Instructions to use unsloth/Qwen3.5-397B-A17B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unsloth/Qwen3.5-397B-A17B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="unsloth/Qwen3.5-397B-A17B-GGUF")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("unsloth/Qwen3.5-397B-A17B-GGUF")
model = AutoModelForMultimodalLM.from_pretrained("unsloth/Qwen3.5-397B-A17B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use unsloth/Qwen3.5-397B-A17B-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 unsloth/Qwen3.5-397B-A17B-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
llama cli -hf unsloth/Qwen3.5-397B-A17B-GGUF:UD-Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf unsloth/Qwen3.5-397B-A17B-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
llama cli -hf unsloth/Qwen3.5-397B-A17B-GGUF:UD-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 unsloth/Qwen3.5-397B-A17B-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf unsloth/Qwen3.5-397B-A17B-GGUF:UD-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 unsloth/Qwen3.5-397B-A17B-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unsloth/Qwen3.5-397B-A17B-GGUF:UD-Q4_K_M

Use Docker

docker model run hf.co/unsloth/Qwen3.5-397B-A17B-GGUF:UD-Q4_K_M

LM Studio
Jan

vLLM

How to use unsloth/Qwen3.5-397B-A17B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unsloth/Qwen3.5-397B-A17B-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": "unsloth/Qwen3.5-397B-A17B-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/unsloth/Qwen3.5-397B-A17B-GGUF:UD-Q4_K_M

SGLang

How to use unsloth/Qwen3.5-397B-A17B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "unsloth/Qwen3.5-397B-A17B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/Qwen3.5-397B-A17B-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 images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "unsloth/Qwen3.5-397B-A17B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/Qwen3.5-397B-A17B-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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use unsloth/Qwen3.5-397B-A17B-GGUF with Ollama:
```
ollama run hf.co/unsloth/Qwen3.5-397B-A17B-GGUF:UD-Q4_K_M
```

Unsloth Studio

How to use unsloth/Qwen3.5-397B-A17B-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 unsloth/Qwen3.5-397B-A17B-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 unsloth/Qwen3.5-397B-A17B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for unsloth/Qwen3.5-397B-A17B-GGUF to start chatting

How to use unsloth/Qwen3.5-397B-A17B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf unsloth/Qwen3.5-397B-A17B-GGUF:UD-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": "unsloth/Qwen3.5-397B-A17B-GGUF:UD-Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use unsloth/Qwen3.5-397B-A17B-GGUF with Docker Model Runner:
```
docker model run hf.co/unsloth/Qwen3.5-397B-A17B-GGUF:UD-Q4_K_M
```

Lemonade

How to use unsloth/Qwen3.5-397B-A17B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unsloth/Qwen3.5-397B-A17B-GGUF:UD-Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-397B-A17B-GGUF-UD-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use unsloth/Qwen3.5-397B-A17B-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 unsloth/Qwen3.5-397B-A17B-GGUF:UD-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 unsloth/Qwen3.5-397B-A17B-GGUF:UD-Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use unsloth/Qwen3.5-397B-A17B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf unsloth/Qwen3.5-397B-A17B-GGUF:UD-Q4_K_M

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 "unsloth/Qwen3.5-397B-A17B-GGUF:UD-Q4_K_M" \
  --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"

Qwen3.5 GGUF Evaluation Results

pinned

by danielhanchen - opened Feb 20

Discussion

danielhanchen

Unsloth AI org Feb 20

•

edited Feb 24

Third party results conducted by Benjamin Marie:

"I tested Unsloth's UD Q4 and Q3 GGUF quantizations of Qwen3.5-397B-A17B and they both performed very well.
In my runs, I didn’t observe a meaningful difference between the original weights and Q3 (less than 1 point of accuracy difference, so only a ~3.5% relative error increase).
You can cut on the order of ~500 GB of memory footprint while seeing little to no practical degradation (at least on the tasks I tried)."

Note the 3-bit is slightly higher accuracy than 4-bit due to a normal margin of error.

danielhanchen pinned discussion Feb 20

ThaiBinh

Feb 20

This comment has been hidden

jdchmiel

Mar 1

Has anyone done similar for the 122b, 35ba35, or 27b yet?

dzupin

Mar 8

I noticed that after the recent update, UD-TQ1_0 has been removed. Are you planning to upload it in the future?
Version UD-TQ1_0 is the only one that can be run on a system with 128 GB of unified RAM. I would really appreciate it if you could put it back (ideally with updated quantization)

tarruda

Mar 8

@dzupin there's a new UD-IQ2_XSS which should fit in 128G. I'm currently downloading it and will run some benchmarks to compare against ubergarm's smol-IQ2_XS: https://huggingface.co/ubergarm/Qwen3.5-397B-A17B-GGUF/discussions/8

tarruda

Mar 8

Apparently the IQ2_M also fits in 128G, so going to test that one too

tarruda

Mar 8

@danielhanchen the chart in this thread is outdated right? I remember looking a while back and the IQ2_M was too big for 128G. Did you guys run another benchmark? I'm interested in the relative error increase for the new IQ2_M and IQ2_XXS

dzupin

Mar 9

My experience was that on my 128GB unified RAM only UD-TQ1_0 was able to run full 256K context without having to do any additional quantization of the context or shrinking of context size window. And I still had some spare RAM for my other apps to run.

tarruda

Mar 9

@dzupin are you on apple silicon? If so you can increase usable VRAM to 125G. I was able to fit ubergarm's smol-IQ2_XS with full 256k context and it worked great.

Note that I don't use this device for anything else, so it sits in pre-login state idling at around 2-3GB RAM.

x-polyglot-x

Mar 14

@dzupin are you on apple silicon? If so you can increase usable VRAM to 125G. I was able to fit ubergarm's smol-IQ2_XS with full 256k context and it worked great.

Note that I don't use this device for anything else, so it sits in pre-login state idling at around 2-3GB RAM.

Thanks for sharing. I've gone up to 120gb vram on my Mac Studio but always get a little nervous as I've locked it up before. 125gb is wild!

I am using the UD-IQ2-M one across two devices via RPC (Studio + 5090 rtx). Performance is very good -- around 170 t/s prompt processing and 20 t/s inference. Quality is excellent so far.

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