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

Libraries

How to use unsloth/Qwen3.6-35B-A3B-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="unsloth/Qwen3.6-35B-A3B-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 AutoModel
model = AutoModel.from_pretrained("unsloth/Qwen3.6-35B-A3B-GGUF", dtype="auto")

llama-cpp-python

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="unsloth/Qwen3.6-35B-A3B-GGUF",
	filename="BF16/Qwen3.6-35B-A3B-BF16-00001-of-00002.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 Settings

llama.cpp

How to use unsloth/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
llama cli -hf unsloth/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
llama cli -hf unsloth/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf unsloth/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unsloth/Qwen3.6-35B-A3B-GGUF:UD-Q4_K_M

Use Docker

docker model run hf.co/unsloth/Qwen3.6-35B-A3B-GGUF:UD-Q4_K_M

LM Studio
Jan

vLLM

How to use unsloth/Qwen3.6-35B-A3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unsloth/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-GGUF:UD-Q4_K_M

SGLang

How to use unsloth/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-GGUF with Ollama:
```
ollama run hf.co/unsloth/Qwen3.6-35B-A3B-GGUF:UD-Q4_K_M
```

Unsloth Studio

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

How to use unsloth/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-GGUF:UD-Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use unsloth/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-GGUF:UD-Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use unsloth/Qwen3.6-35B-A3B-GGUF with Docker Model Runner:
```
docker model run hf.co/unsloth/Qwen3.6-35B-A3B-GGUF:UD-Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unsloth/Qwen3.6-35B-A3B-GGUF:UD-Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-GGUF-UD-Q4_K_M

List all available models

lemonade list

Tool calls fail sometimes even in BF16

by lobanov - opened Apr 16

Discussion

lobanov

Apr 16

•

edited Apr 16

Thanks for prompt release! I noticed that tool call parser fails to do the job sometimes as in the screenshot below. It happens on Q6, Q8 and BF16.

Using Metal llama.cpp v2.13.0 via LM Studio.

pt-ml

Apr 16

I also noticed some issues with tool calls, not sure if its on model or llama.cpp

jreoka

Apr 17

I imported it to ollama and it straight up says tool calling isnt available with this model

gdahlm

Apr 17

Hopefully what I found helps out here.

I had to add --swa-full with llama.cpp, while the tag was emitted, in my case it was almost always due. to looping with this model.

With the Zed OpenAi compatible agent being the easiest to observe for my setup. Try looking in the logs for Forcing full prompt re-processing due to lack of cache data (likely due to SWA or hybrid/recurrent memory.

In my experience the Qwen models often require the fancier if larger --swa-full, and sometimes the defaults will dump what look like raw tool calls into the output, but I am pretty sure it is not the root case. This happened on 3.5 and coder-next for me on some of my personal quasi-benchmark coding tasks.

Let me create all test files in parallel:

I apologize for going in circles. The `tests/` directory already exists. Let me stop trying to create it and directly create all the test files now. I have a thorough understanding of the entire codebase from reading all source files.

Let me create all test files in parallel:

I've been in a loop. The `tests/` directory already exists. Let me stop trying to create it and directly create all the test files now. I have a thorough understanding of the entire codebase from reading all source files. Let me create all test files in parallel:

<tool_call>
<function=create_directory>
<parameter=path>
/.../src/junk/zed_demo/tests
</parameter>
</function>
</tool_call>

For me the suggested presence_penalty 2.0 didn't solve this looping, but --swa-full did, along with removing the crash out with tool call texts. It seems to be something with reentrant access but I haven't had the time to look into it but I think it is just how the prompt caching works with the default.

rusoloco73

Apr 18

--swa-full dont impact in memory?

elpirater312

Apr 20

Could it be due to the ssm_conv1d tensor drift bug reported by LuffyTheFox (u/EvilEnginer)?

More info at:
https://www.reddit.com/r/LocalLLaMA/comments/1sp2l72/qwen3635ba3buncensoredwassersteingguf/
https://huggingface.co/LuffyTheFox/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF

I hope this can be fixed on the quants if it is the case.

ulymp

Apr 20

Having the same problem and tried switching to the Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF. This did not help – the problem still occurs for me.

elpirater312

Apr 21

I see, I’m curious if the new Plus version of this model changes anything for you. It was released to address the more widespread corruption in the base weights that the first Wasserstein version didn't cover on the original weighs released by Qwen. If the tool calling is still failing on the Plus version, it might confirm the issue is probably within the base architecture or llama.cpp's handling of it rather than just the tensor drift, but not sure.
https://huggingface.co/LuffyTheFox/Qwen3.6-35B-A3B-Plus-Uncensored-Wasserstein-GGUF

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