Instructions to use FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF",
	filename="mmproj-qwen36-35b-src-BF16.gguf",
)

llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF:BF16
# Run inference directly in the terminal:
llama-cli -hf FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF:BF16
# Run inference directly in the terminal:
llama-cli -hf FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF:BF16

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-NVFP4-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF:BF16

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-NVFP4-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF:BF16

Use Docker

docker model run hf.co/FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF:BF16

LM Studio
Jan

vLLM

How to use FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-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-NVFP4-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-NVFP4-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-NVFP4-GGUF:BF16

Ollama
How to use FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF with Ollama:
```
ollama run hf.co/FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF:BF16
```

Unsloth Studio new

How to use FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-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-NVFP4-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-NVFP4-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-NVFP4-GGUF to start chatting

Pi new

How to use FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF:BF16

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-NVFP4-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF:BF16

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-NVFP4-GGUF:BF16

Run Hermes

hermes

Docker Model Runner
How to use FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF:BF16
```

Lemonade

How to use FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF:BF16

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-NVFP4-GGUF-BF16

List all available models

lemonade list

Qwen3.6-35B-A3B-NVFP4-GGUF

NVFP4 GGUF quantization of Qwen/Qwen3.6-35B-A3B.

Multimodal model: vision encoder (903 MB) + text MoE LLM (18.36 GB).

About NVFP4

NVFP4 is a Blackwell-native FP4 format (EFM4: 1 sign, 1 mantissa, 2 exponent bits). Applied uniformly to all tensors.

Total params: 35.95B (3B active, 256 experts, 8/token)
Quantization: NVFP4 (~4.55 BPW)
File size: 18.36 GiB (text) + 903 MB (vision)
Vision encoder: 27-layer ViT, hidden 1152, 3->1152x2 patch embed (temporal)
Context: 262,144 tokens natively

NVFP4 requires a Blackwell (RTX 50-series or B-series) GPU for hardware acceleration.

Files

Filename	Type	Size	Description
`qwen3.6-35b-a3b-nvfp4.gguf`	NVFP4	18.36 GiB	Text MoE LLM weights
`mmproj-qwen36-35b-src-BF16.gguf`	MMProj	903 MB	Vision encoder weights

Usage

llama.cpp CLI (text only)

llama-cli -hf FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF -cnv -p "You are a helpful assistant"

llama-server (multimodal)

llama-server -hf FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF --mmproj mmproj-qwen36-35b-src-BF16.gguf --ctx-size 0 --jinja

llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF",
    filename="qwen3.6-35b-a3b-nvfp4.gguf",
)

Quantization Pipeline

# 1. Convert HF model to intermediate GGUF
python convert_hf_to_gguf.py ./models/qwen3.6-35b/ --outfile qwen3.6-35b-a3b-f16.gguf --outtype bf16

# 2. Export vision encoder
python convert_hf_to_gguf.py ./models/qwen3.6-35b/ --mmproj --outtype bf16

# 3. Quantize to NVFP4
llama-quantize --allow-requantize qwen3.6-35b-a3b-f16.gguf qwen3.6-35b-a3b-nvfp4.gguf NVFP4

Hardware

GPU	VRAM	Notes
NVIDIA RTX 5060 Ti	16 GB	Quantization performed on this GPU

License

Apache-2.0 (same as Qwen/Qwen3.6-35B-A3B)

Downloads last month: 873

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

4-bit

Model tree for FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

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(405)

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Collection including FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF

QWEN NVFP4 GGUF

Collection

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