Instructions to use LibertAIDAI/Qwen3.6-27B-NVFP4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LibertAIDAI/Qwen3.6-27B-NVFP4-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="LibertAIDAI/Qwen3.6-27B-NVFP4-GGUF",
	filename="Qwen3.6-27B-NVFP4-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 LibertAIDAI/Qwen3.6-27B-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 LibertAIDAI/Qwen3.6-27B-NVFP4-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf LibertAIDAI/Qwen3.6-27B-NVFP4-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LibertAIDAI/Qwen3.6-27B-NVFP4-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf LibertAIDAI/Qwen3.6-27B-NVFP4-GGUF:Q4_K_M

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 LibertAIDAI/Qwen3.6-27B-NVFP4-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf LibertAIDAI/Qwen3.6-27B-NVFP4-GGUF:Q4_K_M

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 LibertAIDAI/Qwen3.6-27B-NVFP4-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LibertAIDAI/Qwen3.6-27B-NVFP4-GGUF:Q4_K_M

Use Docker

docker model run hf.co/LibertAIDAI/Qwen3.6-27B-NVFP4-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use LibertAIDAI/Qwen3.6-27B-NVFP4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LibertAIDAI/Qwen3.6-27B-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": "LibertAIDAI/Qwen3.6-27B-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/LibertAIDAI/Qwen3.6-27B-NVFP4-GGUF:Q4_K_M

Ollama
How to use LibertAIDAI/Qwen3.6-27B-NVFP4-GGUF with Ollama:
```
ollama run hf.co/LibertAIDAI/Qwen3.6-27B-NVFP4-GGUF:Q4_K_M
```

Unsloth Studio new

How to use LibertAIDAI/Qwen3.6-27B-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 LibertAIDAI/Qwen3.6-27B-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 LibertAIDAI/Qwen3.6-27B-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 LibertAIDAI/Qwen3.6-27B-NVFP4-GGUF to start chatting

Pi new

How to use LibertAIDAI/Qwen3.6-27B-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 LibertAIDAI/Qwen3.6-27B-NVFP4-GGUF:Q4_K_M

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": "LibertAIDAI/Qwen3.6-27B-NVFP4-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use LibertAIDAI/Qwen3.6-27B-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 LibertAIDAI/Qwen3.6-27B-NVFP4-GGUF:Q4_K_M

