Instructions to use FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-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 FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-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 FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-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 FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-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 FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-GGUF:F16

Use Docker

docker model run hf.co/FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-GGUF:F16

LM Studio
Jan

vLLM

How to use FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-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": "FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-GGUF:F16

Ollama
How to use FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-GGUF with Ollama:
```
ollama run hf.co/FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-GGUF:F16
```

Unsloth Studio

How to use FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-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 FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-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 FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-GGUF to start chatting

How to use FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-GGUF:F16

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": "FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-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 "FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-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"

Docker Model Runner
How to use FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-GGUF with Docker Model Runner:
```
docker model run hf.co/FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-GGUF:F16
```

Lemonade

How to use FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-GGUF:F16

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-MXFP4-MOE-Fast-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-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 FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-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 FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-GGUF:F16

Run Hermes

hermes

Atomic Chat

Qwen3.6-35B-A3B-Fast-MXFP4-MOE-GGUF

GGUF MXFP4 MoE quantization of unsloth/Qwen3.6-35B-A3B-NVFP4-Fast, a 35B parameter MoE model with 3B active parameters.

What is the "Fast" Variant?

Unsloth's NVFP4 Fast variant is a speed-optimized quantization that delivers 1.79x faster throughput than other NVFP4 quants. This GGUF extends that optimization to MXFP4 MoE format:

Variant	MMLU-Pro	GPQA	AIME 2025
Unsloth NVFP4 Fast	85.58	87.75	91.67
Unsloth NVFP4	85.85	86.74	92.29
NVIDIA NVFP4	85.60	87.12	91.88
BF16	85.75	86.36	92.50

MXFP4 MoE Format

This quantization uses a hybrid approach for optimal quality:

Expert weights: MXFP4 (E2M1 microscaling, 4-bit)
Non-expert weights (attention, embeddings, norms): Q8_0 (8-bit)

MXFP4 is an open standard supported by AMD, NVIDIA, and Microsoft, making it compatible with a wider range of hardware.

About the Model

Qwen3.6-35B-A3B is a multimodal MoE model from Alibaba's Qwen team:

35B total parameters, 3B active per token (256 experts, 8 active)
40-layer decoder with Gated DeltaNet + full attention hybrid
27-layer vision encoder (SigLIP-based) for image/video understanding
262K native context (extensible to 1M+ via YaRN)
Multi-Token Prediction (MTP) for faster speculative decoding
Agentic coding with SWE-bench Verified 73.4, tool calling support

Files

File	Size	Description
`qwen36-35b-a3b-fast-mxfp4_moe.gguf`	~18.9 GB	MXFP4 MoE quantized text model
`mmproj-qwen36-35b-a3b-f16.gguf`	~0.84 GB	Vision encoder (F16)

Usage

llama.cpp

llama-server \
  -m qwen36-35b-a3b-fast-mxfp4_moe.gguf \
  --mmproj mmproj-qwen36-35b-a3b-f16.gguf \
  -ngl 99 \
  --host 0.0.0.0 \
  --port 8080

Hardware Requirements

Minimum: 24 GB VRAM for partial offload
Recommended: 32+ GB VRAM for full GPU offload

Quantization

Quantized from Qwen/Qwen3.6-35B-A3B BF16 weights using llama.cpp (llama-quantize.exe --allow-requantize MXFP4_MOE).

License

Apache 2.0 - same as the base model.

Credits

Original model: Qwen/Qwen3.6-35B-A3B
NVFP4 Fast quantization: unsloth/Qwen3.6-35B-A3B-NVFP4-Fast
GGUF conversion: FreedomAISVR

Downloads last month: 2,047

GGUF

Model size

36B params

Architecture

qwen35moe

Hardware compatibility

4-bit

Model tree for FreedomAISVR/Qwen3.6-35B-A3B-MXFP4-MOE-Fast-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Quantized

unsloth/Qwen3.6-35B-A3B-NVFP4-Fast

Quantized

(3)

this model