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

Libraries

How to use slevinw/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="slevinw/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("slevinw/Qwen3.6-35B-A3B-GGUF", dtype="auto")

llama-cpp-python

How to use slevinw/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="slevinw/Qwen3.6-35B-A3B-GGUF",
	filename="Qwen3.6-35B-A3B-IQ2_S-2.17bpw.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 slevinw/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 slevinw/Qwen3.6-35B-A3B-GGUF:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf slevinw/Qwen3.6-35B-A3B-GGUF:Q4_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf slevinw/Qwen3.6-35B-A3B-GGUF:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf slevinw/Qwen3.6-35B-A3B-GGUF:Q4_K_S

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 slevinw/Qwen3.6-35B-A3B-GGUF:Q4_K_S
# Run inference directly in the terminal:
./llama-cli -hf slevinw/Qwen3.6-35B-A3B-GGUF:Q4_K_S

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 slevinw/Qwen3.6-35B-A3B-GGUF:Q4_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf slevinw/Qwen3.6-35B-A3B-GGUF:Q4_K_S

Use Docker

docker model run hf.co/slevinw/Qwen3.6-35B-A3B-GGUF:Q4_K_S

LM Studio
Jan

vLLM

How to use slevinw/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 "slevinw/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": "slevinw/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/slevinw/Qwen3.6-35B-A3B-GGUF:Q4_K_S

SGLang

How to use slevinw/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 "slevinw/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": "slevinw/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 "slevinw/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": "slevinw/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 slevinw/Qwen3.6-35B-A3B-GGUF with Ollama:
```
ollama run hf.co/slevinw/Qwen3.6-35B-A3B-GGUF:Q4_K_S
```

Unsloth Studio

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

How to use slevinw/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 slevinw/Qwen3.6-35B-A3B-GGUF:Q4_K_S

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": "slevinw/Qwen3.6-35B-A3B-GGUF:Q4_K_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use slevinw/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 slevinw/Qwen3.6-35B-A3B-GGUF:Q4_K_S

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 slevinw/Qwen3.6-35B-A3B-GGUF:Q4_K_S

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use slevinw/Qwen3.6-35B-A3B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf slevinw/Qwen3.6-35B-A3B-GGUF:Q4_K_S

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 "slevinw/Qwen3.6-35B-A3B-GGUF:Q4_K_S" \
  --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"

