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

Libraries

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

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

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

llama-cpp-python

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="byteshape/Qwen3.6-35B-A3B-MTP-GGUF",
	filename="Qwen3.6-35B-A3B-IQ2_S-2.25bpw.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 byteshape/Qwen3.6-35B-A3B-MTP-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf byteshape/Qwen3.6-35B-A3B-MTP-GGUF:IQ2_S
# Run inference directly in the terminal:
llama-cli -hf byteshape/Qwen3.6-35B-A3B-MTP-GGUF:IQ2_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf byteshape/Qwen3.6-35B-A3B-MTP-GGUF:IQ2_S
# Run inference directly in the terminal:
llama-cli -hf byteshape/Qwen3.6-35B-A3B-MTP-GGUF:IQ2_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 byteshape/Qwen3.6-35B-A3B-MTP-GGUF:IQ2_S
# Run inference directly in the terminal:
./llama-cli -hf byteshape/Qwen3.6-35B-A3B-MTP-GGUF:IQ2_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 byteshape/Qwen3.6-35B-A3B-MTP-GGUF:IQ2_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf byteshape/Qwen3.6-35B-A3B-MTP-GGUF:IQ2_S

Use Docker

docker model run hf.co/byteshape/Qwen3.6-35B-A3B-MTP-GGUF:IQ2_S

LM Studio
Jan

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "byteshape/Qwen3.6-35B-A3B-MTP-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": "byteshape/Qwen3.6-35B-A3B-MTP-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/byteshape/Qwen3.6-35B-A3B-MTP-GGUF:IQ2_S

SGLang

How to use byteshape/Qwen3.6-35B-A3B-MTP-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 "byteshape/Qwen3.6-35B-A3B-MTP-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": "byteshape/Qwen3.6-35B-A3B-MTP-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 "byteshape/Qwen3.6-35B-A3B-MTP-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": "byteshape/Qwen3.6-35B-A3B-MTP-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 byteshape/Qwen3.6-35B-A3B-MTP-GGUF with Ollama:
```
ollama run hf.co/byteshape/Qwen3.6-35B-A3B-MTP-GGUF:IQ2_S
```

Unsloth Studio new

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

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for byteshape/Qwen3.6-35B-A3B-MTP-GGUF to start chatting

Pi new

How to use byteshape/Qwen3.6-35B-A3B-MTP-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf byteshape/Qwen3.6-35B-A3B-MTP-GGUF:IQ2_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": "byteshape/Qwen3.6-35B-A3B-MTP-GGUF:IQ2_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use byteshape/Qwen3.6-35B-A3B-MTP-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 byteshape/Qwen3.6-35B-A3B-MTP-GGUF:IQ2_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 byteshape/Qwen3.6-35B-A3B-MTP-GGUF:IQ2_S

Run Hermes

hermes

Docker Model Runner
How to use byteshape/Qwen3.6-35B-A3B-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/byteshape/Qwen3.6-35B-A3B-MTP-GGUF:IQ2_S
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull byteshape/Qwen3.6-35B-A3B-MTP-GGUF:IQ2_S

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-MTP-GGUF-IQ2_S

List all available models

lemonade list

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

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

MTP allows the model to predict multiple tokens per forward pass, accelerating decoding on GPUs by accepting speculative tokens that match what the base model would have produced. These models are optimized for GPU-only inference.

To learn more about ShapeLearn and to see detailed benchmarks across GPUs, please visit our blog.

If you have questions or want to share feedback, reach us on Reddit.

Quick Start

Pick a model from the table 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 --spec-type draft-mtp --spec-draft-n-max 4`

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

The MTP head is bundled inside the GGUF, so no separate draft model is required — just pass --spec-type draft-mtp to enable MTP-based speculative decoding. --spec-draft-n-max controls how many tokens are drafted per step (see A Note on MTP Performance below for tuning guidance).

Build requirement: MTP support requires a recent build of llama.cpp (the draft-mtp value for --spec-type was added in late 2025). If your llama-server --help output does not list draft-mtp under --spec-type, please update to a newer build from the llama.cpp releases.

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

A Note on MTP Performance

MTP throughput is workload dependent. The realized speedup depends on how predictable your prompt and decoded tokens are — code completion, structured output, and repetitive content benefit the most, while highly creative or out-of-distribution generation benefits less.

The TPS numbers reported in our benchmarks are effective TPS, i.e. the throughput observed when running the benchmark suite end-to-end (including acceptance/rejection of speculative tokens). Your own workload may see higher or lower TPS than the chart.

From our measurements, predicting 2 to 4 tokens ahead offers the best quality/throughput tradeoff for most workloads. The optimal n again depends on the workload — feel free to sweep this value for your specific use case.

How to Pick a Model

All MTP variants are GPU-optimized with a hybrid approach combining KQ and IQ quantization for better throughput. Each model covers a different size and quality tradeoff.

The chart below compares this MTP release against our standard NTP (Next Token Prediction) release of the same model to illustrate the speedup MTP provides, plotting quality versus effective tokens per second (TPS). 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.

GPU Models

Interactive plots for RTX 4090, 4080, 5060Ti, 5090, and RTX Pro 6000 Blackwell are available here.

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	2.25	10 GB	Get llama.cpp command	`byteshape/Qwen3.6-35B-A3B-MTP-GGUF:Qwen3.6-35B-A3B-IQ2_S-2.25bpw`
GPU-2	3.06	13.6 GB	Get llama.cpp command	`byteshape/Qwen3.6-35B-A3B-MTP-GGUF:Qwen3.6-35B-A3B-IQ3_S-3.06bpw`
GPU-3	3.53	15.7 GB	Get llama.cpp command	`byteshape/Qwen3.6-35B-A3B-MTP-GGUF:Qwen3.6-35B-A3B-IQ4_XS-3.53bpw`
GPU-4	3.97	17.6 GB	Get llama.cpp command	`byteshape/Qwen3.6-35B-A3B-MTP-GGUF:Qwen3.6-35B-A3B-IQ4_XS-3.97bpw`
GPU-5	4.19	18.6 GB	Get llama.cpp command	`byteshape/Qwen3.6-35B-A3B-MTP-GGUF:Qwen3.6-35B-A3B-IQ4_XS-4.19bpw`

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.

Downloads last month: 20,123

GGUF

Model size

36B params

Architecture

qwen35moe

Hardware compatibility

2-bit

3-bit

4-bit

Model tree for byteshape/Qwen3.6-35B-A3B-MTP-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Quantized

(389)

this model