Instructions to use mudler/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-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/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-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/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf mudler/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-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/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf mudler/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-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/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf mudler/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-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/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mudler/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-GGUF:F16

Use Docker

docker model run hf.co/mudler/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-GGUF:F16

LM Studio
Jan
Ollama
How to use mudler/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-GGUF with Ollama:
```
ollama run hf.co/mudler/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-GGUF:F16
```
Unsloth Desktop

How to use mudler/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-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/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-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/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mudler/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-GGUF with Docker Model Runner:
```
docker model run hf.co/mudler/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-GGUF:F16
```

Lemonade

How to use mudler/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mudler/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-GGUF:F16

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use mudler/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-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/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-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/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-GGUF:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mudler/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-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/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-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/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-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"

Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-GGUF / README.md

mudler

Banner: replace em dash with a comma

caa912e verified 24 days ago

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	---
	license: apache-2.0
	base_model: lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled
	tags:
	- gguf
	- quantized
	- apex
	- moe
	- mixture-of-experts
	- qwen3
	- vlm
	- vision
	- reasoning
	- distilled
	- claude-opus
	---




	<!-- 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>  \|
	<a href="https://www.buymeacoffee.com/mudler" style="color: white; text-decoration: underline;">☕ Buy Me a Coffee</a>  \|
	<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>

	# Qwen3.6 35B-A3B — Claude 4.7 Opus Reasoning Distilled — APEX GGUF

	APEX (Adaptive Precision for EXpert Models) quantizations of [lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled](https://huggingface.co/lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled).

	Brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team \| [APEX Project](https://github.com/mudler/apex-quant) \| [Technical Report](https://github.com/mudler/apex-quant/blob/main/paper/APEX_Technical_Report.pdf)

	## Available Files

	\| File \| Profile \| Size \| Best For \|
	\|------\|---------\|------\|----------\|
	\| Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-I-Balanced.gguf \| I-Balanced \| 24 GB \| Best overall quality/size ratio \|
	\| Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-Balanced.gguf \| Balanced \| 24 GB \| General purpose \|
	\| Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-I-Quality.gguf \| I-Quality \| 21 GB \| Highest quality with imatrix \|
	\| Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-Quality.gguf \| Quality \| 21 GB \| Highest quality standard \|
	\| Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-I-Compact.gguf \| I-Compact \| 16 GB \| Consumer GPUs, best quality/size \|
	\| Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-Compact.gguf \| Compact \| 16 GB \| Consumer GPUs \|
	\| Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-I-Mini.gguf \| I-Mini \| 13 GB \| Smallest "safe" tier \|
	\| Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-I-Nano.gguf \| I-Nano \| 11 GB \| Experimental — IQ2_XXS mid-layer experts \|
	\| Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-F16.gguf \| F16 reference \| 65 GB \| Full-precision reference \|
	\| 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](https://github.com/mudler/apex-quant) for full details, technical report, and scripts.

	### Nano (experimental tier)

	The APEX Nano tier pushes mid-layer routed experts to IQ2_XXS (2.06 bpw), near-edge to IQ2_S, edges to Q3_K, with shared experts kept at Q5_K. About 20% smaller than Mini with modest quality cost — viable only on MoE thanks to sparse per-token expert activation. Requires imatrix.

	Benchmarks pending. Feedback welcome.

	## Architecture

	- Model: Qwen3.6 35B-A3B Claude 4.7 Opus Reasoning Distilled
	- Base: Qwen 3.6 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)

	## Run with LocalAI

	```bash
	local-ai run mudler/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-GGUF@Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-I-Balanced.gguf
	```

	## Credits

	- Reasoning distill fine-tune: [lordx64](https://huggingface.co/lordx64)
	- Vision projector (mmproj): [mradermacher](https://huggingface.co/mradermacher)
	- APEX quantization: [LocalAI](https://github.com/mudler/LocalAI) team
	- Built on [llama.cpp](https://github.com/ggerganov/llama.cpp)