Docker Model Runner
How to use slevinw/Qwen3.6-35B-A3B-GGUF with Docker Model Runner:
```
docker model run hf.co/slevinw/Qwen3.6-35B-A3B-GGUF:Q4_K_S
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull slevinw/Qwen3.6-35B-A3B-GGUF:Q4_K_S

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-GGUF-Q4_K_S

List all available models

lemonade list

Qwen3.6-35B-A3B-GGUF

File size: 7,161 Bytes

b445774

---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3.6-35B-A3B/blob/main/LICENSE
pipeline_tag: image-text-to-text
base_model:
- Qwen/Qwen3.6-35B-A3B
tags:
- qwen3.6
- byteshape
---

# Qwen3.6-35B-A3B GGUF (ShapeLearn Quantized)

This is a GGUF-quantized version of Qwen3.6-35B-A3B produced with **ByteShape's ShapeLearn**, which learns the optimal datatype per tensor to maintain high quality even at very low bitlengths.

To learn more about ShapeLearn and to see detailed benchmarks across GPUs, CPUs, and even the Raspberry Pi, please visit our [blog](https://byteshape.com/blogs/Qwen3.6-35B-A3B/).

If you have questions or want to share feedback, reach us on [Reddit](https://www.reddit.com/r/ByteShape/).

## Quick Start

Pick a model from the tables below and click **Get llama.cpp command** to get a ready-to-run command with all the correct sampling parameters for this model.

You can also copy the **Model Tag** from the table and use it directly:

| Tool | Command |
|------|---------|
| **llama.cpp** | `llama-server -hf <MODEL_TAG> --mmproj-auto` |

This is a **vision capable** model. llama.cpp auto-downloads the model and vision projector on first run.

Once you run the llama-server, you can access the web interface at `http://localhost:<PORT>`.


> **Note on Ollama:** As of this release, Ollama does not support Qwen3.6-35B-A3B based on Llama.cpp GGUFs. We suggest using [llama.cpp](https://github.com/ggml-org/llama.cpp) or [LM Studio](https://lmstudio.ai/) as an alternative.


## How to Pick a Model

We provide **CPU and GPU optimized variants** for llama.cpp:

- **CPUs:** Models labeled as KQ (Q*_K), optimized for CPU inference with predominantly KQ quantization.
- **GPUs:** Models labeled as IQ, optimized for GPU inference with a hybrid approach combining KQ and IQ quantization for better throughput.

Each hardware target includes a range of models covering different size and quality tradeoffs.

The chart below shows **quality versus tokens per second (TPS)**, with several providers used as the baseline for comparison.
Quality is measured across six benchmarks, including function calling: BFCL-V3, LiveCodeBench V6, HumanEval, GSM8K, IFEVAL, and GSM8K_V in both thinking and instruct modes.

**Selection rule:** Choose the model with the highest quality at your target throughput or the fastest model that still meets your required quality.

### GPU Models
Interactive plots for RTX 4090, 4080, 5060Ti, 5090, and RTX Pro 6000 Blackwell are available [here](https://byteshape.com/blogs/Qwen3.6-35B-A3B/).

![GPU Benchmark - RTX 4090](img/RTX4090.png)

**Table sorted by model size** (match the chart numbers to model IDs):

| Model ID | Bits/Weight | Model Size | Use This Model | Model Tag |
|---------|-------------|-----------|-----|-----------|
| [GPU-1](https://huggingface.co/byteshape/Qwen3.6-35B-A3B-GGUF/blob/main/Qwen3.6-35B-A3B-IQ2_S-2.17bpw.gguf) | 2.17 | 9.42 GB | [Get llama.cpp command](https://byteshape.com/run-hf-model/?tag=byteshape/Qwen3.6-35B-A3B-GGUF:Qwen3.6-35B-A3B-IQ2_S-2.17bpw&platform=llamacpp) | `byteshape/Qwen3.6-35B-A3B-GGUF:Qwen3.6-35B-A3B-IQ2_S-2.17bpw` |
| [GPU-2](https://huggingface.co/byteshape/Qwen3.6-35B-A3B-GGUF/blob/main/Qwen3.6-35B-A3B-IQ3_S-3.00bpw.gguf) | 3.00 | 13 GB | [Get llama.cpp command](https://byteshape.com/run-hf-model/?tag=byteshape/Qwen3.6-35B-A3B-GGUF:Qwen3.6-35B-A3B-IQ3_S-3.00bpw&platform=llamacpp) | `byteshape/Qwen3.6-35B-A3B-GGUF:Qwen3.6-35B-A3B-IQ3_S-3.00bpw` |
