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

Libraries

How to use havenoammo/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="havenoammo/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("havenoammo/Qwen3.6-35B-A3B-MTP-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use havenoammo/Qwen3.6-35B-A3B-MTP-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 havenoammo/Qwen3.6-35B-A3B-MTP-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf havenoammo/Qwen3.6-35B-A3B-MTP-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf havenoammo/Qwen3.6-35B-A3B-MTP-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf havenoammo/Qwen3.6-35B-A3B-MTP-GGUF:UD-Q4_K_XL

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 havenoammo/Qwen3.6-35B-A3B-MTP-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf havenoammo/Qwen3.6-35B-A3B-MTP-GGUF:UD-Q4_K_XL

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 havenoammo/Qwen3.6-35B-A3B-MTP-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf havenoammo/Qwen3.6-35B-A3B-MTP-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/havenoammo/Qwen3.6-35B-A3B-MTP-GGUF:UD-Q4_K_XL

LM Studio
Jan

vLLM

How to use havenoammo/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 "havenoammo/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": "havenoammo/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/havenoammo/Qwen3.6-35B-A3B-MTP-GGUF:UD-Q4_K_XL

SGLang

How to use havenoammo/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 "havenoammo/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": "havenoammo/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 "havenoammo/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": "havenoammo/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 havenoammo/Qwen3.6-35B-A3B-MTP-GGUF with Ollama:
```
ollama run hf.co/havenoammo/Qwen3.6-35B-A3B-MTP-GGUF:UD-Q4_K_XL
```

Unsloth Studio

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

How to use havenoammo/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 serve -hf havenoammo/Qwen3.6-35B-A3B-MTP-GGUF:UD-Q4_K_XL

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": "havenoammo/Qwen3.6-35B-A3B-MTP-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf havenoammo/Qwen3.6-35B-A3B-MTP-GGUF:UD-Q4_K_XL

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 "havenoammo/Qwen3.6-35B-A3B-MTP-GGUF:UD-Q4_K_XL" \
  --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 havenoammo/Qwen3.6-35B-A3B-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/havenoammo/Qwen3.6-35B-A3B-MTP-GGUF:UD-Q4_K_XL
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull havenoammo/Qwen3.6-35B-A3B-MTP-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-MTP-GGUF-UD-Q4_K_XL

List all available models

lemonade list

Hermes Agent

How to use havenoammo/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 serve -hf havenoammo/Qwen3.6-35B-A3B-MTP-GGUF:UD-Q4_K_XL

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 havenoammo/Qwen3.6-35B-A3B-MTP-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat

havenoammo commited on May 13

Commit

a529a17

verified ·

1 Parent(s): 44ce525

Upload README.md with huggingface_hub

Browse files

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README.md +39 -2

README.md CHANGED Viewed

@@ -29,7 +29,44 @@ tags:
 >
 > To run these files with MTP you need a custom build of **llama.cpp** that includes the MTP/speculative
 > decoding support from [PR #22673](https://github.com/ggml-org/llama.cpp/pull/22673). Follow the steps below.
->
 > ---
 >
 > ### Building llama.cpp with MTP Support — Step by Step
@@ -57,7 +94,7 @@ tags:
 > #### 4. Checkout master and reset to latest remote
 > ```bash
 > git checkout master
-> git reset --hard 5207d120e
 > ```
 > Ensures a clean starting point at the current upstream master, discarding any local drift.
 >

 >
 > To run these files with MTP you need a custom build of **llama.cpp** that includes the MTP/speculative
 > decoding support from [PR #22673](https://github.com/ggml-org/llama.cpp/pull/22673). Follow the steps below.
+>
+> ---
+>
+> ### 🐳 Or Use Pre-built Docker Images (Recommended)
+>
+> Don't want to build from source? Ready-to-use Docker images with MTP support are available for multiple backends:
+>
+>
+> | Image | Backend |
+> |---|---|
+> | `havenoammo/llama:cuda13-server` | CUDA 13 |
+> | `havenoammo/llama:cuda12-server` | CUDA 12 |
+> | `havenoammo/llama:vulkan-server` | Vulkan |
+> | `havenoammo/llama:intel-server` | Intel (CPU/Xe) |
+> | `havenoammo/llama:rocm-server` | ROCm (AMD) |
+>
+> Quick start — running this model via Docker:
+> ```bash
+> docker run --gpus all --rm \
+>     -p 8080:8080 \
+>     -v ./models:/models \
+>     havenoammo/llama:cuda13-server \
+>     -m /models/Qwen3.6-35B-A3B-MTP-UD-Q8_K_XL.gguf \
+>     --port 8080 --host 0.0.0.0 \
+>     -n -1 --parallel 1 \
+>     --ctx-size 262144 --fit-target 844 \
+>     --mmap -ngl -1 --flash-attn on \
+>     --temp 1.0 --min-p 0.0 --top-p 0.95 --top-k 20 \
+>     --jinja \
+>     --chat-template-kwargs '{"preserve_thinking":true}' \
+>     --ubatch-size 512 --batch-size 2048 \
+>     --cache-type-k q8_0 --cache-type-v q8_0 \
+>     --spec-type mtp \
+>     --spec-draft-n-max 3
+> ```
+> The two flags that matter most for MTP are `--spec-type mtp` and `--spec-draft-n-max 3`.
+> See the [Reddit post](https://www.reddit.com/r/LocalLLaMA/comments/1tc132c/) for more details and discussion.
+>
 > ---
 >
 > ### Building llama.cpp with MTP Support — Step by Step
 > #### 4. Checkout master and reset to latest remote
 > ```bash
 > git checkout master
+> git reset --hard 856c3adac
 > ```
 > Ensures a clean starting point at the current upstream master, discarding any local drift.
 >