Instructions to use FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-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-NVFP4-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-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 serve -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

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

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 serve -hf FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF:BF16

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": "FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Hermes Agent

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 serve -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

Atomic Chat

OpenClaw

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

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-NVFP4-GGUF:BF16" \
  --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-NVFP4-GGUF / README.md

FreedomAISVR

Update README with vision encoder info

95ba642 verified 3 months ago

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	---
	license: apache-2.0
	language:
	- en
	library_name: gguf
	tags:
	- gguf
	- qwen3.6
	- qwen
	- nvfp4
	- blackwell
	- fp4
	- mixture-of-experts
	- moe
	- multimodal
	- vision
	base_model: Qwen/Qwen3.6-35B-A3B
	pipeline_tag: image-text-to-text
	inference: false
	quantized_by: FreedomAISVR
	---

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

	NVFP4 GGUF quantization of [Qwen/Qwen3.6-35B-A3B](https://huggingface.co/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)

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

	### llama-server (multimodal)

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

	### llama-cpp-python

	```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

	```bash
	# 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](https://huggingface.co/Qwen/Qwen3.6-35B-A3B))