| [GPU-3](https://huggingface.co/byteshape/Qwen3.6-35B-A3B-GGUF/blob/main/Qwen3.6-35B-A3B-IQ3_S-3.48bpw.gguf) | 3.48 | 15.1 GB | [Get llama.cpp command](https://byteshape.com/run-hf-model/?tag=byteshape/Qwen3.6-35B-A3B-GGUF:Qwen3.6-35B-A3B-IQ3_S-3.48bpw&platform=llamacpp) | `byteshape/Qwen3.6-35B-A3B-GGUF:Qwen3.6-35B-A3B-IQ3_S-3.48bpw` |
| [GPU-4](https://huggingface.co/byteshape/Qwen3.6-35B-A3B-GGUF/blob/main/Qwen3.6-35B-A3B-IQ4_XS-3.93bpw.gguf) | 3.93 | 17 GB | [Get llama.cpp command](https://byteshape.com/run-hf-model/?tag=byteshape/Qwen3.6-35B-A3B-GGUF:Qwen3.6-35B-A3B-IQ4_XS-3.93bpw&platform=llamacpp) | `byteshape/Qwen3.6-35B-A3B-GGUF:Qwen3.6-35B-A3B-IQ4_XS-3.93bpw` |
| [GPU-5](https://huggingface.co/byteshape/Qwen3.6-35B-A3B-GGUF/blob/main/Qwen3.6-35B-A3B-IQ4_XS-4.15bpw.gguf) | 4.15 | 18 GB | [Get llama.cpp command](https://byteshape.com/run-hf-model/?tag=byteshape/Qwen3.6-35B-A3B-GGUF:Qwen3.6-35B-A3B-IQ4_XS-4.15bpw&platform=llamacpp) | `byteshape/Qwen3.6-35B-A3B-GGUF:Qwen3.6-35B-A3B-IQ4_XS-4.15bpw` |


### CPU Models
Interactive plots for Ryzen 9 5900X, Intel Core i7 12700KF, Ultra 7 265KF, and Raspberry Pi 5 (16 GB) are available [here](https://byteshape.com/blogs/Qwen3.6-35B-A3B/).
![CPU Benchmark - Ryzen 9 5900X](img/Ryzen9.png)

**Table sorted by model size** (match the chart numbers to model IDs):

| Model ID | Bits/Weight | Model Size | Use This Model | Model Tag |
|---------|-------------|-----------|-----|-----------|
| [CPU-1](https://huggingface.co/byteshape/Qwen3.6-35B-A3B-GGUF/blob/main/Qwen3.6-35B-A3B-Q3_K_S-2.69bpw.gguf) | 2.69 | 11.7 GB | [Get llama.cpp command](https://byteshape.com/run-hf-model/?tag=byteshape/Qwen3.6-35B-A3B-GGUF:Qwen3.6-35B-A3B-Q3_K_S-2.69bpw&platform=llamacpp) | `byteshape/Qwen3.6-35B-A3B-GGUF:Qwen3.6-35B-A3B-Q3_K_S-2.69bpw` |
| [CPU-2](https://huggingface.co/byteshape/Qwen3.6-35B-A3B-GGUF/blob/main/Qwen3.6-35B-A3B-Q3_K_S-2.71bpw.gguf) | 2.71 | 11.7 GB | [Get llama.cpp command](https://byteshape.com/run-hf-model/?tag=byteshape/Qwen3.6-35B-A3B-GGUF:Qwen3.6-35B-A3B-Q3_K_S-2.71bpw&platform=llamacpp) | `byteshape/Qwen3.6-35B-A3B-GGUF:Qwen3.6-35B-A3B-Q3_K_S-2.71bpw` |
| [CPU-3](https://huggingface.co/byteshape/Qwen3.6-35B-A3B-GGUF/blob/main/Qwen3.6-35B-A3B-Q3_K_S-3.39bpw.gguf) | 3.39 | 14.7 GB | [Get llama.cpp command](https://byteshape.com/run-hf-model/?tag=byteshape/Qwen3.6-35B-A3B-GGUF:Qwen3.6-35B-A3B-Q3_K_S-3.39bpw&platform=llamacpp) | `byteshape/Qwen3.6-35B-A3B-GGUF:Qwen3.6-35B-A3B-Q3_K_S-3.39bpw` |
| [CPU-4](https://huggingface.co/byteshape/Qwen3.6-35B-A3B-GGUF/blob/main/Qwen3.6-35B-A3B-Q4_K_S-3.80bpw.gguf) | 3.80 | 16.5 GB | [Get llama.cpp command](https://byteshape.com/run-hf-model/?tag=byteshape/Qwen3.6-35B-A3B-GGUF:Qwen3.6-35B-A3B-Q4_K_S-3.80bpw&platform=llamacpp) | `byteshape/Qwen3.6-35B-A3B-GGUF:Qwen3.6-35B-A3B-Q4_K_S-3.80bpw` |
| [CPU-5](https://huggingface.co/byteshape/Qwen3.6-35B-A3B-GGUF/blob/main/Qwen3.6-35B-A3B-Q4_K_S-4.22bpw.gguf) | 4.22 | 18.3 GB | [Get llama.cpp command](https://byteshape.com/run-hf-model/?tag=byteshape/Qwen3.6-35B-A3B-GGUF:Qwen3.6-35B-A3B-Q4_K_S-4.22bpw&platform=llamacpp) | `byteshape/Qwen3.6-35B-A3B-GGUF:Qwen3.6-35B-A3B-Q4_K_S-4.22bpw` |

## Notes on quantization labels

The labels you see (for example `IQ4_XS`) are only there to make Hugging Face show our models in the GGUF table. We do not use the conventional quantization profiles as defined in llama.cpp. In our case, these labels indicate the primary quantization approach and average bit length. Note that both KQ and IQ models may use a mix of quantization techniques optimized for their target hardware, which is why several models can share the same tag.