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 LibertAIDAI/Qwen3.6-27B-NVFP4-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use LibertAIDAI/Qwen3.6-27B-NVFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/LibertAIDAI/Qwen3.6-27B-NVFP4-GGUF:Q4_K_M
```

Lemonade

How to use LibertAIDAI/Qwen3.6-27B-NVFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LibertAIDAI/Qwen3.6-27B-NVFP4-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-27B-NVFP4-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3.6-27B NVFP4 GGUF

NVFP4 GGUF quantizations of Qwen/Qwen3.6-27B, produced for use with llama.cpp.

About LibertAI

LibertAI is a decentralized AI platform — private inference, an OpenAI-compatible API, and a chat UI, all running on community GPUs over Aleph Cloud instead of a single company's servers. No accounts required to chat, no logs sent home, and the same models you'd self-host are available behind a sovereign endpoint.

If you want to put this model (or any other) to work as an autonomous agent without running your own infrastructure, check out LiberClaw — Hermes-style agents hosted on Aleph Cloud with LibertAI inference. Free tier: 2 agents, no credit card, 5 minutes to deploy. Open source.

The FFN tensors are quantized to NVFP4 (NVIDIA's 4-bit float with E4M3 block scale), repacked from mmangkad/Qwen3.6-27B-NVFP4 (NVIDIA ModelOpt calibration). The remaining tensors (attention projections, SSM linear_attn blocks, embeddings, output) use a conventional GGUF quant — three variants are provided.

Why NVFP4? On NVIDIA Blackwell GPUs (RTX 50-series, B100/B200), llama.cpp uses native NVFP4 tensor-core MMA kernels (added in llama.cpp #22196) for the FFN matmul — the dominant compute cost during inference. On older GPUs the path falls back to dp4a/MMQ kernels, where these GGUFs run but offer no perf advantage over standard K-quants.

Files

File	Size	FFN	Other tensors	When to pick
`Qwen3.6-27B-NVFP4-Q4_K_M.gguf`	15 GB	NVFP4	Q4_K_M	Recommended. Fastest serving throughput on Blackwell + smallest VRAM footprint
`Qwen3.6-27B-NVFP4-Q8_0.gguf`	19 GB	NVFP4	Q8_0	Higher precision attention/embeddings if you have the VRAM
`Qwen3.6-27B-NVFP4-BF16.gguf`	28 GB	NVFP4	BF16	Max quality (preserves source precision for non-FFN tensors); slower in practice — only pick if you need bit-for-bit source fidelity
`mmproj-Qwen3.6-27B-F16.gguf`	889 MB	—	F16 vision tower	Required for image/video input — reusable with any Qwen3.6-27B GGUF, not NVFP4-specific

Performance

Measured on an NVIDIA RTX 5090 (32 GB, Blackwell, sm_120), llama.cpp build c84e6d6db.

Batched serving (`llama-batched-bench`, 512 in / 128 out per request)

NVFP4-Q4_K_M beats stock Q4_K_M on total serving throughput at every parallel batch size we tested (+9 / +0 / +8 / +2% at 1 / 4 / 8 / 16 sequences), with the largest token-generation wins at single stream (+12%) and 8 parallel sequences (+14%). It also uses less VRAM (14.7 vs 16.3 GiB), leaving more room for KV cache.

Variant comparison (same hardware)

Variant	Size	PP512 (tok/s)	TG64 (tok/s)
`NVFP4-Q4_K_M`	14.72 GiB	2865	64
`NVFP4-Q8_0`	18.65 GiB	3346	64
`NVFP4-BF16`	27.19 GiB	1403	49

The Q4_K_M variant is the speed/efficiency winner. The BF16 variant is included for completeness but pays a real bandwidth cost — only pick it if you need maximum precision on the non-FFN tensors and don't care about throughput.

Usage

Text-only (CLI)

llama-cli -m Qwen3.6-27B-NVFP4-Q8_0.gguf -ngl 999 -c 8192 -p "Your prompt here"

Multimodal (server, vision + text)

llama-server \
  -m Qwen3.6-27B-NVFP4-Q8_0.gguf \
  --mmproj mmproj-Qwen3.6-27B-F16.gguf \
  -ngl 999 -c 32768 \
  --host 0.0.0.0 --port 8080

Then POST to /v1/chat/completions with image content blocks — see the llama.cpp multimodal docs.

Recommended sampler

Qwen3.6 is a thinking model. Default chat template enables <think> blocks. For non-thinking usage pass --reasoning off (in llama-cli) or set chat_template_kwargs.enable_thinking=false in the API.

About the architecture

Qwen3.6-27B is a hybrid attention + SSM dense model: every 4th layer is conventional attention; the remaining 48 of 64 layers use Mamba-style linear_attn blocks. The NVFP4 source from mmangkad keeps the SSM in_proj_* projections and standard attention projections at higher precision — only the FFN matmul (192 tensors) is NVFP4. The variants above differ only in how those non-FFN tensors are stored.

Sources & credits

Base model: Qwen/Qwen3.6-27B by Alibaba Qwen team — Apache 2.0
NVFP4 calibration source: mmangkad/Qwen3.6-27B-NVFP4 (NVIDIA ModelOpt v0.42.0)
mmproj source: official BF16 weights from Qwen/Qwen3.6-27B
Tooling: llama.cpp convert_hf_to_gguf.py and llama-quantize

License

Apache 2.0, inherited from the upstream model.

Downloads last month: 6,179

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

8-bit

16-bit

Model tree for LibertAIDAI/Qwen3.6-27B-NVFP4-GGUF

Base model

Qwen/Qwen3.6-27B

Quantized

(372)

this model