;">⚡ Each donation = another big MoE quantized</h2>
+<p style="font-size: 18px; margin: 0 0 15px 0;">I host <b>25+ free APEX MoE quantizations</b> as independent research. My only local hardware is an <b>NVIDIA DGX Spark</b> (122 GB unified memory), enough for ~30-50B-class MoEs, but <b>bigger ones (200B+) require rented compute</b> on H100/H200/Blackwell, typically $20-100 per quant.<br>If APEX quants are useful to you, your support directly funds those bigger runs.</p>
 <p style="font-size: 20px; margin: 0;">
 <a href="https://www.patreon.com/cw/mudler" style="color: white; text-decoration: underline;">🎉 Patreon (Monthly)</a> &nbsp;|&nbsp;
 <a href="https://www.buymeacoffee.com/mudler" style="color: white; text-decoration: underline;">☕ Buy Me a Coffee</a> &nbsp;|&nbsp;
 <a href="https://github.com/sponsors/mudler" style="color: white; text-decoration: underline;">⭐ GitHub Sponsors</a>
 </p>
+<p style="font-size: 14px; margin: 10px 0 0 0; opacity: 0.9;">💚 Big thanks to Hugging Face for generously donating additional storage, much appreciated.</p>
 </div>
 # Qwen3.5-35B-A3B APEX GGUF -- A Novel MoE-Aware Mixed-Precision Quantization Technique